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基于物候学的 MODIS-NDVI 时间序列残差趋势分析评估人为土地退化。

Phenology-Based Residual Trend Analysis of MODIS-NDVI Time Series for Assessing Human-Induced Land Degradation.

机构信息

School of Information Engineering, China University of Geosciences, Beijing 100083, China.

Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China.

出版信息

Sensors (Basel). 2018 Oct 29;18(11):3676. doi: 10.3390/s18113676.

DOI:10.3390/s18113676
PMID:30380671
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6263966/
Abstract

Land degradation is a widespread environmental issue and an important factor in limiting sustainability. In this study, we aimed to improve the accuracy of monitoring human-induced land degradation by using phenological signal detection and residual trend analysis (RESTREND). We proposed an improved model for assessing land degradation named phenology-based RESTREND (P-RESTREND). This method quantifies the influence of precipitation on normalized difference vegetation index (NDVI) variation by using the bivariate linear regression between NDVI and precipitation in pre-growing season and growing season. The performances of RESTREND and P-RESTREND for discriminating land degradation caused by climate and human activities were compared based on vegetation-precipitation relationship. The test area is in Western Songnen Plain, Northeast China. It is a typical region with a large area of degraded drylands. The MODIS 8-day composite reflectance product and daily precipitation data during 2000⁻2015 were used. Our results showed that P-RESTREND was more effective in distinguishing different drivers of land degradation than the RESTREND. Degraded areas in the Songnen grasslands can be effectively detected by P-RESTREND. Therefore, this modified model can be regarded as a practical method for assessing human-induced land degradation.

摘要

土地退化是一个广泛存在的环境问题,也是限制可持续性发展的一个重要因素。在本研究中,我们旨在通过使用物候信号检测和剩余趋势分析(RESTREND)来提高监测人为土地退化的准确性。我们提出了一种名为基于物候学的 RESTREND(P-RESTREND)的改进模型来评估土地退化。该方法通过使用预生长季和生长季 NDVI 与降水之间的双变量线性回归来量化降水对归一化植被指数(NDVI)变化的影响。根据植被-降水关系,比较了 RESTREND 和 P-RESTREND 区分气候和人为活动引起的土地退化的性能。试验区位于中国东北松嫩平原西部,是大面积退化旱地的典型区域。使用了 MODIS 8 天合成反射率产品和 2000-2015 年期间的日降水数据。结果表明,P-RESTREND 比 RESTREND 更有效地区分土地退化的不同驱动因素。P-RESTREND 可以有效地检测松嫩草原的退化区域。因此,该改进模型可视为评估人为土地退化的实用方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b6e/6263966/b1787a4364fb/sensors-18-03676-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b6e/6263966/7beb83b9f5cd/sensors-18-03676-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b6e/6263966/91860b7769c6/sensors-18-03676-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b6e/6263966/702c1be38d34/sensors-18-03676-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b6e/6263966/b00fff3804ab/sensors-18-03676-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b6e/6263966/b1787a4364fb/sensors-18-03676-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b6e/6263966/7beb83b9f5cd/sensors-18-03676-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b6e/6263966/91860b7769c6/sensors-18-03676-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b6e/6263966/702c1be38d34/sensors-18-03676-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b6e/6263966/b00fff3804ab/sensors-18-03676-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b6e/6263966/b1787a4364fb/sensors-18-03676-g005.jpg

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