Abstract
As the largest among terrestrial ecosystems, forests are vital to maintaining ecosystem services and regulating regional climate. The area and spatial distribution of trees in densely forested areas have been focused on in the past few decades, while sparse forests in agricultural zones, so-called agroforests or trees outside forests (TOF), have usually been ignored or missed in existing forest mapping efforts, despite their important role in regulating agricultural ecosystems. We combined Landsat and PALSAR data to map forests in a typical agricultural zone in the North China Plain. The resultant map, based on PALSAR and Landsat (PL) data, was also compared with five existing medium resolution (30–100 m) forest maps from PALSAR (JAXA forest map) and Landsat: NLCD-China, GlobeLand30, ChinaCover, and FROM-GLC. The results show that the PL-based forest map has the highest accuracy (overall accuracy of 95 ± 1% with a 95% confidence interval, and Kappa coefficient of 0.86) compared to those forest maps based on single Landsat or PALSAR data in the North China Plain (overall accuracy ranging from 85 ± 2% to 92 ± 1%). All forest maps revealed higher accuracy in densely forested mountainous areas, while the PL-based and JAXA forest maps showed higher accuracy in the plain, as the higher omission errors existed in only the Landsat-based forest maps. Moreover, we found that the PL-based forest map can capture more patched forest information in low forest density areas. This means that the radar data have advantages in capturing forests in the typical agricultural zones, which tend to be missing in published Landsat-based only forest maps. Given the significance of agroforests in regulating ecosystem services of the agricultural ecosystem and improving carbon stock estimation, this study implies that the integration of PALSAR and Landsat data can provide promising agroforest estimates in future forest inventory efforts, targeting a comprehensive understanding of ecosystem services of agroforests and a more accurate carbon budget inventory.
Highlights
Forests play an important role in maintaining ecosystem services [1], global climate change [2], and water conservation [3]
While many studies have focused on the application of Landsat data and some studies have applied advanced land observation satellite (ALOS) phased array type L-band synthetic aperture radar (PALSAR) images or the combination of SAR and optical data, very limited efforts have been made to integrate both data types to enhance small-area forest inventory in typical agricultural regions where croplands are dominant, and most of the trees are planted along the highways, croplands or surrounding villages [24]
We generated a PALSAR and Landsat (PL)-based forest map with 30-m spatial resolution based on a decision tree approach, through integrating 25-m PALSAR data and 30-m Landsat TM/ETM+ images in the North China Plain (NCP) from circa 2010
Summary
Forests play an important role in maintaining ecosystem services [1], global climate change [2], and water conservation [3]. Fine resolution products are expected to capture more information in sparsely forested areas such as typical agricultural zones These optical remote sensing data, as the major data source for forest mapping, can promote forest mapping efforts with high temporal and spatial resolutions. While many studies have focused on the application of Landsat data and some studies have applied advanced land observation satellite (ALOS) phased array type L-band synthetic aperture radar (PALSAR) images or the combination of SAR and optical data, very limited efforts have been made to integrate both data types to enhance small-area forest inventory in typical agricultural regions where croplands are dominant, and most of the trees are planted along the highways, croplands or surrounding villages [24].
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