Tracking Forest Disturbance in Northeast China’s Cold-Temperate Forests Using a Temporal Sequence of Landsat Data

Author:

Wang Yueting123,Jia Xiang45ORCID,Zhang Xiaoli12ORCID,Lei Lingting12,Chai Guoqi12,Yao Zongqi12,Qiu Shike45,Du Jun45,Wang Jingxu45ORCID,Wang Zheng45,Wang Ran6

Affiliation:

1. Beijing Key Laboratory of Precision Forestry, College of Forestry, Beijing Forestry University, Beijing 100083, China

2. Key Laboratory of Forest Cultivation and Protection, Ministry of Education, Beijing Forestry University, Beijing 100083, China

3. National Engineering Laboratory for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

4. Institute of Geographical Sciences, Henan Academy of Sciences, Zhengzhou 450052, China

5. Key Laboratory of Remote Sensing and Geographic Information Systems in Henan Province, Zhengzhou 450052, China

6. Henan Provincial Forestry Ecological Construction and Development Center, Zhengzhou 450003, China

Abstract

Cold-temperate forests (CTFs) are not only an important source of wood but also provide significant carbon storage in China. However, under the increasing pressure of human activities and climate change, CTFs are experiencing severe disturbances, such as logging, fires, and pest infestations, leading to evident degradation trends. Though these disturbances impact both regional and global carbon budgets and their assessments, the disturbance patterns in CTFs in northern China remain poorly understood. In this paper, the Genhe forest area, which is a typical CTF region located in the Inner Mongolia Autonomous Region, Northeast China (with an area of about 2.001 × 104 km2), was selected as the study area. Based on Landsat historical archived data on the Google Earth Engine (GEE) platform, we used the continuous change detection and classification (CCDC) algorithm and considered seasonal features to detect forest disturbances over nearly 30 years. First, we created six inter-annual time series seasonal vegetation index datasets to map forest coverage using the maximum between-class variance algorithm (OTSU). Second, we used the CCDC algorithm to extract disturbance information. Finally, by using the ECMWF climate reanalysis dataset, MODIS C6, the snow phenology dataset, and forestry department records, we evaluated how disturbances relate to climate and human activities. The results showed that the disturbance map generated using summer (June–August) imagery and the enhanced vegetation index (EVI) had the highest overall accuracy (88%). Forests have been disturbed to the extent of 12.65% (2137.31 km2) over the last 30 years, and the disturbed area generally showed a trend toward reduction, especially after commercial logging activities were banned in 2015. However, there was an unusual increase in the number of disturbed areas in 2002 and 2003 due to large fires. The monitoring of potential widespread forest disturbance due to extreme drought and fire events in the context of climate change should be strengthened in the future, and preventive and salvage measures should be taken in a timely manner. Our results demonstrate that CTF disturbance can be robustly mapped by using the CCDC algorithm based on Landsat time series seasonal imagery in areas with complex meteorological conditions and spatial heterogeneity, which is essential for understanding forest change processes.

Funder

National Key Research and Development Program of China

Henan Academy of Sciences, China “Innovation Team Project of Henan Academy of Sciences”

Department of Science and Technology of Henan Province, China “Joint Fund of Henan Province Science and Technology R&D Program”,

Henan Academy of Sciences, China ”Talent Development Special Project of Henan Academy of Sciences”

Publisher

MDPI AG

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