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Object oriented extraction of reserve resources area for cultivated land using RapidEye image data
Yanmin YAO Key Laboratory of Agri-informatics
Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences
Haiqing SI, Deying WANG Institute of Agricultural Resources and Regional Planning
Chinese Academy of Agricultural Sciences Beijing, P.R.China
Abstract Identify the amount and spatial distribution of
reserve cultivatable land resources is the basis for its development to increase crop planting areas. Taking Jiaxiang county of Shandong Province of China as a case study, this paper conducted image segmentation and merge based on RapidEye image data (5m spatial resolution) after data preprocessing. Then, object-oriented approach was used to classify land use information and the reserve resources of arable land were extracted from them. The results showed: (1) 30% and 80% of the scale level and merge level for image segmentation and merge were chosen for getting better results of independent polygon division based on object-oriented approach; (2) Comparing with K near value method (KNN) and principal component analysis method (PCA), support vector machine (SVM) method had 78% of the highest overall accuracy for the supervised classification; (3) The overall land use classification accuracy was 90.4% verified by field survey data and 1:10000 land use map in 2011, Kappa coefficient was 0.8784. Therefore, using high spatial resolution image can improve the classification accuracy for the reserve cultivatable land resources; (4) Bare land, wild grassland, mudflats and reed land were the main reserve resources of cultivated land for the study region. The area was 2640 ha occupying 2.95% of the total land area only. The area of bare land and wild grassland accounts for 61% and 35% of the reserve cultivatable land resources. Thin soil thickness and lack of irrigation facilities were major limit factors for its development to cropland.
Keywords- reserve resources of cultivated land; object-oriented classification; RapidEye image; Jiaxiang county of China
I. INTRODUCTION Reserve resources refer to be improved from unused land to
cultivated land by developing measures at the current technical conditions (TD/T 1007, 2003). Identifying its amount and spatial distribution is very important to make the reserve resource development plan. China has conducted a nationwide investigation of reserve resources from 2001-2003 by using TM images (30m) (Wen, 2005). As the supervised classification method is based on pixel spectral information, the classification accuracy is not very high. The object-oriented classification method is based on not only spectral information, but also considering its space and texture, more suitable for high spatial resolution image interpretation. It has been used for detection of land use change (Cai, 2008; Han, 2009; Guo, 2010; Luo, 2013), forest resource monitoring (Shi, 2010; Zhou, 2013) and crop area monitoring (Zhang, 2008;
Wang 2008a; Wang, 2008b; Tang, 2010; Li, 2012). The software used for object-oriented classification method is eCognition mostly, but less ENVI. This paper took RapidEye images (5m) as data sources, conducted land use classification by using the object-oriented classification method. Then the reserve resources information was extracted to provide basic data for the development of reserve cultivatable land resources. The study area is Jiaxiang County of Shandong Province in China. The software used for the study is ENVI.
II. MATERIALS AND METHODS
A. Study area Jiaxiang County located in the west of Jining City of
Shandong Province with longitude 11606 ' to 11627' and latitude 3511'~ 3538'. The total area was about 960 km2. It was on the edge of the Huang-Huai alluvial plain. The terrain sloped from northwest to southeast with an altitude of 35-40m. It belongs to warm temperate continental monsoon climate. Average annual temperature is 13.9 . Accumulated temperature above 10 was 4644 . The annual average sunshine time is 2405.2 hours. The frost-free period was 210 days.
B. Data collection RapidEye images processing level 1B level on April 13,
2013 was acquired as data sources. It contains five bands with 6.5m spatial resolution. The orthophoto pixel size is 5m. During this time, winter wheat in the study area was at the jointing stage. Trees and grass appeared shooting or young leaves. The spectrum of the image about features had a large differences and easy to be distinguished.
1:10,000 land use map in 2011 and ground truth data in September 2013 were used as training and validation samples for the land use image interpretation accuracy. DEM data with 30m resolution was also used for the image segmentation to improve classification accuracy.
C. RapidEye image reprocessing RapidEye 1B level data has been corrected at radiometric
correction and geometry correction already. 34 control points were selected from Google Earth data as a reference points for the geometric precision correction by using ENVI 5.0 software. The projection was UTM WGS84. The second-order polynomial correction method was used for the correction.
The study supported by National Basic Research Program of China (973 Program: 2010CB951501)
RMS was 0.15 and the pixel size was resampled to 5m. FLASSH method was used for atmospheric correction. The image was cutting out by the boundary of Jiaxiang County ready for land use classification and reserve cultivatable land resources extraction.
D. Classification approach for reserve cultivatabal land resources The object-oriented approach was used for land use
interpretation. The method can be divided into two steps. First is image segmentation. The adjacent image pixel is divided into separate units as classified objects. Then, land use types were distinguished through processes of training samples selection, object properties calculation, classification rules construction and image classification conduction.
III. RESULTS AND DISCUSSION
A. Image segmentation DEM data with 30m resolution and NDVI were used as
auxiliary information for improving image segmentation accuracy. Image segmentation means that the adjacent pixel with similar value is divided into separate units. Sobel edge algorithm in ENVI was used for edge detection. Only one parameter, Scale Level, need to be input to split out more independent unit. Meanwhile, a combined input parameter, Merge Level, can combine adjacent cells into one cell. The values selected for Scale Level and Merge Level were very important for classification accuracy.
In order to get the best split and merge value, we selected values from 10% to 90%. Through visual comparison, the segmentation scale of 10% and 20% resulted to too fine segmentation. The segmentation scale of above 40% resulted not to completely separation. So, we selected the split value and merge scale were 35% and 80%, 20% and 90%, 30% and 80% with classification method of the support vector machine (SVM). Based on visual and quantitative comparison, the split and merge scales were selected as 30% and 80% for the best RapidEye image segmentation value (Table1, Figure 1).
TABLE I. COMPARISON OF DIFFERENT VALUES OF SEGMENTATION, MERGE AND DIFFERENT CLASSIFICATION ACCURACY
SVM 35 80 0.7096 77.59 SVM 20 90 0.6961 76.39 SVM 30 80 0.7149 78.00 KNN 30 80 0.6589 74.07 PCA 30 80 0.2462 31.86
B. Image classification Considering spectral information as well as the spatial and
texture information, 4000 training samples was selected for the establishment of land use interpreting features combined with field survey data, land use data and statistical data. ENVI provides 3 kinds of methods for object-oriented classification, K near value method (KNN), support vector machine (SVM), and principal component analysis (PCA). We compared these methods in order to select the best ones. The study showed that SVM had 78%the highest overall accuracy for land use
classification and was selected for image classification (Table1).
C. Accuracy assessment Taking the field investigation data and 1:10,000 land use
map of Jiaxiang County in 2011 as validation data, 4 to 2000 samples for each class types were selected for accuracy assessment. The results showed that the overall classification accuracy was 90.39%, Kappa coefficient was 0.8784 (Table 2).
TABLE II. STATISTICS FOR LAND USE CLASSIFICATION ACCURACY
Land use type Error% Omission% Mapping accuracy% User
accuracy% Water 2.58 0.37 99.63 97.42
Cultivated land 2.27 0.02 99.99 97.73 Building 4.81 2.76 97.24 95.19
Green house 0.00 0.80 99.20 100.00 Mudflats 0.00 1.92 98.08 100.00
Forest 64.57 20.03 79.97 35.43 Orchard 44.92 36.44 63.56 55.08
Reed land 0.00 0.20 99.80 100.00 Wild grassland 10.16 49.64 50.36 89.84
Bare land 16.89 15.62 84.38 83.11
Table 2 showed that the highest error classified type was forest with 64.57%. The highest omission classified type were orchard and grassland with above 35%. The lower mapping accuracy or user accuracy was forest, orchard and grassland, less than 80%. The possible reasons were as follows:
1. The image was obtained on April 13, 2013, when the vegetation just began to sprout. Therefore, the spectral difference between forest and grassland was not obvious.
2. The study area has a large area of the seedling planting base. In April, some seedlings have not grown up. It did not show vegetation characteristics obviously.
3. The spectral characteristics of bare land and building was quiet si