Crop classification and acreage estimation in North Korea using phenology features. Issue 3 (4th May 2017)

• Record Type: Journal Article
• Authors: Zhang, Huanxue; Li, Qiangzi; Liu, Jiangui; Shang, Jiali; Du, Xin; Zhao, Longcai; Wang, Na; Dong, Taifeng
• Is Part Of: GIScience & remote sensing. Volume 54:Issue 3(2017)
• Page Start: 381
• Page End: 406
• Publication Date: 2017-05-04
• Subjects: crop classification -- acreage estimation -- MODIS NDVI -- phenology -- North Korea; Geodesy -- Periodicals; Cartography -- Periodicals; Aerial photogrammetry -- Periodicals; Remote sensing -- Periodicals; 526.05
• DOI: 10.1080/15481603.2016.1276255
• Languages: English
• ISSNs: 1548-1603

Abstract

Abstract : In North Korea, reliable and timely information on crop acreage and spatial distribution is hard to obtain. In this study, we developed a fast and robust method to estimate crop acreage in North Korea using time-series normalized difference vegetation index (NDVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data. We proposed a method to identify crop type based on NDVI phenology features using data collected in other areas with similar agri-environmental conditions to mitigate the shortage of ground truth data. Eventually the classification map (MODIScrop) was assessed using the Food and Agriculture Organization (FAO) statistical data and high-resolution crop classification maps derived from one Landsat scene (LScrop). The Pareto boundary method was used to assess the accuracy and crop distribution of the MODIScrop maps. Results showed that acreage derived from the MODIScrop maps was generally consistent with that reported in the FAO data (a relative error <4.1% for rice and <6.1% for maize, and <9.0% for soybean except for in 2004, 2008, and 2009) and the maps derived from the LScrop (a relative error about 5% in 2013, and 7% in 2008 and 2014). The classification accuracy reached 74.4%, 69.8%, and 73.1% of the areas covered by the Landsat images in 2008, 2013, and 2014, respectively. This indicates that features derived from NDVI profiles were able to characterize major crops, and the approaches developed in this study are feasible for crop mapping and acreage estimation in regions with limited ground truth data.