Mapping bamboo forest of China with the use of time series Landsat and Google Earth Engine

Authors: Chong Liu*, Jiangxi Normal University, Shuhua Qi, Jiangxi Normal University
Topics: Remote Sensing, China
Keywords: bamboo forest, mapping, Google Earth Engine, Landsat, China
Session Type: Paper
Day: 4/12/2018
Start / End Time: 5:20 PM / 7:00 PM
Room: Galerie 4, Marriott, 2nd Floor
Presentation File: No File Uploaded


Bamboos are woody grasses broadly distributed in the tropical, subtropical and mild temperate regions. Although these plants represent only 0.8% of the total forest area on earth, they play critical roles in the integrity of global ecosystem. Consequently, quantifying the extent of bamboo forest and documenting its spatial distribution are crucial for a better understanding of the resource management and ecosystem services both locally and globally. China is one of the richest countries in terms of bamboo forest coverage and diversity. The advent of optical remote sensing has revolutionized our ability of identifying bamboo forest distribution in a spatially explicit manner. However, reliable bamboo forest mapping still remains challenging due to the spectral similarity with other forest species and the influence of cloud occurrence. The use of complementary features derived from time series remote sensing data provides a possible solution to the above-mentioned problems. With the increasing amount of cost-free time series data from satellites such as Landsat and the availability of cloud computation resources like Google Earth Engine, there exists a great deal of potential in the time series based approaches for terrestrial environmental monitoring. This study for the first time generated a bamboo forest extent map of China at a spatial resolution of 30 m. In particular, we aimed to: 1) develop a feasible framework for large-scale bamboo forest mapping using time series Landsat and Google Earth Engine and 2) evaluate the spatial pattern of China’s bamboo forest and its relevance to topographical and climatic factors.

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