Next Article in Journal
High-Resolution Sea Surface Temperature Retrieval from Landsat 8 OLI/TIRS Data at Coastal Regions
Previous Article in Journal
Comparison and Validation of the Ionospheric Climatological Morphology of FY3C/GNOS with COSMIC during the Recent Low Solar Activity Period
Open AccessArticle

Tracking Reforestation in the Loess Plateau, China after the “Grain for Green” Project through Integrating PALSAR and Landsat Imagery

1
School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
2
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3
Satellite Environment Center, Ministry of Ecology and Environment, Beijing 100094, China
4
Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019-0390, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(22), 2685; https://doi.org/10.3390/rs11222685
Received: 30 September 2019 / Revised: 13 November 2019 / Accepted: 14 November 2019 / Published: 17 November 2019
(This article belongs to the Special Issue Mapping Forest Dynamics Using Multi-Source Remote Sensing)
An unprecedented reforestation process happened in the Loess Plateau, China due to the ecological restoration project ‘Grain for Green Project’, which has affected regional carbon and water cycles as well as brought climate feedbacks. Accurately mapping the area and spatial distribution of emerged forests in the Loess Plateau over time is essential for forest management but a very challenging task. Here we investigated the changes of forests in the Loess Plateau after the forest reconstruction project. First, we used a pixel and rule-based algorithm to identify and map the annual forests from 2007 to 2017 in the Loess Plateau by integrating 30 m Landsat data and 25 m resolution PALSAR data in this study. Then, we carried out the accuracy assessment and comparison with several existing forest products. The overall accuracy (OA) and Kappa coefficient of the resultant map, were about 91% and 0.77 in 2010, higher than those of the other forest products (FROM-GLC, GlobeLand30, GLCF-VCF, JAXA, and OU-FDL) with OA ranging from 83.57% to 87.96% and Kappa coefficients from 0.52 to 0.68. Based on the annual forest maps, we found forest area in the Loess Plateau has increased by around 15,000 km2 from 2007 to 2017. This study clearly demonstrates the advantages of data fusion between PALSAR and Landsat images for monitoring forest cover dynamics in the Loess Plateau, and the resultant forest maps with lower uncertainty would contribute to the regional forest management. View Full-Text
Keywords: forest change; PALSAR; Landsat; Loess Plateau; Grain for Green Project (GGP); spatiotemporal pattern forest change; PALSAR; Landsat; Loess Plateau; Grain for Green Project (GGP); spatiotemporal pattern
Show Figures

Graphical abstract

MDPI and ACS Style

Zhou, H.; Xu, F.; Dong, J.; Yang, Z.; Zhao, G.; Zhai, J.; Qin, Y.; Xiao, X. Tracking Reforestation in the Loess Plateau, China after the “Grain for Green” Project through Integrating PALSAR and Landsat Imagery. Remote Sens. 2019, 11, 2685.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop