ABSTRACT

Mapping the distribution and type of land use and land cover (LULC) is essential for watershed management. The Tigris-Euphrates basin is a transboundary region in the Middle East shared between six countries, but a recent fine-scale LULC map of the area is lacking. Using Landsat-8 time series, a 30-m resolution LULC map was produced for the Tigris-Euphrates basin. In total, 1184 Landsat scenes were processed within the Google Earth Engine (GEE). For the collection of ground truth data, differential manifestations of green cover were considered by dividing the study area into five climatic regions and the training samples were taken from each sub-region. To account for the temporal variation of LULC types, six two-month interval composite layers, including the spectral and thermal bands of Landsat-8, texture and spectral indices, as well as topographic factors were created for the target year 2019. Image segmentation and classification were performed using the simple non-iterative clustering (SNIC) and Random Forest (RF) algorithms, respectively. A computationally effective parallel processing approach was developed, which created a number of tiles and sub-tiles and a bulk command was converted into smaller parallel commands. The generated LULC map showed a satisfactory overall accuracy of 91.7%, with the highest User’s accuracy in water and wetland, and the lowest in rainfed crop and rangeland and the highest Producer’s accuracy in water and barren areas, and the lowest in garden and rangeland. This study highlights the necessity of using multi-temporal data for LULC mapping, in particular, multi-temporal NDVI, for the separation of different green cover types in arid and semi-arid environment.

摘要

绘制土地利用和土地覆盖 (LULC) 的分布和类型对于流域管理至关重要。底格里斯河-幼发拉底河流域是中东的一个跨界区域，由六个国家共享，但最近缺乏该地区的精细 LULC 地图。使用 Landsat-8 时间序列，为底格里斯河-幼发拉底河流域制作了 30 米分辨率的 LULC 地图。Google Earth Engine (GEE) 总共处理了 1184 个 Landsat 场景。对于地面实况数据的收集，通过将研究区域划分为五个气候区域，并从每个子区域中提取训练样本，考虑了绿色覆盖的差异表现。为了解释 LULC 类型的时间变化，六个两个月间隔的复合层，包括 Landsat-8 的光谱和热带、纹理和光谱指数，以及为 2019 年目标年创建的地形因素。分别使用简单的非迭代聚类 (SNIC) 和随机森林 (RF) 算法执行图像分割和分类。开发了一种计算上有效的并行处理方法，该方法创建了许多图块和子图块，并将批量命令转换为较小的并行命令。生成的 LULC 地图显示总体准确率为 91.7%，用户在水和湿地的准确度最高，雨养作物和牧场最低，生产者在水和荒地的准确度最高，花园和牧场最低。本研究强调了使用多时相数据进行 LULC 制图的必要性，尤其是多时相 NDVI，