Quantifying Multi-Decadal Change of Planted Forest Cover Using Airborne LiDAR and Landsat Imagery

Xiaoyi Wang,Huabing Huang,Qinchuan Xin,Yanlei Chen,Jun Yang,Caixia Liu,Peng Gong,Gregory Biging

doi:10.3390/rs8010062

Abstract

Continuous monitoring of forest cover condition is key to understanding the carbon dynamics of forest ecosystems. This paper addresses how to integrate single-year airborne LiDAR and time-series Landsat imagery to derive forest cover change information. LiDAR data were used to extract forest cover at the sub-pixel level of Landsat for a single year, and the Landtrendr algorithm was applied to Landsat spectral data to explore the temporal information of forest cover change. Four different approaches were employed to model the relationship between forest cover and Landsat spectral data. The result shows incorporating the historic information using the temporal trajectory fitting process could infuse the model with better prediction power. Random forest modeling performs the best for quantitative forest cover estimation. Temporal trajectory fitting with random forest model shows the best agreement with validation data (R2 = 0.82 and RMSE = 5.19%). We applied our approach to Youyu county in Shanxi province of China, as part of the Three North Shelter Forest Program, to map multi-decadal forest cover dynamics. With the availability of global time-series Landsat imagery and affordable airborne LiDAR data, the approach we developed has the potential to derive large-scale forest cover dynamics.

Highlights

Terrestrial forest ecosystems play a key role in global carbon and hydrologic cycling, and influence the climate system through biogeochemical processes [1,2]
Deriving historical and up-to-date forest cover information is important for forest management and monitoring
We developed a new approach to map time-series sub-pixel forest cover with Landsat and LiDAR data, and we further evaluated this method for deriving change information in forest cover in the Northeastern China county of Youyu

Summary

Introduction

Terrestrial forest ecosystems play a key role in global carbon and hydrologic cycling, and influence the climate system through biogeochemical processes [1,2]. Continuous monitoring of forest cover is essential for assessing forest growth condition and exploring effective management strategies. Earth observing satellites, such as the Landsat series, provide frequent and consistent large-scale observations, which allow us to monitor land surface changes and supporting climate change studies [4]. Studies on change detection of forest cover using satellite data have largely been based. To derive trend information of forest cover change while minimizing inter-annual noise introduced by phenological differences, atmospheric interference, solar angle variation and imperfection in geometric registration and radiometric calibration, recent studies tried to detect forest cover changes using qualitative time-series analysis [7,8]. One key challenge is that references of forest cover is often missing when using optical satellite sensor alone in large-scale studies [6]

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Conclusion