Understanding the Spatially Explicit Distribution of Regional Tree Species Using Multi-Seasonal Sentinel-1&amp;amp;2 Imagery within Google Earth Engine

Xie, Bin; Cao, Chunxiang; Xu, Min; Bashir, Barjeece; Chen, Yiyu; Duerler, Robert Shea; Wang, Kaimin

doi:10.21203/rs.3.rs-245409/v1

Cited by 9 publications

(1 citation statement)

References 61 publications

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“…The research process consists of three main steps. Firstly, we conducted preprocessing of Sentinel-2 images for the Kunming region and downloaded these images from the Google Earth Engine (GEE) platform [10][11][12]. Next, we selected pure forest stands from the Forest Resource Second-Class Survey data, covering over 65% dominance of target tree species, to serve as our sample data.…”

Section: Overall Workflowmentioning

confidence: 99%

Deep transfer learning for dominant tree species classification based on google earth engine Sentinel-2: a case study in Kunming City

Liu

2024

Fifth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2023)

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Section: Overall Workflowmentioning

confidence: 99%

Deep transfer learning for dominant tree species classification based on google earth engine Sentinel-2: a case study in Kunming City

Liu

2024

Fifth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2023)

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Evaluation of Correction Algorithms for Sentinel-2 Images Implemented in Google Earth Engine for Use in Land Cover Classification in Northern Spain

Teijido-Murias,

Barrio-Anta,

López-Sánchez

2024

Forests

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This study examined the effect of atmospheric, topographic, and Bidirectional Reflectance Distribution Function (BRDF) corrections of Sentinel-2 images implemented in Google Earth Engine (GEE) for use in land cover classification. The study was carried out in an area of complex orography in northern Spain and made use of the Spanish National Forest Inventory plots and other systematically located plots to cover non-forest classes. A total of 2991 photo-interpreted ground plots and 15 Sentinel-2 images, acquired in summer at a spatial resolution of 10–20 m per pixel, were used for this purpose. The overall goal was to determine the optimal level of image correction in GEE for subsequent use in time series analysis of images for accurate forest cover classification. Particular attention was given to the classification of cover by the major commercial forest species: Eucalyptus globulus, Eucalyptus nitens, Pinus pinaster, and Pinus radiata. The Second Simulation of the Satellite Signal in the Solar Spectrum (Py6S) algorithm, used for atmospheric correction, provided the best compromise between execution time and image size, in comparison with other algorithms such as Sentinel-2 Level 2A Processor (Sen2Cor) and Sensor Invariant Atmospheric Correction (SIAC). To correct the topographic effect, we tested the modified Sun-canopy-sensor topographic correction (SCS + C) algorithm with digital elevation models (DEMs) of three different spatial resolutions (90, 30, and 10 m per pixel). The combination of Py6S, the SCS + C algorithm and the high-spatial resolution DEM (10 m per pixel) yielded the greatest precision, which demonstrated the need to match the pixel size of the image and the spatial resolution of the DEM used for topographic correction. We used the Ross-Thick/Li-Sparse-Reciprocal BRDF to correct the variation in reflectivity captured by the sensor. The BRDF corrections did not significantly improve the accuracy of the land cover classification with the Sentinel-2 images acquired in summer; however, we retained this correction for subsequent time series analysis of the images, as we expected it to be of much greater importance in images with larger solar incidence angles. Our final proposed dataset, with image correction for atmospheric (Py6S), topographic (SCS + C), and BRDF (Ross-Thick/Li-Sparse-Reciprocal BRDF) effects and a DEM of spatial resolution 10 m per pixel, yielded better goodness-of-fit statistics than other datasets available in the GEE catalogue. The Sentinel-2 images currently available in GEE are therefore not the most accurate for constructing land cover classification maps in areas with complex orography, such as northern Spain.

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Improved Forest Canopy Closure Estimation Using Multispectral Satellite Imagery within Google Earth Engine

Xie

Cao

et al. 2022

Remote Sensing

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The large area estimation of forest canopy closure (FCC) using remotely sensed data is of high interest in monitoring forest changes and forest health, as well as in assessing forest ecological services. The accurate estimation of FCC over the regional or global scale is challenging due to the difficulty of sample acquisition and the slow processing efficiency of large amounts of remote sensing data. To address this issue, we developed a novel bounding envelope methodology based on vegetation indices (BEVIs) for determining vegetation and bare soil endmembers using the normalized differences vegetation index (NDVI), modified bare soil index (MBSI), and bare soil index (BSI) derived from Landsat 8 OLI and Sentinel-2 image within the Google Earth Engine (GEE) platform, then combined the NDVI with the dimidiate pixel model (DPM), one of the most commonly used spectral-based unmixing methods, to map the FCC distribution over an area of more than 90,000 km2. The key processing was the determination of the threshold parameter in BEVIs that characterizes the spectral boundary of vegetation and soil endmembers. The results demonstrated that when the threshold equals 0.1, the extraction accuracy of vegetation and bare soil endmembers is the highest with the threshold range given as (0, 0.3), and the estimated spatial distribution of FCC using both Landsat 8 and Sentinel-2 images were consistent, that is, the area with high canopy density was mainly distributed in the western mountainous region of Chifeng city. The verification was carried out using independent field plots. The proposed approach yielded reliable results when the Landsat 8 data were used (R2 = 0.6, RMSE = 0.13, and 1-rRMSE = 80%), and the accuracy was further improved using Sentinel-2 images with higher spatial resolution (R2 = 0.81, RMSE = 0.09, and 1-rRMSE = 86%). The findings demonstrate that the proposed method is portable among sensors with similar spectral wavebands, and can assist in mapping FCC at a regional scale while using multispectral satellite imagery.

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Understanding the Spatially Explicit Distribution of Regional Tree Species Using Multi-Seasonal Sentinel-1&2 Imagery within Google Earth Engine

Cited by 9 publications

References 61 publications

Deep transfer learning for dominant tree species classification based on google earth engine Sentinel-2: a case study in Kunming City

Deep transfer learning for dominant tree species classification based on google earth engine Sentinel-2: a case study in Kunming City

Evaluation of Correction Algorithms for Sentinel-2 Images Implemented in Google Earth Engine for Use in Land Cover Classification in Northern Spain

Improved Forest Canopy Closure Estimation Using Multispectral Satellite Imagery within Google Earth Engine

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