[IEEE 2012 First International Conference on Agro-Geoinformatics - Shanghai, China (2012.08.2-2012.08.4)] 2012 First International Conference on Agro- Geoinformatics (Agro-Geoinformatics) - Satellite observations on agricultural adaptation to drought in southwestern China
Post on 22-Mar-2017
Satellite Observations on Agricultural Adaptation to Drought in Southwestern China
Yansheng Dong1, Hongping Chen 2, Deyong Yu3, Cunjun Li1*
1. Beijing Research Center for Information Technology in Agriculture, Beijing, China 2. Key Laboratory of Regional Climate-Environment Research for Temperate East Asia,
Institute of Atmosphere Physics, Chinese Academy of Sciences, Beijing, China 3. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China
AbstractChina southwest region has suffered from three years of severe drought in the dry season from 2010 to 2012, with devastating impacts on agricultural productions. Previous agricultural cropping patterns were adjusted to adapt to drought. How to find evidences of agricultural adaption to drought? Here, we showed agriculture adaptation to drought stress according to the changes of vegetation greenness, a proxy for agricultural drought conditions. Based on weather station observation from 1991 to April 2012, we concluded that the study area suffered from extreme drought in the dry reason of both 2010 and 2012. Notably, based on satellite monitoring of agricultural vegetation greenness in ten years, the results showed that drought intensity in 2010 and 2012 was almost equivalent (-66.85% in 2010 and -65.87% in 2012) and drought extension in 2012 was 14.52%, which was less than the half of 36.46% in 2010. Overall, the evidences of agricultural adaption to drought were obvious.
Keywords-agricultural drought; remote sensing; adaption; vulnerability
I. INTRODUCTION Drought is the major nature threat to agricultural production
in China, especially in the dry reason. From 2010 to 2012, and during January to March, severe droughts have occurred in southwest region of China. The sustained droughts have triggered serious agricultural losses. The agricultural drought losses were decided by three factors: hazard intensity, vulnerability and adaptive capacity [1-2]. Hazard intensity of agricultural drought refers to precipitation and temperature. The agricultural vulnerability refers to growth variation of vegetation. The agricultural drought adaptive capacity is defined as the response ability to mitigate drought impacts, such as through field management, or financial and social capital assets to improve respond ability . Among these three factors, agricultural adaption capacity to drought is rarely assessed. Vegetation indices derived from satellite data have been widely applied for drought monitoring and assessment, such as Vegetation Condition Index (VCI) derived from Advanced Very High Resolution Radiometer (AVHRR) data . Crop canopy greenness stressed by drought will be lower than normal growth vegetation. Based on these basic facts, green vegetation index can be acted as a proxy for agricultural drought monitoring.
To understand the impacts of agricultural drought, on the one hand, it needs to monitor agricultural drought conditions, on the other hand and more importantly, needs to assess the
adaptability capacity of agricultural drought. Adaptability capacity of agricultural drought, such as the technology of crop irrigation, can effectively reduce agricultural drought loss. Previous studies have paid less attention to agricultural adaptability to drought. If severe droughts have frequently occurred in a region, local people will take other more long-term measures to reduce drought impact on their lives. In this situation, agriculture will less sensitive to drought. For example, in southwest China, Yunnan province, has suffered from three years of continuous droughts from 2010 to 2012, local farmers began to use plastic film to cover crop field in order to prevent moisture evaporation. More recently, a challenge emergences: whether original planting scheme is adapt to continuous drought in this region? We noted that local people begin to plant corn as a rice substitute in order to reduce agricultural water requirement.
In this paper, from a new perspective based on time series satellite observation from 2000 to April 2012, we try to find agricultural drought adaptation evidences. Here, we hypothesize that if agriculture affected by sustained droughts, local farmer were forced to adapt to gradually drought, so from satellite remote sensing monitoring, the crop greenness will increase, and thus agricultural drought adaptation evidences will be showed up. Taking agricultural drought in Yunnan province as example, two questions were answered in this study: (1) whether agricultural growth conditions were affected by continuous droughts, (2) if the answer of question one was positive, then whether agricultural drought affected area decreased, which means agricultural adaption to drought. Firstly, the basic facts of drought were presented based on precipitation observation of weather station from 1991 to April 2012. Secondly, the extension and extent of agricultural drought from 2010 to 2012 were quantified based on satellite data from 2001 to April 2012. Finally, agricultural drought was analyzed by standardized anomalies of Normalized Difference Vegetation Index (NDVI) in the both of dry season and wet season to show up the phenomenon of agriculture adaption to droughts.
II. STUDY AREA AND DATA The northwest of Yunnan Province was chosen as a study
area to detect agricultural drought adaption based on remote sensing data. This region is main agriculture produce region and also has suffered from severe droughts in recent three years. The study area latitude is between 230 N and 26.50N and
* Corresponding author. firstname.lastname@example.org
longitude is between 1000 E and 103.50 E. In this region the dry season and the wet season are distinct. The dry season is from October to the following April and the wet season is from May to September. Most of annual rainfalls centralize in the wet season. In the wet season, the main crops include tobacco, corn and rice, which are the main income source from farmers. In the dry season, the main crops include winter wheat, potato and rape, which are vulnerable to drought during their growing season. Since this study interest is agricultural drought, the seasonal time scales of 3-month were considered to be appropriate . Additional, taking crop growth characteristics into account in this region, the dry season was defined as from January to March (JJM) and the wet season was from June to August (JJA).
In this study, two Moderate resolution Imaging Spectroradiometer (MODIS) products were used, including the Collection5 MODIS 500m land cover (MCD12Q1) and MODIS BRDF (bi-directional reflectance distribution function)/albedo product (MCD43A4) from 2000 to 2011. Precipitation data, derived from 18 weather stations in and around study area from 1991 to April 2012, were selected to calculate monthly precipitation.
III. METHODS The cereal crop pixel and the broadleaf crop pixel in the
study were masked as cropland by using the International Geosphere Biosphere Programme (IGBP) land cover classification scheme . NDVI Time series was calculated based on MCD43A4 production. The pixels of which NDVI values great than 0 and less than 1 were chosen as valid data.
NDVI Standardized anomalies (anomaly divided by the standard deviation) were calculated for all 2010, 2011 and 2012 cropland pixels as,
Where, is the standardized anomaly of NDVI in a specific year (2010, 2011or 2012). x is mean value in the dry season or the wet season in either of 2010, 2011or 2012. is historical mean value of NDVI during the dry season or the wet season in the past nine years (2000-2009). is standard deviation of NDVI during the dry season or the wet season from 2000 to 2009.
IV. RESULTS AND DISCUSSION
A. Drought Intensity
We used monthly precipitation anomaly percentage as the sign of drought intensity. The monthly precipitation anomaly percentage from 18 meteorological stations was calculated, as shown in Figure 1. In 2010, the values of the monthly precipitation anomaly percentage during the dry season were -71.18, -78.61 and -48.82 respectively. The monthly precipitation anomaly percentage was -88.94 on February 2011. Besides, in the first four months of 2012, the results were -
12.44, -94.22, -93.88 and -38.67 respectively. Synchronously, the means of monthly precipitation anomaly percentage during the dry or the wet season were calculated, and they were -65.87, 15.04, and -66.85 respectively. When monthly precipitation anomaly is less than -45.00 in a region, the extreme meteorological drought has occurred. Therefore, it has suffered from extreme droughts in the dry season of 2010 and 2012. Distinctly, the meteorological drought intensity in 2010 and 2012 were almost equivalent.
Figure 1. The percentage of monthly precipitation anomaly
B. Drought Patterns in the Dry Season
The spatial patterns of NDVI Standardized anomaly in the dry season of 2010, 2011 and 2012 were show in Figure 2. There is drought stress if NDVI Standardized anomalies were less than -1 . So the percent of cropland suffered from drought in the dry season of 2010, 2011 and 2012 were 36.46%, 6.57% and 14.52%. Obviously, the extension of drought affected in 2012 was less than the half of 36.46% in 2010. Serious drought concentrated in the northwest region in recent three years, which indicated that if a region has suffered from severe drought stress, the region was still sensitivity to drought.
The decline of drought intensification in 2012 can be found according to the distributions of NDVI anomalies (Figure 3). The NDVI anomalies in 2010 displayed a strong positive skew, i.e. characterized by a majority of negative anomalies, with a peak value between 0.5 and 1 std. A similar positive skew was also observed in the distribution of NDVI anomalies in 2012, with a peak value at about 0.5 std. Distributions of greenness anomalies means that drought impact in 2012 was weaker than in 2010. Based on the hypothesis above, thus, the evidences of agricultural adaption to drought were obvious.
Figure 3. Distributions of greenness anomalies within the cropland area affected by droughts in 2010 2011 and 2012. Shown here are January to
March (JJM) standardized anomalies of NDVI during January to March 2010 (red lines), 2011(blue lines) and 2012 (pick lines).
C. Drought Patterns in the Wet Season
When the precipitation almost equals to historical data in a region where would not affect by drought, which was as shown in Figure 4. In wet season, only 0.64% of cropland area in 2010 was anomalistic and 0.20% in 2012. Slightly, it indicated that cropland area affected by droughts in the wet season of 2012 was less than that of 2010.
The decline of drought intensification in 2010 and 2011 can not be found acording to the distributions of NDVI anomalies in the wet reason (Figure 5). The NDVI anomalies in 2010 displayed a weak positive skew, with a peak value at about -0.5 std. However, a positive skew was also observed in the distribution of NDVI anomalies in 2011, with a peak value at about 0.5 std. Distributions of greenness anomalies in the wet reason means that there were no drought impact on agriculture.
Figure 5. Distributions of greenness anomalies within the cropland area in both of 2010 and 2011. Shown here are June to August (JJA) standardized anomalies of NDVI during June to June 2010 (red lines), 2011(blue lines).
V. CONCLUSIONS This study took monthly precipitation anomaly
percentage as a sign of drought intensity, and the results indicated that the meteorological drought intensity in 2010 and 2012 were almost equivalent. However, the extension of drought in 2012 was less than the half of that in 2010. In general, we conceded that the evidences of agricultural adaption to drought were obvious. In order to further understand agricultural adaption, temperature data in the study area will be used in the future work.
ACKNOWLEDGMENT The work is supported by Special the Fund for Agro-
scientific Research in the Public Interest of China (200903010) and the National Natural Science Foundation (NNSF) of China (41001199).
REFERENCES  Simelton, E., Fraser, E. D. G., Termansen, M., Forster, P. M., & Dougill,
A. J. Typologies of crop-drought vulnerability: an empirical analysis of the socioeconomic factors that influence the sensitivity and resilience to drought of three major food crops in China (1961-2001), Environmental Science and Policy, Vol.12, No 4, pp. 438-452, 2009.
 Philip, A., Evan, D.G.F., Andrew J. D., Lindsay, C.S., and Elisabeth S., Mapping the vulnerability of crop production to drought in Ghana using rainfall, yield and socioeconomic data, Applied Geography, Vol.32, pp.324-344, 2011.
 Kogan, F, Application of vegetation index and brightness temperature for drought detection, Advances in Space Research, vol.15, pp.91-100, 1995.
 Rouault, M., & Richard, Y., Intensity and spatial extension of drought in South Africa at different time scales, Water SA, 29, pp.489 500, 2003.
 Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., and Huang, X., MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets, Remote Sensing of Environment, Vol.114, No 1, pp.168-182, 2010.
 Xu, L., Samanta, A., Costa, M. H., Ganguly, S., Nemani, R.R., & Myneni, R. B., Widespread decline in greenness of Amazonian vegetation due to the 2010 drought, Vol. 38, pp. Geophysical Research Letters, 2011.
a JFM2010 b JFM2011 C JFM2012
Figure 2. Spatial patterns of January to March (JJM) standardized anomalies of NDVI in cropland affected by drought (NDVI anomalies are less than -1 standardized anomalies).
a JJA 2010 (b) JJA2011 Figure 4. Spatial patterns of June to August (JJA) 2010(a) and 2011(b) NDVI standardized anomalies in vegetated areas of drought (NDVI anomalies are that less
than -1 standardized anomalies).
/ColorImageDict > /JPEG2000ColorACSImageDict > /JPEG2000ColorImageDict > /AntiAliasGrayImages false /CropGrayImages true /GrayImageMinResolution 200 /GrayImageMinResolutionPolicy /OK /DownsampleGrayImages true /GrayImageDownsampleType /Bicubic /GrayImageResolution 300 /GrayImageDepth -1 /GrayImageMinDownsampleDepth 2 /GrayImageDownsampleThreshold 1.50000 /EncodeGrayImages true /GrayImageFilter /DCTEncode /AutoFilterGrayImages false /GrayImageAutoFilterStrategy /JPEG /GrayACSImageDict > /GrayImageDict > /JPEG2000GrayACSImageDict > /JPEG2000GrayImageDict > /AntiAliasMonoImages false /CropMonoImages true /MonoImageMinResolution 400 /MonoImageMinResolutionPolicy /OK /DownsampleMonoImages true /MonoImageDownsampleType /Bicubic /MonoImageResolution 600 /MonoImageDepth -1 /MonoImageDownsampleThreshold 1.50000 /EncodeMonoImages true /MonoImageFilter /CCITTFaxEncode /MonoImageDict > /AllowPSXObjects false /CheckCompliance [ /None ] /PDFX1aCheck false /PDFX3Check false /PDFXCompliantPDFOnly false /PDFXNoTrimBoxError true /PDFXTrimBoxToMediaBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXSetBleedBoxToMediaBox true /PDFXBleedBoxToTrimBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXOutputIntentProfile (None) /PDFXOutputConditionIdentifier () /PDFXOutputCondition () /PDFXRegistryName () /PDFXTrapped /False
/CreateJDFFile false /Description >>> setdistillerparams> setpagedevice