Dynamic Monitoring of Forest Land in Fuling District Based on Multi-Source Time Series Remote Sensing Images
Time series remote sensing images can be used to monitor the dynamic changes of forest lands. Due to consistent cloud cover and fog, a single sensor typically provides limited data for dynamic monitoring. This problem is solved by combining observations from multiple sensors to form a time series (a...
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doaj-4c6b2beea54643fd904f30d970ffed732020-11-25T00:50:23ZengMDPI AGISPRS International Journal of Geo-Information2220-99642019-01-01813610.3390/ijgi8010036ijgi8010036Dynamic Monitoring of Forest Land in Fuling District Based on Multi-Source Time Series Remote Sensing ImagesBingxin Bai0Yumin Tan1Dong Guo2Bo Xu3School of Transportation Science and Engineering, Beihang University, Beijing 100191, ChinaSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, ChinaBeijing Research Institute of Automation for Machinery Industry, Beijing 100120, ChinaDepartment of Geography & Environmental Studies, California State University, San Bernardino, CA 92407, USATime series remote sensing images can be used to monitor the dynamic changes of forest lands. Due to consistent cloud cover and fog, a single sensor typically provides limited data for dynamic monitoring. This problem is solved by combining observations from multiple sensors to form a time series (a satellite image time series). In this paper, the pixel-based multi-source remote sensing image fusion (MulTiFuse) method is applied to combine the Landsat time series and Huanjing-1 A/B (HJ-1 A/B) data in the Fuling district of Chongqing, China. The fusion results are further corrected and improved with spatial features. Dynamic monitoring and analysis of the study area are subsequently performed on the improved time series data using the combination of Mann-Kendall trend detection method and Theil Sen Slope analysis. The monitoring results show that a majority of the forest land (60.08%) has experienced strong growth during the 1999–2013 period. Accuracy assessment indicates that the dynamic monitoring using the fused image time series produces results with relatively high accuracies.http://www.mdpi.com/2220-9964/8/1/36time seriesimage fusiondynamic monitoringLandsatHJ-1 A/B |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Bingxin Bai Yumin Tan Dong Guo Bo Xu |
spellingShingle |
Bingxin Bai Yumin Tan Dong Guo Bo Xu Dynamic Monitoring of Forest Land in Fuling District Based on Multi-Source Time Series Remote Sensing Images ISPRS International Journal of Geo-Information time series image fusion dynamic monitoring Landsat HJ-1 A/B |
author_facet |
Bingxin Bai Yumin Tan Dong Guo Bo Xu |
author_sort |
Bingxin Bai |
title |
Dynamic Monitoring of Forest Land in Fuling District Based on Multi-Source Time Series Remote Sensing Images |
title_short |
Dynamic Monitoring of Forest Land in Fuling District Based on Multi-Source Time Series Remote Sensing Images |
title_full |
Dynamic Monitoring of Forest Land in Fuling District Based on Multi-Source Time Series Remote Sensing Images |
title_fullStr |
Dynamic Monitoring of Forest Land in Fuling District Based on Multi-Source Time Series Remote Sensing Images |
title_full_unstemmed |
Dynamic Monitoring of Forest Land in Fuling District Based on Multi-Source Time Series Remote Sensing Images |
title_sort |
dynamic monitoring of forest land in fuling district based on multi-source time series remote sensing images |
publisher |
MDPI AG |
series |
ISPRS International Journal of Geo-Information |
issn |
2220-9964 |
publishDate |
2019-01-01 |
description |
Time series remote sensing images can be used to monitor the dynamic changes of forest lands. Due to consistent cloud cover and fog, a single sensor typically provides limited data for dynamic monitoring. This problem is solved by combining observations from multiple sensors to form a time series (a satellite image time series). In this paper, the pixel-based multi-source remote sensing image fusion (MulTiFuse) method is applied to combine the Landsat time series and Huanjing-1 A/B (HJ-1 A/B) data in the Fuling district of Chongqing, China. The fusion results are further corrected and improved with spatial features. Dynamic monitoring and analysis of the study area are subsequently performed on the improved time series data using the combination of Mann-Kendall trend detection method and Theil Sen Slope analysis. The monitoring results show that a majority of the forest land (60.08%) has experienced strong growth during the 1999–2013 period. Accuracy assessment indicates that the dynamic monitoring using the fused image time series produces results with relatively high accuracies. |
topic |
time series image fusion dynamic monitoring Landsat HJ-1 A/B |
url |
http://www.mdpi.com/2220-9964/8/1/36 |
work_keys_str_mv |
AT bingxinbai dynamicmonitoringofforestlandinfulingdistrictbasedonmultisourcetimeseriesremotesensingimages AT yumintan dynamicmonitoringofforestlandinfulingdistrictbasedonmultisourcetimeseriesremotesensingimages AT dongguo dynamicmonitoringofforestlandinfulingdistrictbasedonmultisourcetimeseriesremotesensingimages AT boxu dynamicmonitoringofforestlandinfulingdistrictbasedonmultisourcetimeseriesremotesensingimages |
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