Quantification of carbon sequestration by urban forest using Landsat 8 OLI and machine learning algorithms in Jodhpur,India

Urban forests play a significant role in carbon cycling. Quantification of Aboveground Biomass (AGB) is critical to understand the role of urban forests in carbon sequestration. In the present study, Machine learning (ML) based regression algorithms (SVM, RF, kNN and XGBoost) have been taken into account for spatial mapping of AGB and carbon for the urban forests of Jodhpur city, Rajasthan, India, with the aid of field-based data and their correlations with spectra and textural variables derived from Landsat 8 OLI data. A total of 198 variables were retrieved from the satellite image, including bands, Vegetation Indices (VIs), linearly transformed variables, and Grey Level Co-occurrence textures (GLCM) taken as independent input variables further reduced to 29 variables using Boruta feature selection method. All the models have been compared where with RF algorithm, R² = 0.83, RMSE = 16.22 t/ha and MAE = 11.86 t/ha. For kNN algorithm R² = 0.77, RMSE = 28.04 t/ha and MAE = 24.24 t/ha and SVM where R² = 0.73, RMSE = 89.21 t/ha and MAE = 74.22 t/ha and the best prediction accuracy has been noted with XGBoost algorithm (R² = 0.89, RMSE = 14.08 t/ha and MAE = 13.66 t/ha) with predicted AGB as 0.51−153.76 t/ha. The study indicates that ML-based regression algorithms have great potential over other linear and multiple regression techniques for spatial mapping of AGB and carbon of urban forests for arid regions.     

基于Landsat 8数据的荒漠土壤水分遥感反演

[目的]分析荒漠土壤水分变化特征,为南疆干旱区荒漠土壤水分遥感监测提供理论依据和方法支持.[方法]以Landsat 8数据构建干旱地区荒漠土壤水分建模指示因子,通过优选的26个光谱指数、地表温度(Ts)和地形数据(DEM)为建模因子,分别以偏最小二乘(PLSR)、支持向量机(SVM)和随机森林(RF)3种方法构建土壤水...     

基于Markov-PLUS模型的柴北缘土地利用变化及模拟分析

以柴达木盆地北缘为研究区域，分析近30 a柴北缘土地利用时空变化，可为区域生态环境保护 、土地利用方式优化提出建议。基于1990、2000、2010、2020年4期Landsat影像数据，获取土地利用空间分布数据，借助转移矩阵、重心模型对近30 a柴北缘土地利用时空变化及驱动力计算分析，利用Markov-PLUS复合模型对2040年柴北缘土地利用变化进行预测和模拟。结果表明:1)未利用地、林地、水域是柴北缘的主要用地类型。近30 a耕地面积缓慢增加，重心向西南方向移动，建设用地面积增长迅速，林地、草地、水域面积总体呈上升趋势，重心都向西北方向移动，未利用地面积持续下降。2)使用FLUS模型与Markov-PLUS模型分别预测柴北缘2020年土地利用情况并对比分析，Markov-PLUS模型比FLUS模型的模拟准确性更高。3)2040年多情景模拟结果表明，柴北缘土地利用整体格局一致性较高，城镇发展和耕地保护情景下均会挤占林地、草地，对可持续发展造成威胁，在区域生态安全建设中应加以重视。PLUS模型与Markov模型相结合，实现了对未来土地利用斑块级的精准预测，可为未来提升柴北缘生态退化整治效果、城镇开发边界与生态保护红线的制定与规划等提供科学决策参考。