Detecting of forest  afforestation and deforestation in Hainan Jianfengling Forest Park (China) using yearly Landsat time-series images

Quanjun Jiao; Xiao Zhang; Qi Sun

doi:10.1117/12.2285023

8 March 2018 Detecting of forest afforestation and deforestation in Hainan Jianfengling Forest Park (China) using yearly Landsat time-series images

Quanjun Jiao, Xiao Zhang, Qi Sun

Author Affiliations +

Proceedings Volume 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 106111A (2018) https://doi.org/10.1117/12.2285023
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

Abstract

The availability of dense time series of Landsat images pro-vides a great chance to reconstruct forest disturbance and change history with high temporal resolution, medium spatial resolution and long period. This proposal aims to apply forest change detection method in Hainan Jianfengling Forest Park using yearly Landsat time-series images. A simple detection method from the dense time series Landsat NDVI images will be used to reconstruct forest change history (afforestation and deforestation). The mapping result showed a large decrease occurred in the extent of closed forest from 1980s to 1990s. From the beginning of the 21st century, we found an increase in forest areas with the implementation of forestry measures such as the prohibition of cutting and sealing in our study area. Our findings provide an effective approach for quickly detecting forest changes in tropical original forest, especially for afforestation and deforestation, and a comprehensive analysis tool for forest resource protection.

Citation Download Citation

Quanjun Jiao, Xiao Zhang, and Qi Sun "Detecting of forest afforestation and deforestation in Hainan Jianfengling Forest Park (China) using yearly Landsat time-series images", Proc. SPIE 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 106111A (8 March 2018); https://doi.org/10.1117/12.2285023

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KEYWORDS

Earth observing sensors

Landsat

Vegetation

Near infrared

Remote sensing

Reflectivity

Forestry

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