Very Rapid Forest Cover Change in Sichuan Province, China: 40 Years of Change Using Images From Declassified Spy Satellites and Landsat

Forests have significant impacts on the global carbon cycle, hydrological processes, and biodiversity. Driven by socioeconomic developments, forests experienced drastic changes since the mid-20th century in China. Although declassified spy satellite and other Earth observation satellite data offer r...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Dan-Xia Song, Chengquan Huang, Tao He, Min Feng, Ainong Li, Sike Li, Yong Pang, Hao Wu, Abdul Rashid Mohamed Shariff, John Townshend
Formato: article
Lenguaje:EN
Publicado: IEEE 2021
Materias:
Acceso en línea:https://doaj.org/article/b24b552558394044a716cec59f340840
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
id oai:doaj.org-article:b24b552558394044a716cec59f340840
record_format dspace
spelling oai:doaj.org-article:b24b552558394044a716cec59f3408402021-11-18T00:00:24ZVery Rapid Forest Cover Change in Sichuan Province, China: 40 Years of Change Using Images From Declassified Spy Satellites and Landsat2151-153510.1109/JSTARS.2021.3121260https://doaj.org/article/b24b552558394044a716cec59f3408402021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9580698/https://doaj.org/toc/2151-1535Forests have significant impacts on the global carbon cycle, hydrological processes, and biodiversity. Driven by socioeconomic developments, forests experienced drastic changes since the mid-20th century in China. Although declassified spy satellite and other Earth observation satellite data offer remote sensing technologies for mapping these long-term changes, challenges remain unsolved for applications of large volumes of historical data. This study uses long-term satellite observations, including declassified satellite data in the 1960s and Landsat products since the 1970s to monitor the decadal changes. A semi-automated method was developed for the rapid registration of declassified images with reference to Landsat data. The method was applied to quantify the forest cover (FC) in Sichuan Province (excluding Chongqing), China. Combined with a Landsat-based FC change product, it revealed that the FC in Sichuan declined rapidly by 38% from the 1960s to 2005. The FC was estimated to be 45.19 ± 1.62% in the 1960s and 38.98 ± 2.06% in 1975, but it rapidly decreased to 28.91 ± 2.07% in 1990 and 27.87 ± 2.14% by 2005. Supplemented with the official statistics, the FC in Sichuan was reported to increase to 38.03% by 2018. Although differences between the remote sensing-based estimates and the statistics were observed, they highlight the challenges in reconstructing historical land use changes for carbon and other studies. The drastic loss of forests before 1990 and the stabilizing afterward reflects the changes in forest policies, which transitioned from serving timber products to forest conservations.Dan-Xia SongChengquan HuangTao HeMin FengAinong LiSike LiYong PangHao WuAbdul Rashid Mohamed ShariffJohn TownshendIEEEarticleDeclassified spy satellite (DSS)forest cover change (FCC)image registrationlandsatOcean engineeringTC1501-1800Geophysics. Cosmic physicsQC801-809ENIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 10964-10976 (2021)
institution DOAJ
collection DOAJ
language EN
topic Declassified spy satellite (DSS)
forest cover change (FCC)
image registration
landsat
Ocean engineering
TC1501-1800
Geophysics. Cosmic physics
QC801-809
spellingShingle Declassified spy satellite (DSS)
forest cover change (FCC)
image registration
landsat
Ocean engineering
TC1501-1800
Geophysics. Cosmic physics
QC801-809
Dan-Xia Song
Chengquan Huang
Tao He
Min Feng
Ainong Li
Sike Li
Yong Pang
Hao Wu
Abdul Rashid Mohamed Shariff
John Townshend
Very Rapid Forest Cover Change in Sichuan Province, China: 40 Years of Change Using Images From Declassified Spy Satellites and Landsat
description Forests have significant impacts on the global carbon cycle, hydrological processes, and biodiversity. Driven by socioeconomic developments, forests experienced drastic changes since the mid-20th century in China. Although declassified spy satellite and other Earth observation satellite data offer remote sensing technologies for mapping these long-term changes, challenges remain unsolved for applications of large volumes of historical data. This study uses long-term satellite observations, including declassified satellite data in the 1960s and Landsat products since the 1970s to monitor the decadal changes. A semi-automated method was developed for the rapid registration of declassified images with reference to Landsat data. The method was applied to quantify the forest cover (FC) in Sichuan Province (excluding Chongqing), China. Combined with a Landsat-based FC change product, it revealed that the FC in Sichuan declined rapidly by 38% from the 1960s to 2005. The FC was estimated to be 45.19 ± 1.62% in the 1960s and 38.98 ± 2.06% in 1975, but it rapidly decreased to 28.91 ± 2.07% in 1990 and 27.87 ± 2.14% by 2005. Supplemented with the official statistics, the FC in Sichuan was reported to increase to 38.03% by 2018. Although differences between the remote sensing-based estimates and the statistics were observed, they highlight the challenges in reconstructing historical land use changes for carbon and other studies. The drastic loss of forests before 1990 and the stabilizing afterward reflects the changes in forest policies, which transitioned from serving timber products to forest conservations.
format article
author Dan-Xia Song
Chengquan Huang
Tao He
Min Feng
Ainong Li
Sike Li
Yong Pang
Hao Wu
Abdul Rashid Mohamed Shariff
John Townshend
author_facet Dan-Xia Song
Chengquan Huang
Tao He
Min Feng
Ainong Li
Sike Li
Yong Pang
Hao Wu
Abdul Rashid Mohamed Shariff
John Townshend
author_sort Dan-Xia Song
title Very Rapid Forest Cover Change in Sichuan Province, China: 40 Years of Change Using Images From Declassified Spy Satellites and Landsat
title_short Very Rapid Forest Cover Change in Sichuan Province, China: 40 Years of Change Using Images From Declassified Spy Satellites and Landsat
title_full Very Rapid Forest Cover Change in Sichuan Province, China: 40 Years of Change Using Images From Declassified Spy Satellites and Landsat
title_fullStr Very Rapid Forest Cover Change in Sichuan Province, China: 40 Years of Change Using Images From Declassified Spy Satellites and Landsat
title_full_unstemmed Very Rapid Forest Cover Change in Sichuan Province, China: 40 Years of Change Using Images From Declassified Spy Satellites and Landsat
title_sort very rapid forest cover change in sichuan province, china: 40 years of change using images from declassified spy satellites and landsat
publisher IEEE
publishDate 2021
url https://doaj.org/article/b24b552558394044a716cec59f340840
work_keys_str_mv AT danxiasong veryrapidforestcoverchangeinsichuanprovincechina40yearsofchangeusingimagesfromdeclassifiedspysatellitesandlandsat
AT chengquanhuang veryrapidforestcoverchangeinsichuanprovincechina40yearsofchangeusingimagesfromdeclassifiedspysatellitesandlandsat
AT taohe veryrapidforestcoverchangeinsichuanprovincechina40yearsofchangeusingimagesfromdeclassifiedspysatellitesandlandsat
AT minfeng veryrapidforestcoverchangeinsichuanprovincechina40yearsofchangeusingimagesfromdeclassifiedspysatellitesandlandsat
AT ainongli veryrapidforestcoverchangeinsichuanprovincechina40yearsofchangeusingimagesfromdeclassifiedspysatellitesandlandsat
AT sikeli veryrapidforestcoverchangeinsichuanprovincechina40yearsofchangeusingimagesfromdeclassifiedspysatellitesandlandsat
AT yongpang veryrapidforestcoverchangeinsichuanprovincechina40yearsofchangeusingimagesfromdeclassifiedspysatellitesandlandsat
AT haowu veryrapidforestcoverchangeinsichuanprovincechina40yearsofchangeusingimagesfromdeclassifiedspysatellitesandlandsat
AT abdulrashidmohamedshariff veryrapidforestcoverchangeinsichuanprovincechina40yearsofchangeusingimagesfromdeclassifiedspysatellitesandlandsat
AT johntownshend veryrapidforestcoverchangeinsichuanprovincechina40yearsofchangeusingimagesfromdeclassifiedspysatellitesandlandsat
_version_ 1718425245404102656