Mapping Vegetation and Land Use Types in Fanjingshan National Nature Reserve Using Google Earth Engine

Abstract

Fanjingshan National Nature Reserve (FNNR) is a biodiversity hotspot in China that is part of a larger, multi-use landscape where farming, grazing, tourism, and other human activities occur. The steep terrain and persistent cloud cover pose challenges to robust vegetation and land use mapping. Our objective is to develop satellite image classification techniques that can reliably map forest cover and land use while minimizing the cloud and terrain issues, and provide the basis for long-term monitoring. Multi-seasonal Landsat image composites and elevation ancillary layers effectively minimize the persistent cloud cover and terrain issues. Spectral vegetation index (SVI) products and shade/illumination normalization approaches yield significantly higher mapping accuracies, compared to non-normalized spectral bands. Advanced machine learning image classification routines are implemented through the cloud-based Google Earth Engine platform. Optimal classifier parameters (e.g., number of trees and number of features for random forest classifiers) were achieved by using tuning techniques. Accuracy assessment results indicate consistent and effective overall classification (i.e., above 70% mapping accuracies) can be achieved using multi-temporal SVI composites with simple illumination normalization and elevation ancillary data, despite the fact limited training and reference data are available. This efficient and open-access image analysis workflow provides a reliable methodology to remotely monitor forest cover and land use in FNNR and other mountainous forested, cloud prevalent areas.

Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns

“…soils and vegetation components from satellite imagery. The authors in [15] used machine learning classification in order to map vegetation and land use types. As seen from the abovementioned literature, a lot of work has been done with machine learning to extract vegetation information.…”

Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

“…For more information about the Google Earth Engine, please refer to [36]. In the context of the big data era, the Google Earth Engine cloud platform has powerful data storage and management capabilities and data processing capabilities, providing a technical means for the wide spatial and temporal scales of remote sensing mapping studies [35,[37][38][39]. For example, Hansen et al [40] studied the state of global forest change, at a spatial resolution of 30 m, from 2000 to 2012, by using Google Earth Engine technology and Landsat images.…”

Determination of Vegetation Thresholds for Assessing Land Use and Land Use Changes in Cambodia using the Google Earth Engine Cloud-Computing Platform

“…where the coefficients of the EVI equation are L = 1 (canopy background adjustment factor); C1 = 6 and C2 = 7.5 (aerosol correction factors); and G = 2.5 (gain factor) [23,27,63,90]. NIR represents the near-infrared band (TM band 4 and OLI band 5); RED represents TM band 3 and OLI band 4; BLUE represents TM band 1 and OLI band 2 [85,87].…”

“…In recent years, GEE has been widely used for global-scale applications such as characterizing global forest cover change; forest expansion, loss, and gain from 2000 using large collections of Landsat scenes [5]; and crop yield estimation [21,22]. Other studies have also confirmed the ease of integrating various sources of temporal satellite imagery data and automating image classification routines for vegetation and land cover mapping using the GEE [17,[21][22][23][24][25][26].…”

“…Phenology-based threshold methods have increasingly gained public and scientific attention in geospatial science. The Phenology-based classification has shown a promising level of accuracy for mapping vegetation, crops, and land cover using moderate to high spatial resolution satellite data [24,27,[54][55][56] with aid of GEE cloud computing technology [23,53,[57][58][59][60][61]. There has been some research effort to study surface phenology of vegetation, rubber plantation, and cropland mapping in tropical regions [62][63][64].…”