An Open-Boundary Locally Weighted Dynamic Time Warping Method for Cropland Mapping

ISPRS International Journal of Geo-Information

Xuelian Meng¹  Xarapat Ablat²  Gaohuan Liu²  Zhuoran Chen²  Chong Huang²  Chunsheng Wu²  Xudong Guan²  Qiang Wang²  Qingsheng Liu² 

[1] Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA;State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;

关键词: dynamic time warping (DTW);    remote sensing;    Lower Mekong Basin;    MODIS;    time series;    crops;    cropland;    classification;   

DOI  :  10.3390/ijgi7020075

来源: DOAJ

【 摘 要 】

This paper proposes an open-boundary locally weighted dynamic time warping (OLWDTW) method using MODIS Normalized Difference Vegetation Index (NDVI) time-series data for cropland recognition. The method solves the problem of flexible planting times for crops in Southeast Asia, which has sufficient thermal and water conditions. For NDVI time series starting at the beginning of the year and terminating at the end of the year, the method can separate the non-growing season cycle and growing season cycle for crops. The non-growing season cycle may provide some useful information for crop recognition, such as soil conditions. However, the shape of the growing season’s NDVI time series for crops is the key to separating cropland from other land cover types because the shape contains all of the crop growth information. The principle of the OLWDTW method is to enhance the effects of the growing season cycle on the NDVI time series by adding a local weight to the growing season when comparing the similarity of time series based on the open-boundary dynamic time warping (DTW) method. Experiments with two satellite datasets located near the Khorat Plateau in the Lower Mekong Basin validate that OLWDTW effectively improves the precision of cropland recognition compared to a non-weighted open-boundary DTW method in terms of overall accuracy. The method’s classification accuracy on cropland exceeds the non-weighted open-boundary DTW by 5–7%. In future studies, an open-boundary self-adaption locally weighted DTW and a more effective combination rule for different crop types should be explored for the method’s best performance and highest extraction accuracy for cropland.

【 授权许可】

Unknown