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Article

Mapping Plant Diversity Based on Combined SENTINEL-1/2 Data—Opportunities for Subtropical Mountainous Forests

1
Key Laboratory for Environment and Disaster Monitoring and Evaluation, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Hubei Provincial Academy of Eco-Environmental Sciences, Wuhan 430072, China
*
Author to whom correspondence should be addressed.
Academic Editors: Francesco Petruzzellis, Enrico Tordoni, Daniele Da Re, Giovanni Bacaro and Duccio Rocchini
Remote Sens. 2022, 14(3), 492; https://doi.org/10.3390/rs14030492
Received: 20 November 2021 / Revised: 13 January 2022 / Accepted: 18 January 2022 / Published: 20 January 2022
(This article belongs to the Special Issue Remote Sensing of Ecosystem Diversity)
Plant diversity is an important parameter in maintaining forest ecosystem services, functions and stability. Timely and accurate monitoring and evaluation of large-area wall-to-wall maps on plant diversity and its spatial heterogeneity are crucial for the conservation and management of forest resources. However, traditional botanical field surveys designed to estimate plant diversity are usually limited in their spatiotemporal resolutions. Using Sentinel-1 (S-1) and Sentinel-2 (S-2) data at high spatiotemporal scales, combined with and referenced to botanical field surveys, may be the best choice to provide accurate plant diversity distribution information over a large area. In this paper, we predicted and mapped plant diversity in a subtropical forest using 24 months of freely and openly available S-1 and S-2 images (10 m × 10 m) data over a large study area (15,290 km2). A total of 448 quadrats (10 m × 10 m) of forestry field surveys were captured in a subtropical evergreen-deciduous broad-leaved mixed forest to validate a machine learning algorithm. The objective was to link the fine Sentinel spectral and radar data to several ground-truthing plant diversity indices in the forests. The results showed that: (1) The Simpson and Shannon-Wiener diversity indices were the best predicted indices using random forest regression, with ȓ2 of around 0.65; (2) The use of S-1 radar data can enhance the accuracy of the predicted heterogeneity indices in the forests by approximately 0.2; (3) As for the mapping of Simpson and Shannon-Wiener, the overall accuracy was 67.4% and 64.2% respectively, while the texture diversity’s overall accuracy was merely 56.8%; (4) From the evaluation and prediction map information, the Simpson, Shannon-Wiener and texture diversity values (and its confidence interval values) indicate spatial heterogeneity in pixel level. The large-area forest plant diversity indices maps add spatially explicit information to the ground-truthing data. Based on the results, we conclude that using the time-series of S-1 and S-2 radar and spectral characteristics, when coupled with limited ground-truthing data, can provide reasonable assessments of plant spatial heterogeneity and diversity across wide areas. It could also help promote forest ecosystem and resource conservation activities in the forestry sector. View Full-Text
Keywords: sentinel-1 and -2; satellite imagery time-series; random forest; subtropical evergreen-deciduous broad-leaved mixed forest; plant diversity sentinel-1 and -2; satellite imagery time-series; random forest; subtropical evergreen-deciduous broad-leaved mixed forest; plant diversity
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MDPI and ACS Style

Yang, Q.; Wang, L.; Huang, J.; Lu, L.; Li, Y.; Du, Y.; Ling, F. Mapping Plant Diversity Based on Combined SENTINEL-1/2 Data—Opportunities for Subtropical Mountainous Forests. Remote Sens. 2022, 14, 492. https://doi.org/10.3390/rs14030492

AMA Style

Yang Q, Wang L, Huang J, Lu L, Li Y, Du Y, Ling F. Mapping Plant Diversity Based on Combined SENTINEL-1/2 Data—Opportunities for Subtropical Mountainous Forests. Remote Sensing. 2022; 14(3):492. https://doi.org/10.3390/rs14030492

Chicago/Turabian Style

Yang, Qichi, Lihui Wang, Jinliang Huang, Lijie Lu, Yang Li, Yun Du, and Feng Ling. 2022. "Mapping Plant Diversity Based on Combined SENTINEL-1/2 Data—Opportunities for Subtropical Mountainous Forests" Remote Sensing 14, no. 3: 492. https://doi.org/10.3390/rs14030492

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