Geo-information Science and Remote Sensing

Thesis Report GIRS-2019-14

Assessing impacts of forest landscape restoration:

An evaluation of three projects in Latin America and the

Caribbean using the Collect Earth land monitoring tool.

Marcus Betts

May 2

01

9

i

ii

Assessing impacts of forest landscape restoration:

An evaluation of three projects in Latin America and the

Caribbean using the Collect Earth land monitoring tool.

Marcus Betts

Registration number 960206-061-100

Supervisors:

Erika Romijn

Martin Herold

A thesis submitted in partial fulfilment of the degree of Master of Science

at Wageningen University and Research Centre,

The Netherlands.

May 2019

Wageningen, The Netherlands

Thesis code number: GRS-80436 Thesis Report: GIRS-2019-14 Wageningen University and Research Centre Laboratory of Geo-Information Science and Remote Sensing

iii

iv

Abstract

Forests landscapes are of huge importance in terms of the resources they provide, the

biodiversity they support and the role they play in regulating climate. Today, however, they are

under threat from deforestation, resulting in loss of biodiversity, increased CO2 emissions and

habitat fragmentation, particularly across Latin America with its expanse of tropical rainforest.

To address deforestation, international commitments such as the Bonn Challenge have been

established, committing millions of hectares of deforested land for restoration. Forest

landscape restoration (FLR) projects have been implemented across Latin America and the

Caribbean in order to meet these targets. The objective of this research is to analyse the impact

of 3 FLR initiatives in Argentina, Haiti and Peru using the augmented visual interpretation tool

Collect Earth. For each project, land use change and change in canopy cover percentage were

recorded in order to determine whether restoration or degradation had occurred. In terms of

restored land area, the Argentina project had 2697.5 hectares, the Haiti project 594.1 hectares

and the Peru project 39.3 hectares. Also, 3168.5 hectares of land was degraded in the Haiti

project area and 6.4 hectares in the Peru area. Comparison with project goals, shows evidence

that the Argentina project is achieving its primary goal of establishing forest through plantation.

However, evidence for the Haiti and Peru projects meeting their goals is less clear, due to the

different FLR approaches they take. It was concluded that Collect Earth is a suitable tool for

monitoring more obvious FLR impacts such as forest regrowth due to reforestation. More

subtle changes related to different FLR approaches such as agroforestry or sustainable forest

management are less easily detected.

v

Contents

1 Context and Background ................................................................................................................ 1

2 Problem Definition .......................................................................................................................... 5

3 Research Objective and Research Questions .............................................................................. 7

4 Materials and Methods .................................................................................................................... 7

4.1 Data ............................................................................................................................................. 7

4.2 Methodological Approach ............................................................................................................ 9

4.3 Selection of Project Areas ......................................................................................................... 10

4.4 Project Areas ............................................................................................................................. 10

4.5 Delineation of Project Areas ...................................................................................................... 13

4.6 Characterisation of Project Areas .............................................................................................. 14

4.7 Sampling Strategy ..................................................................................................................... 14

4.8 Collect Earth Survey Design ...................................................................................................... 16

4.9 Collect Earth Augmented Visual Interpretation ......................................................................... 17

5 Results ............................................................................................................................................ 21

5.1 Stratification of Haiti Project Area .............................................................................................. 21

5.2 Project Characterisation ............................................................................................................ 22

5.3 Land Use ................................................................................................................................... 30

5.4 Land Use Conversion ................................................................................................................ 31

5.5 Canopy Cover Percentage ........................................................................................................ 33

5.6 Restoration or Degradation ....................................................................................................... 33

6 Discussion ..................................................................................................................................... 36

6.1 Delineation of Project Areas ...................................................................................................... 36

6.2 Characterisation of Project Areas .............................................................................................. 37

6.3 Changes in Forest Ecosystems and Land Use ......................................................................... 39

6.4 Critical reflection on Collect Earth ............................................................................................. 41

7 Conclusion ..................................................................................................................................... 44

References ........................................................................................................................................... 46

Appendix .............................................................................................................................................. 53

1

1 Context and background

1.1 Forest ecosystems

Forests cover almost a third of the world’s total land area, an area estimated to be just below

4 billion hectares (FAO, 2016a). This means forests are some of the most extensive

ecosystems on earth and as result they are of huge importance not only in terms of the

resources they provide but also the biodiversity they support and the role they play in regulating

climate. Forests provide ecosystem services that support the livelihoods of people, by acting

as a source of fuel and other products that can be used to generate income. In addition, they

also help to improve food security by providing sources of nutrition (Adams et al., 2016;

Rasmussen et al., 2017). In total, it is thought that 1.6 billion people, including many

indigenous, depend on forest resources for some part of their livelihoods whilst many countries

are also heavily reliant on forest ecosystems for their economic development (Munang et al.,

2011; Kohl et al., 2015). As human populations continue to increase, the per capita forest area

is in decline, which means more pressure will be put upon these important ecosystems as

demand for resources will increase (FAO, 2016a).

Forests are extremely diverse ecosystems, with tropical forests in particular being the most

biodiverse terrestrial ecosystems on earth, hosting at least two-thirds of the earth’s terrestrial

species (Gardner et al., 2009). In addition forests have a huge influence on the climate through

exchanges of energy, water and carbon dioxide with the atmosphere (Bonan, 2008). They are

vital carbon sinks that sequester large amounts of carbon annually, storing approximately 45%

of the world’s terrestrial carbon (Kohl et al., 2015; Romijn et al., 2015). This is fundamental for

reducing the rate of global warming. The vegetation in forest ecosystems also helps to slow

runoff by intercepting rainfall and preventing soil erosion by holding soil together. This helps to

prevent natural hazards such as flooding and landslides.

1.2 Deforestation and Degradation

Today, however, forest ecosystems are disappearing rapidly, mainly due to the severe ongoing

threat they face from deforestation and land degradation. According to the Food and

Agriculture Organisation (FAO, 2010) deforestation can be defined as the conversion of forest

to another land use or the long-term reduction of canopy cover to below 10 percent cover. The

way that forest degradation has been defined varies throughout literature, but the theme that

is common in all definitions is that degradation is a reduction in a forest’s capacity to provide

resources and services (ITTO, 2002; FAO, 2011). This can be caused by a combination of

2

factors, namely an absence of forest cover, loss of soil fertility or loss of natural function that

prevents the forest from recovering unaided (CBD, 2001).

Deforested and degraded landscapes encompass a significant proportion of the world’s

forested areas with a resulting loss of biodiversity, impacts on global greenhouse gas

emissions and habitat fragmentation (Stanturf et al., 2015; Mansourian et al., 2017). It is now

thought that a billion people live in areas prone to land degradation, with their livelihoods being

impacted (Sabogal et al., 2015). Furthermore, the degradation of such land has been

recognised as a contributing factor to numerous disasters such as flooding, landslides, famines

and human migrations (Runyan & D’Odorico 2016). Land use change, particularly the

conversion of forest to other land uses during deforestation, plays a significant role in changing

the world’s climate and is the second largest source of anthropogenic CO2 emissions

(Hosonuma et al., 2012). The vast majority of this land use change occurs in the tropics,

particularly in Latin America which has the highest emissions from land use change since the

1980s (IPCC, 2013). There are different drivers that can be the reason for the extensive land

use changes across the tropics. For example, in Latina America, the biggest driver of forest

loss is expansion of agricultural land, particularly pasture expansion (De Sy et al., 2015). In

order to meet demand for food production, further intensification of agriculture is likely to occur

and this in turn can lead to more deforestation (Boucher et al., 2011; Rudel et al., 2009). Also,

as biofuels become an increasingly favourable fuel, there is expected to be greater pressure

on forests to make space for their production (Lapola et al., 2010). Moreover, further

exploitation of mineral resources is expected to occur as demand increases, contributing to

forest degradation (Rademaekers, 2010).

1.3 International Restoration Commitments

According to Lewis et al. (2015) less than half of the world’s tropical forests remain standing

due in the most part to deforestation. Protection of the remaining areas will simply not be

enough to safeguard biodiversity and provide resources for the people relying on them

(Chazdon et al., 2009). As a result many policy frameworks and international agreements have

been established to address the impacts of land degradation and deforestation. For example,

Goal 15 of the United Nations Sustainable Development Goals addresses land degradation

with the aim of sustainably managing forests, combatting desertification, reversing land

degradation and halting biodiversity loss (United Nations, 2018). In addition, Target 15 of the

Aichi Biodiversity targets, set as part of the International Convention on Biological Diversity

calls for the restoration of at least 15 percent of degraded ecosystems by 2020 (CBD, 2018).

Also the REDD+ scheme developed as part of the United Nations Framework Convention on

Climate Change addresses the issue of forest degradation (UNFCCC, 2010; Alexander et al.,

3

2011). It aims to contribute to climate change reduction by focussing efforts on reducing

emissions from forest degradation and deforestation (REDD+, 2016). A range of approaches

are used to achieve the objectives of such policy frameworks. For example, in recent years,

restoration has gained more salience as political leaders begin to recognise the need to fulfil

international agreements such as those mentioned previously (Chazdon et al., 2017).

However, often these agreements are not always transparent with what exactly is meant by

restoration and what approaches will be taken to achieve them (Stanturf et al., 2014).

1.4 Forest Landscape Restoration

One such approach, referred to as Forest Landscape Restoration (FLR) is a strategy that has

been used frequently. Initially, it was a concept that needed much refinement and redefinition

to accommodate new perspectives on what makes it different from more conventional

approaches to restoration (Maginnis and Jackson, 2005). But, generally, FLR consists of the

restoration of degraded landscapes, with a particular focus on, but not limited to deforested

landscapes. It combines three key elements: firstly, forests by increasing the number of trees

in an area, landscapes by involving whole countries, watersheds or natural areas that

transcend different land uses and restoration by improving the productivity and ecological

functions of an area (IUCN AND WRI, 2014). FLR considers entire landscapes for restoration

as opposed to small sites. It can therefore incorporate a range of interdependent land uses

across a landscape such as protected forest areas, ecological corridors, agroforestry systems,

agriculture, plantations or riparian strips (IUCN AND WRI, 2014). This emerged from the

recognition that restoration needed to focus on the wider scale to incorporate other land uses

in addition to just forested land (Maginnis and Jackson, 2005). As a result, two main

approaches to FLR have developed: wide-scale and mosaic. Wide-scale aims to restore areas

that were formerly dominated by forest, whilst mosaic restoration takes an approach of

integrating trees into mixed-use landscapes that weren’t previously totally dominated by forest

(IUCN AND WRI, 2014).

Therefore, FLR can be seen as an integrating concept applied across a range of land uses to

ensure key ecosystem functions services are strengthened, whilst functionality and productivity

is restored to degraded and deforested land (Chazdon et al., 2017). For example, the addition

of trees to agricultural landscapes can boost food production, whilst restored forest landscapes

can reduce soil erosion and mitigate climate change through sequestering carbon (Chazdon

et al., 2017). This is implemented whilst trying to enhance both the biodiversity and the human

well-being in an area (Newton et al., 2012). Long-term FLR is needed on a greater scale to

reverse the historical trends of degradation in the tropics (Chazdon & Uriarte et al., 2016).

According to the Global Partnership on Forest Landscape Restoration (2011) more than two

4

billion hectares of land globally offer opportunities for some type of restoration intervention.

Whilst it is thought that more than 1 billion hectares of this land is degraded tropical forest that

has the potential to be restored (Laestadius et al. 2011).

1.5 Bonn Challenge

Today many organisations across the planet are keen to engage in FLR (Chazdon et al., 2017).

Restoring productivity to degraded land has become a global priority with several united

nations conventions putting forward goals specifically addressing the issue of restoration

(CBD, 2011; UN, 2012; UNCCD, 2013).The largest initiative focussing on restoration was

launched in 2011 in Bonn, Germany now known as the Bonn Challenge. It is a global initiative

that aimed to bring together the wide range of restoration targets under one initiative (Verdone

and Seidl, 2017). It has the ambitious target of calling for 150 million hectares of deforested or

degraded land to be restored by 2020 (Verdone and Seidl, 2017). So far the Bonn Challenge

has managed to secure commitments to restore more than 148 million hectares of degraded

land. This includes 18 commitments that have been procured in Latin America to restore 35.64

million hectares of land, from Argentina, Brazil, Colombia, Costa Rica, Peru and many more

(Bonn Challenge, 2017). Evidence that the world’s largest restoration initiative has garnered

global approval was also reflected in the fact that from a poll of over one million global citizens

ahead of the 2012 Rio+20 conference, it was voted the most pressing forest issue upon which

governments should act, and the second most important sustainable development issue

overall after sustainable energy (IUCN, 2014). This was followed by the New York Declaration

on Forests – a declaration agreed by a number of heads of governments and influential

organisations pledging to restore 350 million hectares of forest by 2030 (UN, 2014; Stanturf et

al., 2015; Suding et al., 2015). This was another international agreement that affirmed FLR’S

growing importance as an environmental policy (Suding et al., 2015).

1.6 Latin America and Caribbean

In 2008, over 20 percent of forest and agricultural land in Latin America and the Caribbean

(LAC) consisted of degraded land and this has increased since (Bai et al. 2008). In addition, a

total of 3.4 million hectares of tree cover was lost in the region in 2013 (Hansen et al. 2013).

The situation isn’t set to improve immediately either, with projections indicating that between

2000 and 2050, LAC countries could lose an additional 7 percent of their total forest cover

(Chiabai et al. 2011). As is the case with most forested regions globally, agriculture and forestry

are exerting a huge pressure here as they continue to grow (WRI, 2010). Therefore, forest and

landscape restoration has the potential to offer a solution to these increasing pressures. If

implemented well, it presents an opportunity to reduce agricultural expansion, mitigate land

5

degradation and deforestation, whilst maintaining the provision of ecosystem services and

biodiversity that are widespread throughout the region (WRI, 2016). As a result many projects

that implement FLR have been initiated across LAC supported by initiatives such as Initiative

20x20, the Clean Development Mechanism (CDM), Forest Investment Programme (FIP), the

Global Environment Facility (GEF) and many other local initiatives. The Initiative 20x20 aims

restore 20 million hectares of degraded land across LAC by 2020 (Mendez-Toribio et al., 2017).

CDM projects aim to reduce emissions and sequester carbon through the establishment of

plantations. GEF and FIP projects have taken more of a focus on supporting LAC countries to

reduce deforestation and degradation through sustainable management practices.

2 Problem Definition

2.1 Assessing Impacts of FLR

Whilst attempts to restore degraded land are often perceived as positive, in reality, there have

also been restoration projects that have caused more damage than they have solved

(McElwee, 2009; Mansourian et al,. 2017). They do not always address the underlying drivers

of deforestation and land degradation and this means they do not always have they positive

impact that was intended. Although many millions of hectares are being pledged for restoration

by governments, there is still not enough transparency or guidance for what type of land should

be committed (Mansourisan and Kleine, 2013). This can result in unintended outcomes, for

example, land that was historically non-forested has been erroneously committed for

restoration in a number of cases (Veldman et al. 2015).

As FLR becomes more of a priority, the lessons extracted from previous FLR projects are

becoming increasingly important. There is agreement within literature that there is a need for

evaluation of restoration actions (Bautista and Alloza, 2009; Vallauri et al. 2005). The lack of

evaluation and subsequent dissemination of the outcomes of FLR projects can limit the

application of the best approaches in future projects (Bautista and Alloza, 2009). Assessing

the impact of FLR projects is therefore essential in order to improve future initiatives

(Stephenson et al., 2015). Furthermore, if FLR is to be adopted more widely as best practice

to tackle deforestation and landscape degradation, then its effectiveness must first be

demonstrated (Newton et al., 2012). Despite the many commitments of land to restoration,

governments are still often hesitant to support targets like the Bonn Challenge and see

restoration in general as a poor investment, perceiving that the costs outweigh the benefits

(De Groot et al., 2013). Therefore being able to measure the impact of FLR is essential in order

6

to influence future actions and show that the ongoing actions are beneficial (Stephenson et al.

2015).

The evaluation of initially proposed project objectives needs to be included as an integral part

of any FLR project. However, frequently, FLR objectives are not set out transparently, therefore

this can impede the evaluation of such projects (Bautista and Alloza, 2009). Also if objectives

lack detail, then the outcomes of project evaluation may yield information of limited value (Hutto

& Belote 2013). The challenge for new restoration projects is scaling up to a larger area – this

requires clarity of goals and effective monitoring protocols (Murcia et al., 2015). Therefore it is

important for each project to have clearly stated objectives prior to implementation, that should

be fulfilled on completion; only then can success really be measured (Clewell et al., 2004).

Monitoring of the project outcomes can prove to be challenging and is often lacking from many

FLR initiatives (Rey-Benayas et al., 2009). Despite the significant benefits that evaluation

brings, the number of evaluated restoration projects remains low and many are carried out

without considering their efficacy (Bautista and Alloza, 2009). A study of 119 restoration

projects in Colombia showed that only 5% of projects took the long-term goals into

consideration during evaluation, and only short-term impacts were monitored (Murcia et al.,

2015).

2.2 Use of Remote Sensing

While there are increasingly more projects implementing FLR across Latin America, with many

nations committing to agreements such as the Bonn Challenge, there is still no fixed agreement

on a standard methodology to evaluate their outcomes (Stanturf et al., 2015). This can be due

to the fact that FLR projects can take very different approaches, therefore it is challenging to

find one method of evaluation to fit all approaches. Also the drivers of deforestation and forest

degradation significantly influence the methods used to monitor their impacts. The

effectiveness of different monitoring approaches vary per project and need to be selected

carefully. In some cases earth observation has been the primary method used to monitor forest

change (Achard et al., 2010). Methods have been used to measure regrowth of vegetation

after reforestation using time series analysis of Landsat images (DeVries et al., 2015; Muller

et al., 2016). Hansen et al. (2013) also quantified global forest gain, by assessing the

establishment of tree canopy from a non-forest state using Landsat imagery. Other methods

include LiDAR data that has been used to derive tree canopy cover and tree height to estimate

forest regrowth (Coughlin et al., 2016). Remote sensing is an effective approach for monitoring

land use change and for quantifying anthropogenic impacts to the landscape. In particular, it

has shown efficiency for observing forest ecosystems on a large scale and has underexploited

potential in terms of utilising it to assess FLR initiatives (Stephenson et al., 2015).

7

Despite these developments, monitoring of FLR schemes in Latin America through earth

observation techniques still needs further development with many developing countries lacking

the capacity to implement the necessary technologies (Hansen et al., 2010; Romijn et al.,

2015). In the past, land monitoring through remote sensing has been a challenge due to high

costs and the level of technical skills required to conduct a thorough assessment (Gibbs et al.,

2007; Bey et al., 2016). However, new software has been developed to make land assessment

more accessible to non-experts and in turn, making assessment of FLR projects through

remote sensing more feasible. Most recently, the Collect Earth software was developed,

enabling the robust land assessment of any area on earth (Bey et al., 2016). It is based on

open source software and allows a sampling based approach that has the potential to be used

to assess the impacts of FLR projects without excessive costs (Silva Carbon, 2017). However

it still remains to be seen how useful such a tool can be when assessing the impacts of FLR

projects.

3 Research objective and research questions

The overarching objective of this research is to analyse the impact of FLR initiatives on the

forest landscape in terms of regrowth by performing a comparative analysis of three selected

FLR projects across Latin America and the Caribbean. This research will also aim to determine

whether the augmented visual interpretation tool Collect Earth is suitable to assess FLR

initiatives. This will be addressed by answering the following research questions:

1. How can the three selected FLR projects be characterised using multiple environmental

GIS datasets?

2. What changes in forest ecosystems and land use can be detected within the project

areas using the augmented visual interpretation tool Collect Earth?

3. How do the three different FLR projects in Latin America compare in terms of their

impact on the forest ecosystem?

4 Materials and Methods

4.1 Data

4.1.1 Restoration Database

A series of datasets have been compiled concerning planned, ongoing and completed FLR

projects across Latin America and the Caribbean by Wageningen University and Research in

8

collaboration with the International Centre for Tropical Agriculture (CIAT) (Romijn and Coppus,

2018). This comprises of two databases: the first being a compilation of detailed project

descriptions, regarding the implemented restoration activities and the intended project goals

and outcomes. The second database is an analysis of the projects themselves, in terms of

their goals, planning, execution, monitoring and results. A spatial dataset has also been

created that represents the spatial extent of each the projects (Romijn and Coppus, 2018).

This dataset will be used to delineate the areas of the selected projects.

4.1.2 Characterisation Datasets

In order to characterise each of the selected projects, 5 datasets were selected. From these

datasets the project areas can be put in context in terms of the environment they are situated

within. The following table presents such datasets (figure 1).

Figure 1: Description of datasets used for project characterisation

Dataset Description

Net emissions from agriculture, forests and other land uses (AFOLU) (2000-2005).

Annual mean emissions for 2000-2005 (kg ha-1 yr-1) which combines CO2, CH4 and N2O from deforestation, fire, wood harvesting, enteric fermentation, manure management, paddy rice and cropland soils. The pixel size is 0.5 x 0.5 degrees (Roman-Cuesta et al., 2016).

Potential Biomass Accumulation (BAP) (Mg/ha)

Shows the amount of biomass that has the potential to accumulate if restoration occurs (Mg/ha). Dataset created from Global Ecological Zones data (FAO, 2010) and a Geocarbon Global Forest biomass map, based on: Avitabile et al. (2016), Avatibile et al. (2014) and Santoro et al. (2015). The pixel size is 300m x 300m (Romijn et al., under review).

Global Ecological Zones (2010)

Dataset showing Forest Ecological Zones (broad forest types) produced by the Food and Agriculture Association as a second edition to the 2000 Global Ecological Zones dataset (FAO, 2012).

Atlas of Forest Landscape Restoration Opportunity (Forest Status and Restoration Potential)

The Forest Status layer classifies forest landscapes into 4 categories (Intact, Fragmented, Degraded or Deforested). Spatial resolution is 1km. The Restoration layer categorises the areas that have the potential to be restored (Wide-scale restoration, Mosaic Restoration, Remote restoration). Spatial resolution is 1km (Potapov et al., 2011).

Global forest cover gain (2000-2012)

The dataset represents the forest gain for the period 2000 – 2012. It has a spatial resolution of 1 arc-second per pixel. (Hansen et al., 2013).

9

4.2 Methodological Approach

The methodological approach for this project is presented in the following flowchart (figure 2).

Figure 2: Flowchart showing the steps taken throughout this research, and the results needed for each research question.

10

4.3 Selection of Project Areas

From the database of FLR projects, 3 projects were selected in order to make a comparison

of different FLR initiatives. It would be beyond the scope of this research to assess all 154

projects due to the amount of time that would be required. Therefore in order to select 3 suitable

projects for comparison several criteria were taken into account.

First of all, the projects had to be either complete or running for at least 5 years so that enough

time has elapsed to allow any possible impacts to be detected. Secondly, it was decided that

the selected projects should each be implemented as part of different initiatives, to see how

they differ in terms of their impacts. It was also important that the selected projects should have

a clearly defined extent so that only the areas where restoration activities have taken place are

used for further analysis. This avoids carrying out analysis outside of the project extent or in

parts of the project area where restoration activities weren’t implemented. Therefore it was

important that the project extent was reported clearly in the form of co-ordinates or maps that

explicitly show where activities took place. The size of the project area was also taken into

account during project selection. Projects areas larger than 10,000 hectares were not selected

because they would require more points to sample over the whole area than is feasible in the

timeframe of this research. After taking all these factors into consideration 3 projects were

selected for the analysis.

4.4 Project Areas

In this section a brief description of each of the selected study areas is given. The maps of

each project area produced from the dataset of general project areas (Romijn and Coppus,

2018).

4.4.1 Project 1 - Peru - International Centre for Tropical Agriculture (CIAT)

This project is entitled: ‘Sustainable development options and land-use based alternatives to:

enhance climate change mitigation and adaptation capacities in the Colombian and Peruvian

Amazon, while enhancing ecosystem services and local livelihoods’. It focuses on

implementing FLR in the tropical and subtropical broadleaf forests surrounding Yurimaguas in

the Loreto region of the Peruvian Amazon (figure 3). It was set up by CIAT and ran for a 5 year

period from 2014 to 2018. Previously, the area was degraded by extensive burning,

overgrazing, illegal logging and unsustainable agricultural practices. The primary objective of

the project was to assist national environmental authorities and local farmers in managing

deforested areas, by enhancing their adaptive and mitigation capacities (CGIAR, 2015). The

11

approach involved promotion of agroforestry and silvo-pastoral productivity, recovery of

biodiversity and ecological processes and capture and storage of carbon.

Figure 3: Map of the Alto Amazonas and Datem del Maranon provinces where the CIAT project was implemented.

4.4.2 Project 2 – Haiti – Global Environment Facility (GEF)

The second project is entitled: ‘SFM Sustainable Land Management of the Upper Watersheds

of South Western Haiti’. It was a project funded by GEF running for 68 months between 2009

and 2015. The project area is focused on the rainforest in the Macaya National Bioreserve,

located in the South West of Haiti (figure 4). Land degradation has occurred here due to fuel

wood demand and unsustainable agriculture. This continuous degradation of land and loss of

forest undermines efforts to reduce poverty. Therefore the main objective of the project is to

address and contain degradation through the integration of sustainable land and forest

management practices (Lejonc and Palazy, 2018). Additionally, the project seeks to support

forest restoration and implementation of a carbon stock and sequestration monitoring.

12

Figure 4: Map showing the area of Haiti where the GEF project was implemented.

4.4.3 Project 3 – Argentina – Clean Development Mechanism (CDM)

The third and final project is entitled: ‘Reforestation of grazing Lands in Santo Domingo,

Argentina’. This projects implements FLR through reforestation by planting forestry plantations

in northern Argentina with native species to increase carbon sequestration. The project area

itself is located in the department of Ituzaingó, Province of Corrientes, Northern Argentina

(figure 5). This project was initiated by CDM and began in 2007; it is expected to run until 2027.

The main objectives of the project are to sequester CO2 through forest planting in grassland

areas, resulting in environmental benefits such as soil protection, water runoff regulation and

increased biodiversity (Clean Development Mechanism, 2013).

13

Figure 5: Map of Ituzaingo Department where the CDM project was implemented.

4.5 Delineation of project areas

In the case of the CIAT project in Peru, a shapefile of the project area was available in addition

to the original FLR project database. This shows detailed information of the exact plots of land

where FLR was implemented. This means that no further stratification is required to define the

project extent and this shape file can be used for sampling. In the case of the CDM project in

Argentina, there are detailed maps within the project report that show the extent of the project

area (CDM, 2013). Geo-referencing was performed in Google Earth by overlaying the map

image from the project report on satellite imagery. FLR was implemented throughout the whole

of this area, therefore sampling can be carried out across the whole of this area without further

stratification. In the case of the GEF project in Haiti the project area is not clearly defined. The

project reports give details in the form of a map of the Macaya National Bioreserve where the

project was implemented (Lejonc and Palazy, 2018). However it is not clear in which parts of

the reserve restoration activities were focused. Geo-referencing of the map from the project

report was carried out within Google Earth. The output represents the extent of the national

park where the project took place but still lacks more specific details of where activities took

14

place. Therefore, in addition it was proposed to use the forest regrowth dataset (Hansen et al.,

2013) to give an indication of where changes to the forest have been detected within the

reserve. A stratification of the project area was made with areas that show forest regrowth to

be used as the area in which to take sample points.

4.6 Characterisation of Project Areas

The final delineated project areas were characterised with 5 datasets. This was done by

overlaying them onto 5 different datasets: Global Ecological Zones, Forest Status, Restoration

Potential, Potential Biomass Accumulation and AFOLU annual emissions (previously

described in Section 4.1.2).

4.7 Sampling Strategy

In order to be able to carry out analysis within Collect Earth a sampling grid must first be

generated for each of the project areas. Random sampling was selected as the best approach

in order to have representative samples of the whole area. The project areas for Haiti and

Argentina were one single polygon which meant that a simple random sampling approach

could be taken. Random sampling was also selected as it is relatively simple to implement and

produces an unbiased sample (Olofsson et al., 2014). It was decided that 50 sampling points

should be taken across each of these areas as this was deemed feasible in the timeframe for

the project (figures 6 and 7). However the Peru project area is comprised of 18 different farm

plots that are in turn made up of multiple polygons. This makes random sampling across the

whole project area a less suitable approach, as in theory, whole farm plots could be left

unsampled. Therefore a stratified random sampling approach was taken with each farm plot

set as an individual stratum, giving 18 strata in total. Then 5 sample points were taken within

each of these strata amounting to a total of 90 sample points (figure 8). The sampling density

for each project was significantly different: for the Peru project area there was one sample

point for each area 1.773 ha, for Argentina one for each 67.437 ha and for Haiti one for each

198.032 ha. This was approach taken as it is not feasible during the timeframe of this project

to take all the sampling points required for the Haiti and Argentina projects to have the same

sampling density as the Peru project.

Using the shape files of each project area as the spatial extent input, sampling grids were

generated in QGIS. This results in a csv file that is compatible with Collect Earth with 6 attribute

columns in the following order: plot ID number, latitude, longitude, elevation, slope and aspect.

15

Figure 6: Sampling grid generated for the Argentina project area.

Figure 7: Sampling grid generated for the Haiti project area.

16

Figure 8: Map showing part of the Peru sampling grid. The green polygons make up one strata and there are 5 sample points within it. There are 17 further strata, each with 5 sample points that make up the Peru sample grid. Due to the distances between strata it was not possible to show the whole sampling grid clearly on one map.

4.8 Collect Earth Survey Design

In addition to the sampling grids, the Collect Earth software also requires a survey in the form

of a Collect Earth Project (CEP) file. The survey was designed within Collect – another tool

from the OpenForis toolset (OpenForis, 2019). When a plot is selected within Collect Earth the

survey will appear and all the information can be recorded on a plot by plot basis. Several

pieces of information were decided upon to be recorded within the survey, these are

summarised in the table below (figure 9).

Figure 9: Information recorded within Collect Earth survey

Page 1 - Before FLR Implementation Page 2 - After FLR Implementation

Land use category Land use category

Land use category confidence Land use category confidence

Estimated Canopy Cover % Land use conversion

Date of imagery Land use conversion confidence

Source of imagery Estimated Canopy Cover %

- Restoration / Degradation trend

- Date of imagery

- Source of imagery

17

4.9 Collect Earth Augmented Visual Interpretation

Once the Collect Earth survey and sampling grids were created, visual interpretation was

carried out within Collect Earth (figure 10). When the sampling grids were loaded into Collect

Earth each sample point in the grid was represented by a plot. The size of plot that each point

in the sampling grid represents was set to 0.49 ha. It was set to this size to correspond with

the FAO definition of a forest that must be at least 0.5 ha in size (FAO, 2010). The smaller plot

sizes that can be selected in Collect Earth would therefore hinder the classification of forest.

Within each plot are 25 sampling points; the number of sampling points that are covered by

each land use or contain tree cover was counted to give an estimation of the land use and

percentage canopy cover.

Figure 10: Collect Earth user interface. The left-hand column shows the plot ID numbers along with a red exclamation mark (a plot that hasn’t been surveyed yet), a green tick (a plot that has been surveyed) or a yellow exclamation mark (a plot that has an incomplete survey). The sample plot is the yellow box with 25 sampling points within it. The popup to the right of the sample plot is the project survey which appear when the sample plot is clicked.

4.9.1 Source Imagery

Several sources of imagery are available for interpretation within Collect Earth, but it was

decided that Google Earth imagery would be used due to its high resolution. In all cases the

imagery used was provided by Digital Globe and on average has a resolution of 0.5m which

18

allowed for better interpretation than imagery with a lower resolution (DigitalGlobe, 2019). For

each project area it was also important that the date of the imagery used for interpretation was

consistent with the initiation and the completion of each project. In some circumstances,

different date images were used as images from the same date were not available for the

whole project area, or they were inhibited by cloud cover.

The Argentina project began in 2007 and is expected to finish in 2027, so the imagery used

for initial interpretation was 10/12/2006. As the project is still ongoing the most recent imagery

was used to interpret after project implementation, this was 09/05/2013 in most cases, but

04/05/2015 was used for other plots. For the Peru project area (2014-2018), the initial imagery

used was from 21/07/2012, and in some cases 01/01/2010 depending on availability. Imagery

from the 29/07/2018 was used for interpretation after project completion. The Haiti project

(2009-2015) had less consistency in the imagery date used for interpretation due to the fact it

had more intermittent coverage and cloud cover prevented the use of a number of images.

Therefore, the imagery used for before project implementation was 29/07/2007, however for

after project completion, several dates were used depending on availability: 14/06/2015,

7/10/2016, 9/10/2016, 10/10/2016, 11/10/2016 and 12/10/2016.

4.9.2 Land use classification

In order to classify land use across the project areas, six general land use classes were

chosen: Forest, Cropland, Grassland, Wetland, Settlements and Other land. These land use

categories were based on those defined by the International Panel for Climate Change (IPCC,

2003):

(i) Forest land – This includes all land with tree cover or woody vegetation cover. For

this assessment the FAO definition of a forest has been adapted to set a threshold for

detecting forest land. Land spanning more than 0.5 hectares with a canopy cover of

more than 10 percent is considered to be forest (FAO, 2010).

(ii) Cropland - This includes arable land and agro-forestry systems where vegetation

cover is lower than the threshold for forest of 10 percent forest cover.

(iii) Grassland - This category includes any pasture land that is not considered as

cropland. It also includes land without vegetation, wild grassland as well as

recreational areas and silvo-pastoral systems.

(iv) Wetlands - This includes land that is covered by water for all or part of the year. It

includes reservoirs, rivers, lakes and peatland.

(v) Settlements - All developed land, including all human settlements and transportation

infrastructure.

19

(vi) Other land - This category includes all land that cannot be placed in any of the other

categories including bare soil, rock, ice.

Land use was determined by assessing each plot in relation to theses class descriptions. The

number of sampling points are counted within each plot to give the predominant land use type.

Figure 11 shows 3 sample plots and how they have been categorised with respect to the 6

classes. To aid land use classification in plots where a predominant land use was difficult to

determine due to mixed land uses, a hierarchical rule was applied which puts the 6 land use

categories in a predetermined order of priority: settlement, cropland, forest land, grassland,

wetland, other land. If the settlement land use covers more than the 20% area threshold of the

plot, then the plot will be classified as settlement, if it covers less then cropland will be assessed

in the same way, and so on until a land use is assigned to the plot. This hierarchical rule

methodology, first theorised by Martinez and Mollicone (2012) has been used to classify land

use, including within Collect Earth when interpreting satellite imagery (FAO, 2016; Asrat et al.,

2018)

Figure 11: Example plots classified as grassland, forest and cropland according to the IPCC land use classes.

4.9.3 Canopy Cover Percentage Change

In order to estimate the percentage of and detect changes in canopy cover within each sample

plot, 6 percentage classes were selected: No cover, 0-20% cover, 20-40% cover, 40-60%

cover, 60-80% cover and 80-100% cover. These ranges are purposefully broad as detecting

canopy cover to a greater level of detail from visual interpretation is unreliable. The canopy

cover percentage was calculated from the ratio of plot points under canopy cover to the total

number of plot sampling points (Bey et al., 2015). Interpreting canopy cover with such a

technique is far quicker, and less costly when the capacity to use other methods are limited

(Asrat et al., 2018). Figure 12 shows 6 examples of how the percentage canopy cover was

interpreted within each plot .

20

Figure 12: Example plots for each of the 6 canopy cover percentage classes and how they were interpreted within Collect Earth.

4.9.4 Restoration or Degradation

It was also recorded whether the detected changes indicated restoration or degradation. This

was determined based on the land use change that occurred. A land use change matrix of the

6 land use classes was used to determine which types of land use change could be considered

restorative or degrading (figure 13). The land use change matrix shows the 30 possible

changes that can occur and if they are identified as either restoration or degradation (Sims et

al., 2017). Additionally, if no land use change occurred, but the canopy cover percentage

increased then this was also considered restoration and if it decreased then it was considered

degradation.

21

Figure 13: Land Use change matrix. Cells coloured in red indicate a change that is considered degradation. Cells coloured in green indicate a change that is considered restoration. Grey cells represent no change in land use.

Final Land Use Class

Original

Land Use

Class

IPCC Class

Forest Land

Grassland Cropland Wetland Settlement Other Land

Forest Land

Grassland

Cropland

Wetland

Settlement

Other land

5 Results

5.1 Stratification of Haiti Project Area

Stratification of the Haiti project area using the Hansen global forest gain dataset was

unsuccessful. Overlaying the initial project area with the forest gain dataset revealed that only

a few gain pixels fall within the extent of the project (figure 14). The area that these pixels cover

is a negligible proportion of the whole project area. Also the pixels are generally dispersed

across the whole project individually and are less than 0.5 ha in size. If these pixels were to be

taken as the Haiti project area, many of the polygons it would produce would be too small to

sample as they are smaller than the 0.5 ha plot that are used for sampling in Collect Earth.

Therefore the original georeferenced outline of the Haiti Project area was used.

22

Figure 14: Map showing forest gain pixels within the Haiti project area

5.2 Project Characterisation

5.2.1 Global Ecological Zones

Both the Haiti and Peru projects fall within the tropical rainforest zone according to the 2010

Global Ecological Zones (FAO, 2012) (figure 15). The Argentina project also partially falls

within this ecological zone, however the majority of it is characterised by tropical moist forest.

These zones have broadly homogenous characteristics in terms of their vegetation cover,

climate and physiography as described by Simons (2001). The tropical rainforest zone is

considered wet with average annual rainfall ranging from 1500 to 3000 mm and even higher

rainfall occurs in the Amazon basin where the Peru project is located. Hurricanes are also

common across the Caribbean islands that fall into the zone, including Haiti. Temperatures

average between 20°C and 26°C throughout the year. Vegetation is generally tall and dense

evergreen forest with tree canopies reaching in excess of 40m. This includes well-developed

rainforests characterised by high biomass levels such as that of the Amazon Basin. The

tropical moist forest zone is still characterised by high rainfall but with a more pronounced dry

season; this is characteristic of the Argentina project area. A mosaic of grasslands, tree

23

savannahs and woodlands, with patches of semi-deciduous forest is the general vegetation

pattern throughout this zone.

Figure 15: Overview of the 3 selected projects in relation to Global Ecological Zones defined according to FAO (2012).

5.2.2 Forest Status

Overlaying the project areas with a forest status map gives an indication of the state of the

forest in that area. According to this dataset the Argentina project area mostly consists of

fragmented or managed forest, with some deforested areas in the centre and south of the area

(figure 16). The Haiti project area consists of mostly fragmented or managed forest throughout

(figure 17). The fringes of the project area show partially deforested land and in some parts in

the south, deforested land is present. The Peru project area shows great variation across the

different strata (figure 18). The majority are characterised by partially deforested land, but

many of the strata also consist of fragmented or managed forest.

24

Figure 16: Argentina project area overlaid with Forest Status dataset. This gives an indication of the state of the forest in terms of whether it is deforested, degraded, intact or fragmented.

Figure 17: Haiti project area overlaid with Forest Status dataset. This gives an indication of the state of the forest in terms of whether it is deforested, degraded, intact or fragmented.

25

Figure 18: Peru project area overlaid with Forest Status dataset. This gives an indication of the state of the forest in terms of whether it is deforested, degraded, intact or fragmented.

5.2.3 Restoration Potential

The restoration opportunities map gives an approximation of the extent of opportunities for

FLR globally, to improve livelihoods, restore forests and protect the environment. The

Argentina project area is not represented as an opportunity for restoration, only parts in the

centre and south of the project area show opportunities for mosaic restoration (figure 19). The

Haiti project similarly shows a lack of restoration opportunities across the area according to

this dataset (figure 20). The areas adjacent to the project area boundaries provide

opportunities for mosaic restoration in particular. The Peru project area shows different

opportunities for restoration across the different plots (figure 21). Mosaic restoration is the most

common type of opportunity, but wide-scale restoration also has potential here. As with the

other two projects, some parts also show a lack of restoration opportunities.

26

Figure 19: Restoration opportunities within the Argentina project area. This gives an indication of whether restoration opportunities are present in this area and if so what type of opportunities.

Figure 20: Restoration opportunities within the Haiti project area. This gives an indication of whether restoration opportunities are present in this area and if so what type of opportunities.

27

Figure 21: Restoration opportunities within the Peru project area. This gives an indication of whether restoration opportunities are present in this area and if so what type of opportunities.

5.2.4 Potential Biomass Accumulation

When the project areas are overlaid with the BAP dataset, insight can be gained into the

potential each restoration project has for accumulating biomass when they have been restored.

Results for the Argentina project show that most of the area has very little potential for biomass

accumulation (figure 22). However, the central and southern parts show a BAP value of 49

Mg/ha and one pixel value is as high as 97 Mg/ha. The Haiti project area shows great potential

for biomass accumulation towards the outer limits of the area with values ranging from 101-

140 Mg/ha (figure 23). The central and northern region show very little forest biomass

accumulation potential, with values less than 2 Mg/ha. The Peru project area shows great

variation between the different strata that make up the project area (figure 24). The majority

do appear to show a high potential for biomass accumulation with values ranging from 100-

175 Mg/ha, however at least 5 of the strata that make up the area show very little potential

biomass accumulation.

28

Figure 22: Argentina project area overlaid with Potential Biomass Accumulation.

Figure 23: Haiti project area overlaid with Potential Biomass Accumulation.

29

Figure 24: Peru project area overlaid with Potential Biomass Accumulation.

5.2.5 Mean annual AFOLU emissions

All three of the projects show low to medium levels of emissions from agriculture, forestry and

other land uses. The Haiti project area has mean annual emissions of 864 kg ha-1 yr-1, the

values for Peru range between 359 - 1415 kg ha-1 yr-1 and the Argentina has emissions of 1189

kg ha-1 yr-1. This means they are not considered emissions hotspots that are represented by

red pixels in figure 25.

30

Figure 25: Annual mean emissions of CO2, CH4 and N2O from agriculture, forests and other land uses for the years 2000-2005 (kg ha-1 yr-1). Emissions hotspots are indicated by red pixels. The location of the 3 projects are represented.

5.3 Land use

The land use patterns of each of the 3 project areas were very different (figure 26). The

Argentina project area was made up of almost 90% grassland before project implementation

with a few patches of forest dispersed across the area. After project implementation, the land

use pattern was the opposite with forest making up 90% of the project area and grassland

reduced to 10% of the area. This shows that almost 80% of the area showed forest growth

during the 7 years between the 2 images. The Argentina project area consisted of only two

land use types whereas the samples taken from the other two project areas showed greater

variety in land uses. The Peru project area had 51 of 90 sample plots classified as forest before

project implementation, afterwards this increased to 55 of the 90 plots. 40% of land was

classified as grassland before implementation and decreased to less than 20% after project

implementation. Before project implementation just one plot was classified as cropland, this

increased to 18 afterwards. The Haiti project was the only one of the 3 projects to have less

land classified as forest after implementation than before. It decreased from 30 plots to 22

31

classified as forest. Approximately one quarter of the area was classified as grassland before

project implementation and this remained the case after implementation. The significant

increase was in the other land class, doubling from 7 to 14 plots classified as other land after

FLR implementation.

Figure 26: The proportion of land uses within each of the 3 project areas before and after each project was implemented.

5.4 Land use conversion

In terms of the actual land use conversions that took place (figures 27, 28 and 29), in the

Argentina project area all initial forest land remained after implementation. The additional 39

plots classified as forest after implementation were all converted from grassland. In the Haiti

project area 8 of the original 30 plots classified as forest were converted to other land. The

original grassland and other land generally remained as it was. Overall in the Haiti project area,

the greatest land use conversion was from forest land to other land. In the Peru project area,

all but 2 of the 52 plots that were originally classified as forest remained as forest. The

grassland class showed the greatest changes with 18 of the 35 plots classified as grassland

converting to cropland, 2 converting to forest land and the rest remaining as grassland. The

only plot initially classified as cropland converted to forest. Therefore the most noticeable land

use conversion across the Peru project area was grassland to cropland.

32

Figure 27: Land use conversion in Peru project area

Figure 28: Land use conversion in Haiti project area

Figure 29: Land use conversion in Argentina project area.

33

5.5 Canopy Cover percentage

Before FLR was implemented, there was no canopy cover in 43 of the 50 sample plots of the

Argentina project (figure 30). This decreased to just 5 of the plots after FLR implementation.

In addition almost half of the plots had between 80-100% canopy cover and in total 41 of the

50 plots had at least 40% canopy cover. This shows that the implementation of FLR increased

the canopy cover across the Argentina project. The Haiti project area showed less obvious

changes in canopy cover. Overall there appeared to be a reduction in canopy cover across the

area, with the number of plots with at least 40% canopy cover decreasing from 30 to 21 after

implementation. The Peru project area showed very few changes in terms of canopy cover,

with a slight reduction in the number of plots with no cover, from 11 to 8. Aside from that, the

canopy cover percentage did not change much over the whole Peru area.

Figure 10: The proportion of each project area that is covered by each canopy cover percentage class before and after each project was implemented.

5.6 Restoration or degradation

For the Argentina project area 40 of the 50 sample plots showed a restoration trend, 10 of the

sample plots showed no degradation nor restoration and none of the plots showed degradation

(figure 31). For the Haiti project area 3 of the 50 sample plots showed a restoration trend, 16

of the sample plots showed a degradation trend and 31 of the plots showed no change (figure

32). The classification of whether each of these plots shows restoration or degradation is based

on the land use conversion (section 5.4) or change in canopy cover recorded (section 5.5).

34

Figure 31: Map showing the sample plots of the Argentina project area in terms of whether they indicated degradation, restoration or no change.

Figure 32: Map showing the sample plots of the Haiti project area in terms of whether they indicated degradation, restoration or no change.

35

For the Peru project area 5 of its 18 strata showed a restoration trend, 13 of the strata showed

no degradation nor restoration and none of the strata showed degradation (Figure 33).

However the result for each strata is made of an average of 5 sample plots within each. On

the plot level, 3 plots showed degradation, however within their strata, there were more sample

plots that showed no changes or restoration. The majority of the 18 strata contained plots that

showed restoration and plots that showed no change, only one strata showed restoration in all

5 of the sampling plots within it.

Figure 33: Map showing the strata of the Peru project area in terms of whether they indicated degradation, restoration or no change. Each strata contains 5 sample points as shown in the enlarged image of plot Y48 and they are used to determine for each strata whether restoration or degradation is present. See the Appendix for the changes at plot level for all 18 strata.

When inferred to the total area of the entire project area, the Argentina project area showed

the greatest proportion of restored land, with 80% classed as showing restoration and the

remaining 20% showing no changes (Figure 34). When taking each sample plot, one third of

the Peru project area showed restoration and almost two thirds showed no changes, the small

remainder showed degradation. Only 6% of the Haiti plot was considered to show restoration,

more than 30% showed degradation and the remaining 62% showed no change. In terms of

36

area, the Argentina project has 2697.5 ha of land that showed restoration, the Haiti project

area had 594.1 ha that showed restoration and 3168.5 ha of degradation and for the Peru

project 39.3 ha of land showed restoration and 6.4 ha of land showed degradation.

Figure 34:Graph showing the proportion of restoration and degradation within each of the 3 project areas.

6 Discussion

The following chapter will discuss the previously presented results in relation to the 3 research

questions. It will also provide a critical assessment of the methodology used to arrive at these

results, particularly focusing on the use of Collect Earth as a visual interpretation tool.

Furthermore, it will give insight into the limitations of this research and highlight what is required

in terms of further research.

6.1 Delineation of project areas

Delineation of project areas was important for this research in order to know where FLR

activities were being focussed within a project area. Defining the extent of the Peru project was

done using available spatial data that gave exact details of the project area to the plot level.

This mean that assessment of the project was far more reliable as the exact area was already

37

predefined. There was more uncertainty around the Argentina project area as it had to be

determined from maps in the project report. The process of geo-referencing that took place

using the report map could have introduced errors in defining the area by involuntarily emitting

parts of the project area or including parts where FLR didn’t take place as a result of human

error.

The Haiti project area also involved geo-referencing from a project report map of the Macaya

National Bioreserve. In addition, the Hansen global forest gain dataset was used to stratify the

area further in an attempt to include only the areas where FLR activities were focussed, in this

case pixels that represented forest gain. This approach failed to give a resulting area that could

be used to sample within. There are a number of reasons why this approach was not suitable

to stratify the Haiti project area. The first is that the Hansen dataset covers forest cover gain

between 2000 and 2012, whereas the Haiti project ran from 2009 to 2015. Therefore, only

changes that occurred within the first 3 years of the project may have been visible in this

dataset, and any forest gain that occurred before 2009, not as a result of FLR activities would

also be represented. In addition, the main goal of the Haiti project was not to increase forest

cover, it was instead to demarcate, protect and sustainably manage existing forest resources.

The other limitation of this approach is that it assumes that the project has been successful in

increasing forest cover. Therefore any land that didn’t represent gain according to the Hansen

dataset was assumed not to be where FLR activities took place, whereas in reality it could be

that the FLR approach had failed to work on that land.

6.2 Characterisation of project areas

6.2.1 Global Ecological Zones

Positioning each project area within its ecological zone gives insight into the climatic and

vegetation characteristics for each project. This is important when trying to explain observed

changes that have been recorded within Collect Earth and the importance of FLR for each

area. For example, the Haiti project is located in a region where hurricanes are a common

natural phenomenon and these can cause extensive damage to forest landscapes. The

strength of wind can destroy vegetation and cause reductions in forest cover (Boose et al.,

1994). The degradation that was observed within the Haiti project area could be attributed to

hurricane damage that has occurred during the project and not directly related to poor

implementation of FLR. Also the FLR approach that is undertaken differs for each of the

projects and this is because each of the projects is set within a different biophysical

environment. The restoration approach taken at different locations within a landscape depends

on the biophysical environment (Chazdon et al., 2015).

38

6.2.2 Forest Status

The project areas were characterised based on the status of the forest within them to indicate

where there is potential for forest to grow. This dataset was useful in giving an overall indication

of where deforested and degraded forest was situated and to see if this was consistent with

the project reports. However to see greater detail at the project level, the resolution was far too

coarse and made it difficult to extract meaningful information.

6.2.3 Restoration Potential

Characterisation of the project areas based on the restoration opportunities gave interesting

results. Overlaying the project areas with this dataset showed that much of the project areas

weren’t considered restoration opportunity areas despite having restoration activities carried

out there. The restoration opportunities dataset did highlight the majority were mosaic

restoration. This was consistent with the FLR approaches for the Peru and Haiti project areas

where the aim was not simply to restore dense forests to the landscape, but to integrate trees

into mixed-use landscapes. The Argentina project, although small in scale, took more of a

similar approach to wide-scale restoration by aiming to reforest the entire project area. The

fact that this dataset has a coarse resolution, meant it wasn’t the most suitable for assessing

restoration potential at the scale of the project areas. It is more useful to give an overall

indication of where the different restoration opportunities are on the continent scale.

6.2.4 Potential Biomass Accumulation

The potential biomass accumulation across each project area was mapped to indicate whether

there was considered to be a high potential for biomass accumulation when each area is

restored. In general the Haiti and Argentina areas showed a low potential for biomass

accumulation. This is surprising, as restoration of degraded or deforested land, particularly

actively planting trees, as was the goal in Argentina, substantially accelerates above-ground

biomass accumulation (Holl and Zahawi, 2014). This BAP dataset was created using the forest

status dataset as an input for forest coverage, so again the resolution was coarse, making it

difficult to see detail at the project level.

6.2.5 Mean annual AFOLU emissions

The locations of the 3 project areas were projected on a map of mean annual AFOLU

emissions. The AFOLU sector which includes emissions from deforestation is responsible for

a high proportion of greenhouse gas emissions: 40% of total greenhouse gas emissions in

39

Latin America in 2008 (Calvin et al., 2016). Emissions from land-use change like forest

degradation and deforestation can be reduced (Smith et al., 2014). FLR is an important method

for doing this by protecting existing forest landscapes and promoting regeneration of degraded

landscapes. The three project areas all show medium to low AFOLU emissions, which means

they are not considered emissions hotspots. Although the implementation of these 3 FLR

projects will potentially assist with reduction of emissions, their potential for doing so is not as

high as it would be if they were located in emissions hotspots.

6.3 Changes in forest ecosystems and land use

6.3.1 Land use changes - Argentina project area

The Argentina project area showed the greatest change in land use during the timespan of the

project, increasing from 12% forest to 90% forest, with forest land replacing the grassland that

was widespread throughout the project area before. The detection of grassland as the initial

land use is consistent with project reports that describe the initial land use as extensively

managed grassland for cattle grazing (CDM, 2013). Furthermore the shift towards forest land

is in accordance with the main project goal of increasing vegetation cover by forest planting in

grassland areas to increase CO2 sequestration. This land use change provides evidence that

the aim of establishing a forest plantation by 2027 is already beginning to show signs of being

achieved even after just 7 years of a project scheduled to last 20 years in total.

6.3.2 Land use changes - Haiti project area

Land use change was detected in fewer plots across the Haiti project area. The most

noticeable change was that the amount of forest land decreased, and 8 plots changed from

forest to bare land that was classified as other land. This shows evidence of deforestation

occurring within the project area whilst there were only 2 plots that changed to forest from

another land use. The main objective of the this project was the demarcation, protection and

surveillance of existing forest land to contain the rapid degradation that was occurring

throughout the Macaya National Park. However from the land use changes observed, this goal

doesn’t appear to have been met, with deforestation still occurring. Whether these changes

occurred as a result of human activity that hasn’t been prevented effectively due to a poor

project strategy or as a result of damage from natural disasters like hurricanes is difficult to

determine from visual interpretation.

40

6.3.3 Land use changes - Peru project area

Almost all land initially classified as forest land in the Peru project area remained the same

after project implementation. The goal of protecting natural forests in the area showed more

evidence of being achieved, particularly when compared to the Haiti project area that had a

similar goal. Instead the greatest land use change was from grassland to cropland. Initially

grassland was extensively grazed by cattle, but a shift towards cropland could be evidence of

agroforestry practices being introduced. The main restoration approach of this project was to

introduce agroforestry and silvo-pasture practices to assist local farmers in enhancing their

adaptive capacities to climate change. Therefore this land use conversion shows evidence that

this FLR approach is being put into practice. One of the difficulties in the Peru project area was

classifying the land use of plots as many covered multiple land uses. This could be due to the

fact that agroforestry was promoted here which is characterised by tree cover on agricultural

land which makes it a challenge to classify (Zomer et al., 2009). Other studies have highlighted

challenges with identifying agroforestry systems from Collect Earth, with Daniel et al. (2018)

stating that silvo-pastoral systems were the most challenging to detect.

6.3.4 Leakage

In addition to land use changes observed within the project areas themselves it was also

possible to observe changes occurring just outside project boundaries. Evidence of

degradation and forest land being converted to plantations, was observed on land adjacent to

the Peru project area. This highlights the issue of leakage that commonly occurs in areas

immediately next to restoration projects when the activities that cause degradation are not

permanently avoided, but displaced to another area nearby (Auckland et al., 2003; Atmadja

and Verchot, 2011). Whether these changes can be directly attributed to the FLR interventions

is difficult to assess. However it highlights the fact that future monitoring of FLR projects should

also consider extending beyond the project boundaries, to detect more subtle impacts.

6.3.5 Canopy Cover percentage

The Haiti and Peru project areas show minimal changes in their canopy cover percentage.

This is consistent with their project goals which didn’t directly aim to increase forest cover

across their project areas. Therefore, it is difficult to draw conclusions about the impact of these

two projects based on the canopy cover percentage if this was not addressed in their goals.

The Argentina project, however, showed a significant increase in canopy cover since project

implementation: almost half of the project area had more than 80% canopy cover and only

10% had no cover at all. This was compared to more than 85% having no cover before the

41

project started. This provides further evidence that the Argentina project is heading towards

achieving its goal of establishing a forest plantation by 2027. However, this monitoring needs

to continue beyond the project completion date to fully assess if this has been achieved.

6.3.6 Restoration or degradation

The Argentina project area showed the greatest amount of restored land according to the visual

interpretation of land use changes and canopy cover change. The Peru project showed only a

third of the area was restored, whilst the Haiti area showed less than 10% restoration, with

almost a third of its area showing degradation. However there is still uncertainty over the exact

size of these areas due to multiple factors. The area of land restored is inferred from the area

covered by the sample plots which only represent a small proportion of the overall project area.

The Haiti area particularly, was sampled sparsely, so upscaling the information from the 50

sample plots introduces great uncertainty. More sampling points would be required, particularly

for the Haiti and Argentina areas to reduce this uncertainty. Another factor that can influence

the results is the duration of a project. If it took place over a longer period of time, then it would

be expected that the impacts of FLR would be more visible.

Furthermore, what is considered restoration or degradation here is only based on the land use

change and change in canopy cover within each plot. This is an over-simplification when in

reality the 3 projects also implement different aspects of FLR in order to address a range of

goals that cannot be assessed within Collect Earth. It is important to recognise that FLR does

not simply focus on maximizing new forest cover, but instead restoring forest functionality

(Maginnis et al., 2012). This is illustrated by the Peru project area where FLR approaches

varied even within the same strata; in some cases reforestation was taking place directly next

to silvo-pastoral plots. The different FLR approaches can be identified with varying degrees of

difficulty using Collect Earth, so the classification of whether it is a plot is considered to show

restoration or not can be influenced by the type of restoration approach. Therefore, it is only

possible to determine whether the projects have achieved the goals that can be interpreted

from satellite imagery. The use of Collect Earth for monitoring the impacts of FLR must be part

of a greater monitoring plan otherwise goals that involve improving livelihoods for local people,

for example, cannot be assessed.

6.4 Critical reflection on Collect Earth

As a new and innovative augmented interpretation tool Collect Earth has contributed

significantly to the field of remote sensing by providing improved access to freely available

satellite imagery. It is currently the only tool that can simultaneously access archives of imagery

42

from Google Earth, Google Earth Engine and Bing Maps (Bey et al., 2016). Since its release,

Collect Earth has gained popularity due to is simplicity compared to other remote sensing

applications and the fact that information can be generated, analysed and shared amongst

users rapidly (Daniel et al., 2018). Another distinct advantage of Collect Earth is its

compatibility with other tools for geospatial and statistical analysis such as SAIKU analytics.

This makes integration of results from Collect Earth far more intuitive for the user.

6.4.1 Applicability for monitoring FLR projects

A key aspect of Collect Earth is its flexibility to design an assessment that can meet the

requirements of a range of different applications, through different sampling designs, adaptable

surveys and a vast archive of imagery. As a result Collect Earth can support a number of

different applications, such as: forest inventories, monitoring land use change, validation of

land cover maps, quantifying deforestation, restoration and degradation (Mendoza et al.,

2017). In terms of assessing restoration, Collect Earth has been used to assist with large-scale

restoration monitoring efforts (Buckingham et al., 2017). Although it is an extremely useful

monitoring tool, it is critical to realise that it is only part of the monitoring that needs to take

place for restoration projects worldwide. Collect earth focuses primarily on indicators, such as

land use change and forest change, excluding other important goals for which restoration

projects are undertaken. It cannot assess social impacts such as generating local employment

opportunities, improving livelihoods which were key in the Haiti project. It is also a challenge

to assess to environmental impacts such as soil protection, water runoff regulation and

increased biodiversity within Collect Earth that were important for the Argentina project.

6.4.2 Economic Feasibility

The huge advantage with using Collect Earth in this research is that it provides free access to

an extensive archive of available imagery. The application is open-source and free to use as

a desktop application. This makes it an extremely cost effective tool for monitoring land (Daniel

et al., 2018; Mendoza et al., 2017). Many projects have shown that Collect Earth provides an

economically feasible methodology for assessing forest and land use changes throughout the

entirety of the world (FAO, 2016b). It also reduces expenses by reducing the need for complex

pre-processing of imagery and facilitates a methodology that can easily be applied by non-

remote sensing experts (Bey et al., 2016). The Haiti project exemplifies where Collect Earth

can assist with providing more cost-effective monitoring, when previously project funds were

not adequately given to monitoring of the project. If Collect Earth remains to be free and open-

source it will be a feasible monitoring option for future FLR projects as a smaller proportion of

project funds will have to be set aside for it.

43

6.4.3 Use by non-experts

The fact that Collect Earth can be utilised cost-effectively by non-experts helps to increase the

capacity of countries for monitoring restoration projects more feasibly, when previously the

existing technologies were too expensive or required expert remote sensing knowledge.

However, during the visual interpretation of images, uncertainties did arise when trying

determine land use for example. Although Collect Earth can be used by non-experts, it is also

important to consider the integration of local expertise to improve the reliability of the image

interpretation process (Daniel et al., 2018). Buckingham et al. (2017) argued that local

knowledge is an essential part of the Collect Earth process and assessment in Collect Earth

benefits from being conducted with local stakeholders who can provide knowledge at the

ground level of areas being monitored in cases of uncertainty. Alternatively interpretation of

the same imagery can be undertaken by more than one person to increase quality assurance

through multiple interpretations (Shepaschenko et al., 2017).

6.4.4 Lack of high-resolution imagery

One of the primary challenges faced when using Collect Earth was the lack of widespread

coverage across each project area of high-resolution imagery, particularly for the same date.

The Haiti project area in particular lacked complete coverage by high-resolution imagery for a

specific date. It has been highlighted in literature that there is a lack of data available in

historical archives for monitoring change, most notably across the tropics and the Amazon

region where the projects from this research are located (Lesiv, et al., 2018). These spatial

and temporal gaps in high-resolution imagery are inevitable and when they do occur, Collect

Earth offers integration of information from coarser resolution imagery such as Sentinel 2,

Landsat and MODIS imagery (Bey et al., 2016). However, when image interpretation was

prevented by cloud cover, or high-resolution images were unavailable for the same date, high-

resolution images from the closest available date were used, despite the fact that lower

resolution images were available for the same date. This is because image interpretation is

made far more challenging by lower resolution images as more subtle changes are difficult to

identify. Shepaschenko et al. (2017) questioned the validity of visual interpretation from low

resolution imagery, stating is virtually impossible in the majority of cases when high-resolution

imagery is not used. Differentiation between vegetation types and different land use classes is

almost impossible. It is important that there is greater availability of high-resolution imagery

and gaps in existing images are filled to make change detection and land use monitoring

through Collect Earth more reliable in the future (Lesiv, et al., 2018).

44

6.4.5 Effective survey design

The construction of the surveys used to record data within Collect Earth requires careful

consideration in order to support efficient data collection. It was important to ensure that all the

required information was recorded without the inclusion of unnecessary information, to make

data collection more time-efficient (Daniel et al., 2018). The survey used for this research could

have been further improved, by allowing the recording of multiple land uses for each plot.

Simplifying this to a dominant land use for each plot can mean that important information is

lost. Also when recording the canopy cover of each plot, it could also have been beneficial to

add qualitative descriptions of forest canopy cover to provide further detail of how the forest

has changed over the period of each project, for example: grouped forest, sparse forest etc.

Tailoring each survey individually to address the specific goals could help to make results more

meaningful for each specific project. However this will make comparison between projects

more difficult if the recorded results are very different.

6.4.6 Sampling Strategy

Collect Earth provides a platform where samples can be collected and then these can be

extrapolated to draw conclusions for a whole project area. It avoids having to conduct a time

intensive survey at ground level by using sampling to increase the effectiveness of monitoring

large land areas (Buckingham et al., 2017). However the accuracy of an assessment depends

on an appropriate sampling design and sampling intensity to be able to reflect the variability of

the land characteristics being assessed (Bey et al., 2016). The sampling approaches for the 3

projects in this research vary in terms of their sampling intensity, due to the differences in the

project areas. Time constraints prevented further sampling for each of the project areas.

However, a better sampling design should have been implemented initially to ensure that all

project areas were sampled equally, with a sampling density proportional to the project area.

7 Conclusion

This research aimed to investigate the impacts that FLR can have on degraded and deforested

landscapes through a comparative analysis of three projects across Latin America and the

Caribbean. Through augmented visual interpretation using the recently developed Collect

Earth tool, changes in forest ecosystems and land use were detected within the project areas.

Each of the projects were characterised with 5 datasets: Global Ecological Zones, Forest

Status, Restoration Potential, Potential Biomass Accumulation and AFOLU annual emissions.

The use of these datasets were not useful to characterise the project areas in detail due to

45

their coarse spatial resolution. However they provided an overall indication of the

circumstances in which each project area lies.

The greatest changes to the forest ecosystem were detected for the Argentina project area,

with 39 of the 50 sample plots changing from grassland to forest land, 40 showing a restoration

trend and 24 plots showing more than 80% canopy cover compared to just 6 before. This is

consistent with the primary goal of the project to increase vegetation cover by establishing a

forest plantation to increase CO2 sequestration. For the Haiti project area, the greatest land

use conversion was from forest land to other land (8 plots), changes in canopy cover were less

clear with a small reduction in canopy cover across the area and only 3 of 50 plots showed

restoration, whilst 16 showed degradation. Overall, the Haiti project area showed more

degradation than restoration. The main project goal to demarcate and protect existing forest

land to contain degradation doesn’t appear to have been met, with degradation of forest land

still occurring. In the Peru project area, the greatest land use conversion was grassland to

cropland (18 plots), whilst forest land remained intact, there were very few changes in terms

of canopy cover, whilst 5 of its 18 strata showed a restoration trend. These results do indicate

evidence for the restoration approach of introducing agroforestry and silvo-pasture practices,

that was implemented here.

It can also be concluded that the use of Collect Earth for monitoring FLR impacts is feasible. It

can increase the capacity of countries for monitoring FLR as it is a cost-effective option that is

open-source and freely available. It is also intuitive and useable for users lacking remote

sensing knowledge. Furthermore, it offers access to a wide range of satellite imagery for

analysis. However it is primarily useful for assessing more obvious land use changes and

changes in canopy cover which cannot be used to assess all FLR project goals. The more

subtle changes such as those related to agroforestry activities or sustainable forest

management are less easily detected. It also lacks full coverage of high-resolution imagery

both spatially and temporally which is needed to interpret changes reliably across large project

areas. If Collect Earth is to be used for such a purpose in the future it is vital that project goals

are clearly set out and the exact location of the project activities to be assessed are known.

Additionally, it is important that the sampling strategy and survey are adapted accordingly for

each project as the approaches different FLR projects take vary and cannot all be assessed in

the same way. Future studies assessing more FLR projects with a greater sampling density

are required to have a more complete overview of the applicability of Collect Earth for

assessing different FLR approaches.

46

References

Achard, F., Stibig, H. J., Eva, H. D., Lindquist, E. J., Bouvet, A., Arino, O., & Mayaux, P.

(2010) Estimating tropical deforestation from Earth observation data. Carbon Management.

Vol. 1, no. 2, p. 271-287.

Alexander, S., Nelson, C. R., Aronson, J., Lamb, D., Cliquet, A., Erwin, K. L., ... & Hobbs, R.

J. (2011) Opportunities and challenges for ecological restoration within REDD+. Restoration

Ecology. Vol. 19, no. 6, p. 683-689.

Asrat, Z., Taddese, H., Ørka, H., Gobakken, T., Burud, I., & Næsset, E. (2018). Estimation of

Forest Area and Canopy Cover Based on Visual Interpretation of Satellite Images in

Ethiopia. Land. Vol. 7, no. 3, p. 92.

Avitabile, V., Herold, M., Heuvelink, G. B. M., Lewis, S. L., Phillips, O. L., Asner, G. P.,

Armston, J., Ashton, P. S., Banin, L. (2016) An integrated pan-tropical biomass map using

multiple reference datasets. Global Change Biology. Vol. 22, p. 1406–1420.

Avitabile, V., Herold, M., Lewis, S.L., Phillips, O.L., Aguilar-Amuchastegui, N., Asner, G. P.,

Brienen, R.J.W., DeVries, B., Cazzolla Gatti, R. (2014) Comparative analysis and fusion for

improved global biomass mapping. Global Vegetation Monitoring and Modelling.

Baccini, A., Walker, W., Carvalho, L., Farina, M., Sulla-Menashe, D., & Houghton, R. A.

(2017) Tropical forests are a net carbon source based on aboveground measurements of

gain and loss. Science. Vol. 358, no. 6360, p. 230-234.

Bai, Z. G., Dent, D. L., Olsson, L., & Schaepman, M. E. (2008) Global assessment of land

degradation and improvement: 1. identification by remote sensing (No. 5). ISRIC-World Soil

Information.

Bautista, S. U. S. A. N. A., & Alloza, J. A. (2009) Evaluation of forest restoration

projects. Land restoration to combat desertification. CEAM, Valencia.

Bey, A., Sánchez-Paus Díaz, A., Maniatis, D., Marchi, G., Mollicone, D., Ricci, S., ... &

Patriarca, C. (2016) Collect Earth: Land use and land cover assessment through augmented

visual interpretation. Remote Sensing. Vol. 8, no. 10, p. 807.

Bey, A., Sanchez-Paus Diaz, A., Pekkarinen, A., Patriarca, C., Maniatis, D., Weil, D.,

Mollicone, D., Marchi, G., Niskala, J., Rezende, M. and Ricci, S. (2015) Collect Earth User

Manual: A guide to monitoring land use change and deforestation with free and open-source

software.

Birdlife International (2013) Species distribution maps. Online.

http://datazone.birdlife.org/home

Bonan, G. B. (2008) Forests and climate change: forcings, feedbacks, and the climate

benefits of forests. science,. Vol. 320, no. 5882, p. 1444-1449.

Bonn Challenge (2017) Bonn Challenge Latin America, 2017 Report. Roatan, Honduras,

June 12-13

47

Boose, E. R., Foster, D. R., & Fluet, M. (1994) Hurricane impacts to tropical and temperate

forest landscapes. Ecological Monographs. Vol. 64, no. 4, p. 369-400.

Boucher, D., Elias, P., Lininger, K., May-Tobin, C., Roquemore, S., & Saxon, E. (2011) The

root of the problem: what's driving tropical deforestation today?.

Buckingham, K., Ray, S., Stolle, F., & Zoveda, F. (2017) Measuring progress for forest and

landscape restoration. World Resources Institute and FAO, Washington DC and Rome, Italy.

Calvin, K. V., Beach, R., Gurgel, A., Labriet, M., & Rodriguez, A. M. L. (2016) Agriculture,

forestry, and other land-use emissions in Latin America. Energy Economics. Vol. 56, p. 615-

624.

Caughlin, T. T., Rifai, S. W., Graves, S. J., Asner, G. P., & Bohlman, S. A. (2016) Integrating

LiDAR‐derived tree height and Landsat satellite reflectance to estimate forest regrowth in a

tropical agricultural landscape. Remote Sensing in Ecology and Conservation. Vol. 2, no. 4,

p. 190-203.

CGIAR (2015) Sustainable development options and land-use based alternatives to:

enhance climate change mitigation and adaptation capacities in the Colombian and Peruvian

Amazon, while enhancing ecosystem services and local livelihoods. Online.

https://wle.cgiar.org/project/sustainable-development-options-and-land-use-based-

alternatives-enhance-climate-change. Accessed 12th November 2018.

CGIAR-CSI (2018) SRTM Data. Online. http://srtm.csi.cgiar.org/srtmdata/

Chazdon, R. L., & Uriarte, M. (2016) Natural regeneration in the context of large‐scale forest

and landscape restoration in the tropics. Biotropica. Vol. 48, no. 6, p. 709-715.

Chazdon, R. L., Brancalion, P. H., Lamb, D., Laestadius, L., Calmon, M., & Kumar, C. (2017)

A policy‐driven knowledge agenda for global forest and landscape restoration. Conservation

Letters. Vol. 10, no. 1, p. 125-132.

Chazdon, R. L., Harvey, C. A., Komar, O., Griffith, D. M., Ferguson, B. G., Martínez‐Ramos,

M., ... & Philpott, S. M. (2009) Beyond reserves: A research agenda for conserving

biodiversity in human‐modified tropical landscapes. Biotropica. Vol. 41, no. 2, p. 142-153.

Chiabai, A., Travisi, C. M., Markandya, A., Ding, H., & Nunes, P. A. (2011) Economic

assessment of forest ecosystem services losses: cost of policy inaction. Environmental and

Resource Economics. Vol. 50, no. 3, p. 405-445.

Clean Development Mechanism (2013) Reforestation of grazing Lands in Santo Domingo,

Argentina. Project Design Document for Afforestation and Reforestation Project Activities.

Version 6.

Clewell, A., Aronson, J., & Winterhalder, K. (2004) The SER international primer on

ecological restoration.

Convention on Biological Diversity (2002) Review of the status and trends of, and major

threats to, the forest biological diversity. (CBD Technical Series no. 7).

Convention on Biological Diversity (2018) Aichi Biodiversity Targets. Online.

https://www.cbd.int/sp/targets/ Accessed 12th November 2018

48

Daniel, J., Tenneson, K., Suber, M., Mulia, R., Van Tranh, P., Arango, J., & Rosenstock, T.

(2018). Open-and crowd-sourced MRV for agroforestry?: Preliminary results and lessons

learned from a pilot study using Collect Earth to identify agroforestry on multiple land uses in

Vietnam and Colombia.

De Sy, V., Herold, M., Achard, F., Beuchle, R., Clevers, J. G. P. W., Lindquist, E., & Verchot,

L. (2015) Land use patterns and related carbon losses following deforestation in South

America. Environmental Research Letters. Vol. 10, no. 12.

DeVries, B., Decuyper, M., Verbesselt, J., Zeileis, A., Herold, M., & Joseph, S. (2015)

Tracking disturbance-regrowth dynamics in tropical forests using structural change detection

and Landsat time series. Remote Sensing of Environment. Vol 169, p. 320-334.

Digital Globe (2019) Satellite Imagery. Online.

https://www.digitalglobe.com/products/satellite-imagery

European Space Agency (2015). The Land Cover CCI Climate Research Data Package

(CRDP). Online. http://maps.elie.ucl.ac.be/CCI/viewer/download.php

FAO (2010) Global forest resources assessment 2010: Main Report

FAO (2011) Assessing forest degradation: Towards the development of globally applicable

guidelines. Forest Resources Assessment Working Paper 177.

FAO (2012) Global ecological zones for FAO forest reporting: 2010 Update. Forest resources

Assessment Working Paper 179. Data available at:

http://www.fao.org/geonetwork/srv/en/main.home Report available at:

http://www.fao.org/docrep/017/ap861e/ap861e00.pdf

FAO (2016a) Global forest resources assessment 2015: how are the world's forests

changing? (2nd Edition)

FAO (2016b) Trees, Forests and Land Use in Drylands: The First Global Assessment, 2016.

Gardner, T. A., Barlow, J., Chazdon, R., Ewers, R. M., Harvey, C. A., Peres, C. A., & Sodhi,

N. S. (2009) Prospects for tropical forest biodiversity in a human‐modified world. Ecology

letters. Vol. 12, no. 6, p. 561-582.

Gibbs, H. K., Brown, S., Niles, J. O., & Foley, J. A. (2007) Monitoring and estimating tropical

forest carbon stocks: making REDD a reality. Environmental Research Letters. Vol. 2, no. 4.

Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A. A., Tyukavina, A.,

... & Kommareddy, A. (2013) High-resolution global maps of 21st-century forest cover

change. Science. Vol. 342, no. 6160, p. 850-853.

Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A. A., Tyukavina, A.,

... & Kommareddy, A. (2013) High-resolution global maps of 21st-century forest cover

change. Science. Vol. 342, no. 6160, p. 850-853.

Hansen, M. C., Stehman, S. V., & Potapov, P. V. (2010) Quantification of global gross forest

cover loss. Proceedings of the National Academy of Sciences. Vol. 107, no. 19, p. 8650-

8655.

49

Holl, K. D., & Zahawi, R. A. (2014) Factors explaining variability in woody above-ground

biomass accumulation in restored tropical forest. Forest Ecology and Management. Vol. 319,

p. 36-43.

Hosonuma, N., Herold, M., de Sy, V., de Fries, R. S., Brockhaus, M., Verchot, L., ... &

Romijn, E. (2012) An assessment of deforestation and forest degradation drivers in

developing countries. Environmental Research Letters. Vol. 7, no. 4.

Hutto, R. L., & Belote, R. T. (2013) Distinguishing four types of monitoring based on the

questions they address. Forest Ecology and Management. Vol. 289, p. 183-189.

IPCC (2003) Good Practice Guidance for Land Use, Land-Use Change and Forestry.

IPCC (2013) Climate Change 2013: The Physical Science Basis. Contribution of Working

Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change

ed T F Stocker, D Qin, K Plattner, M Tignor, S K Allen, J Boschung, A Nauels, Y Xia, V Bex

and P M Midgley (Cambridge: Cambridge University Press)

ITTO (2002) ITTO guidelines for the restoration, management and rehabilitation of degraded

and secondary tropical forests. ITTO Policy Development Series No. 13. Yokohama, Japan.

IUCN (2013) Spatial Data Download: Mammals. Online.

https://www.iucnredlist.org/resources/spatial-data-download

IUCN and WRI (2014). A guide to the Restoration Opportunities Assessment Methodology

(ROAM): Assessing forest landscape restoration opportunities at the national or sub-national

level. Working Paper.

Jenkins, C. N., Pimm, S. L., & Joppa, L. N. (2013) Global patterns of terrestrial vertebrate

diversity and conservation. Proceedings of the National Academy of Sciences. Vol. 110, no.

28, p. 2602-2610.

Köhl, M., Lasco, R., Cifuentes, M., Jonsson, Ö., Korhonen, K. T., Mundhenk, P., ... &

Stinson, G. (2015) Changes in forest production, biomass and carbon: Results from the 2015

UN FAO Global Forest Resource Assessment. Forest Ecology and Management. Vol. 352,

p. 21-34.

Laestadius, L., Maginnis, S., Minnemeyer, S., Potapov, P., Saint-Laurent, C., & Sizer, N.

(2011) Opportunities for forest landscape restoration. Unasylva. Vol. 62, no. 2, p. 238.

Lapola, D. M., Schaldach, R., Alcamo, J., Bondeau, A., Koch, J., Koelking, C., & Priess, J. A.

(2010) Indirect land-use changes can overcome carbon savings from biofuels in

Brazil. Proceedings of the national Academy of Sciences. Vol. 107, no. 8, p. 3388-3393.

Lejonc, G. and Palazy, L. (2018) Final Evaluation of the Sustainable Land Management of

the Upper Watersheds of South Western Haiti Program. Global Environmental Facility.

Lesiv, M., See, L., Laso Bayas, J. C., Sturn, T., Schepaschenko, D., Karner, M., ... & Fritz, S.

(2018). Characterizing the Spatial and Temporal Availability of Very High Resolution Satellite

Imagery for Monitoring Applications. Earth System Science Data Discussions. P. 1-24.

Lewis, S. L., Edwards, D. P., & Galbraith, D. (2015) Increasing human dominance of tropical

forests. Science. Vol. 349, no. 6250, p. 827-832.

50

Maginnis, S., Rietbergen-McCracken, J., & Sarre, A. (Eds.). (2012). The forest landscape

restoration handbook. Routledge.

Mansourian, S., Stanturf, J. A., Derkyi, M. A. A., & Engel, V. L. (2017) Forest Landscape

Restoration: increasing the positive impacts of forest restoration or simply the area under

tree cover? Restoration Ecology. Vol. 25, no. 2, p. 178-183.

Martínez, S., & Mollicone, D. (2012) From land cover to land use: A methodology to assess

land use from remote sensing data. Remote Sensing. Vol. 4, no. 4, p. 1024-1045.

McElwee, P. (2009) Reforesting" bare hills" in Vietnam: social and environmental

consequences of the 5 million hectare reforestation program. Ambio. Vol. 38, no. 6, p. 325-

333.

Méndez-Toribio, M., Martínez-Garza, C., Cecconc, E., & Guariguata, M. R. (2017) Current

ecological restoration plans in Latin America: Progress and omissions. Ciencias

Ambientales. Vol. 51, no. 2, p. 1-30.

Mendoza, G. A., Marchi, G., Quintos-Natividad, M., de Claro, M., Patiga, N., Rabang, B., ... &

SanchesPaus-Diaz, A. (2017) Using Collect Earth to assess and monitor land use change in

selected regions of the Philippines.

Müller, H., Rufin, P., Griffiths, P., Hissa, L. D. B. V., & Hostert, P. (2016) Beyond

deforestation: Differences in long-term regrowth dynamics across land use regimes in

southern Amazonia. Remote Sensing of Environment. Vol. 186, p. 652-662.

Munang, R. T., Thiaw, I., & Rivington, M. (2011) Ecosystem management: Tomorrow’s

approach to enhancing food security under a changing climate. Sustainability. Vol. 3, no. 7,

p. 937-954.

Newton, A. C., Del Castillo, R. F., Echeverría, C., Geneletti, D., González-Espinosa, M.,

Malizia, L. R., ... & Williams-Linera, G. (2012) Forest landscape restoration in the drylands of

Latin America. Ecology and Society. Vol. 17, no. 1.

Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A.

(2014). Good practices for estimating area and assessing accuracy of land change. Remote

Sensing of Environment. Vol. 148, p. 42-57.

Openforis (2019) Introduction to Collect. Online. http://www.openforis.org/home.html

Pimm, S. L., Jenkins, C. N., Abell, R., Brooks, T. M., Gittleman, J. L., Joppa, L. N., ... &

Sexton, J. O. (2014). The biodiversity of species and their rates of extinction, distribution, and

protection. Science. Vol. 344, no. 6187, p. 1246752.

Potapov, P., Laestadius, L., and Minnemeyer, S. (2011) Global map of forest cover and

condition. World Resources Institute: Washington, DC. Online.

http://www.wri.org/applications/maps/flr-atlas/#

Rademaekers K., Eichler L., Berg J., Obersteiner M., Havlik P., (2010) Study on the evolution

of some deforestation drivers and their potential impacts on the costs of an avoiding

deforestation scheme. Prepared for the European Commission by ECORYS and IIASA.

Rotterdam, Netherlands.

REDD+ (2016) UN-REDD Programme Fact Sheet: About REDD+

51

Rey-Benayas, J. M. R., Newton, A. C., Diaz, A., & Bullock, J. M. (2009) Enhancement of

biodiversity and ecosystem services by ecological restoration: a meta-analysis. Science. Vol.

325, no. 5944, p. 1121-1124.

Roman-Cuesta, R. M., Rufino, M. C., Herold, M., Butterbach-Bahl, K., Rosenstock, T. S.,

Herrero, M., ... & Martius, C. (2016) Hotspots of gross emissions from the land use sector:

patterns, uncertainties, and leading emission sources for the period 2000–2005 in the

tropics. Biogeosciences. Vol. 13, no. 14, p. 4253-4269.

Romijn, E., Lantican, C. B., Herold, M., Lindquist, E., Ochieng, R., Wijaya, A., ... & Verchot,

L. (2015) Assessing change in national forest monitoring capacities of 99 tropical

countries. Forest Ecology and Management. Vol. 352, p. 109-123.

Romijn, J. E., & Coppus, R. (2018) Restoration Database for Latin America and the

Caribbean. Comparative Research Project on Landscape Restoration for Emissions

Reductions, CIAT/WUR project for USAID [database] Online: http://lucid.wur.nl/

Romijn, J.E., Coppus, R., De Sy, V., Herold, M., Roman-Cuesta, R.M., Verchot,L. Land

restoration in Latin America and the Caribbean: An overview of recent, ongoing and planned

restoration initiatives and their potential for climate change mitigation. Submitted to Forests,

under review.

Rudel, T. K., Schneider, L., Uriarte, M., Turner, B. L., DeFries, R., Lawrence, D., ... &

Birkenholtz, T. (2009) Agricultural intensification and changes in cultivated areas, 1970–

2005. Proceedings of the National Academy of Sciences. Vol. 106, no. 49, p. 20675-20680.

Runyan, C., & D'Odorico, P. (2016) Global deforestation. (Cambridge University Press,

Cambridge).

Sabogal, C., Besacier, C., & McGuire, D. (2015) Forest and landscape restoration: concepts,

approaches and challenges for implementation. Unasylva. Vol. 66, no. 245, p. 3.

Santoro, M., Beaudoin, A., Beer, C., Cartus, O., Fransson, J.E.S., Hall, R.J., Pathe, C.,

Schmullius, C., Schepaschenko, D., Shvidenko, A., Thurner, M. and Wegmüller, U. (2015).

Forest growing stock volume of the northern hemisphere: Spatially explicit estimates for 2010

derived from Envisat ASAR. Remote Sensing of Environment. Vol. 168, p. 316-334.

Schepaschenko, D., Fritz, S., See, L., Bayas, J. C. L., Lesiv, M., Kraxner, F., & Obersteiner,

M. (2017). Comment on “The extent of forest in dryland biomes”. Science. Vol 358, no. 6362.

Simons, H. (2001) Global Ecological Zoning for the Global Forest Resources Assessment

2000. FRA Working Paper 56. FAO, Rome.

Smith, P., Bustamante, M., Ahammad, H., Clark, H., Dong, H., Elsiddig, E. A., ... & Masera, O.

(2014) Agriculture, forestry and other land use (AFOLU). In Climate change 2014: mitigation

of climate change. Contribution of Working Group III to the Fifth Assessment Report of the

Intergovernmental Panel on Climate Change. Cambridge University Press.

Stanturf, J. A., Kant, P., Lillesø, J. P. B., Mansourian, S., Kleine, M., Graudal, L., & Madsen,

P. (2015) Forest landscape restoration as a key component of climate change mitigation and

adaptation. (Vienna, Austria: International Union of Forest Research Organizations).

Stanturf, J. A., Palik, B. J., & Dumroese, R. K. (2014) Contemporary forest restoration: a

review emphasizing function. Forest Ecology and Management. Vol. 331, p. 292-323.

52

Stephenson, P. J., Burgess, N. D., Jungmann, L., Loh, J., O’Connor, S., Oldfield, T., ... &

Shapiro, A. (2015) Overcoming the challenges to conservation monitoring: integrating data

from in-situ reporting and global data sets to measure impact and performance. Biodiversity.

Vol. 16, no. 2-3, p. 68-85.

Suding, K., Higgs, E., Palmer, M., Callicott, J. B., Anderson, C. B., Baker, M., ... & Randall,

A. (2015) Committing to ecological restoration. Science. Vol. 348, no. 6235, 638-640.

UN (2014) Climate Summit 2014: FORESTS Action Statements and Action Plans UN

Headquarters, New York.

UNCCD (2017) Good Practice Guidance SDG Indicator 15.3.1: Proportion of land that is

degraded over total land area.

UNFCCC (2010) Outcome of the work of the Ad Hoc Working Group on long-term Cooperative

Action under the Convention – C. Policy approaches and positive incentives on issues relating

to reducing emissions from deforestation and forest degradation in developing countries; and

the role of conservation, sustainable management of forests and enhancement of forest carbon

stocks in developing countries. UNFCCC COP 16 Cancun. Available at:

https://unfccc.int/resource/docs/2010/cop16/eng/07a01.pdf#page=12

United Nations (2018) Sustainable Development Goals – 15 Life on Land. Online.

https://www.un.org/sustainabledevelopment/biodiversity/ Accessed 12th November 2018.

Verdone, M., & Seidl, A. (2017) Time, space, place, and the Bonn Challenge global forest

restoration target. Restoration Ecology. Vol. 25, no. 6, p. 903-911.

53

Appendix

A.1 Peru project area strata

54

Figure 35: The 18 strata of the Peru project area in terms of whether they indicated degradation, restoration or no change. Each strata contains 5 sample plots each classified as degradation,

restoration or no change and these are used to determine the overall result for each strata.