Abstract
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.
Highlights
The forest is an important subsystem in the ecosystem that provides a variety of valuable timber, raw materials and other supplies necessary to human’s production and life and ecological balance
Because the interval of a single source time series is very long due to the consistent cloud and fog in this region, we propose a multi-source time series remote sensing image fusion method based on MulTiFuse
The Landsat image sequence is selected as the reference image sequence, and the Huanjing-1 B (HJ-1 B) image sequence is used as the image sequence to be fused
Summary
The forest is an important subsystem in the ecosystem that provides a variety of valuable timber, raw materials and other supplies necessary to human’s production and life and ecological balance. It is of great significance to use long-term remote sensing time series imagery to dynamically monitor the forest land resources on both sides of the reservoir. Long-term time series analysis based on remote sensing have been proven extremely useful in monitoring forest resources changes [1,2,3,4]. The Landsat satellites revisit the same area every 16 days, which makes the time interval of the image time series too long. In some areas, such as the tropics, Landsat satellite data may be contaminated by clouds and shadows These issues make the available image sequences sparse and reduce the accuracy of dynamic monitoring [6,7]
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