Abstract
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.
Highlights
Forests provide ecological services that are crucial for the human well-being, such as biomass for energy, water storage, carbon storage and sequestration, nutrient cycling, and climate regulation [1,2]
In terms of remote sensing combined variables, S-1 & S-2 principal component analysis (PCA) and vegetation indices (VIs) PCA were found to be suitable for predicting taxonomic diversity (i.e., Simpson, Shannon-Wiener and richness indices), while S-1 & S-2 and S1 & S-2 & VIs PCA were the best for predicting heterogeneity indices (Table 3)
The introduced S-1 and S-2 workflow for forest plant diversity is cost-efficient and scalable
Summary
Forests provide ecological services that are crucial for the human well-being, such as biomass for energy, water storage, carbon storage and sequestration (below and aboveground), nutrient cycling, and climate regulation [1,2]. 2022, 14, 492 diversity in forests, as well as spatial changes in its distribution, are frequently required in the forestry operations and conservation of relevant sites [7,8] Such spatially explicit maps are vital for ecological function stability [5,9,10], exotic species invasion assessments [11], and prevention of plant diseases and insect pests [3,12]– in maintaining the stability of plant community composition [2,6,10,13]. Efforts designed to monitor plant diversity in forests have relied on field campaigns to calculate plant diversity indices of forest vegetation [14,15]. Maps or remotely sensed products could help to improve assessments of plant diversity
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