Mapping Potential Plant Species Richness over Large Areas with Deep Learning, MODIS, and Species Distribution Models

Abstract

The spatial patterns of species richness can be used as indicators for conservation and restoration, but data problems, including the lack of species surveys and geographical data gaps, are obstacles to mapping species richness across large areas. Lack of species data can be overcome with remote sensing because it covers extended geographic areas and generates recurring data. We developed a Deep Learning (DL) framework using Moderate Resolution Imaging Spectroradiometer (MODIS) products and modeled potential species richness by stacking species distribution models (S-SDMs) to ask, “What are the spatial patterns of potential plant species richness across the Korean Peninsula, including inaccessible North Korea, where survey data are limited?” First, we estimated plant species richness in South Korea by combining the probability-based SDM results of 1574 species and used independent plant surveys to validate our potential species richness maps. Next, DL-based species richness models were fitted to the species richness results in South Korea, and a time-series of the normalized difference vegetation index (NDVI) and leaf area index (LAI) from MODIS. The individually developed models from South Korea were statistically tested using datasets that were not used in model training and obtained high accuracy outcomes (0.98, Pearson correlation). Finally, the proposed models were combined to estimate the richness patterns across the Korean Peninsula at a higher spatial resolution than the species survey data. From the statistical feature importance tests overall, growing season NDVI-related features were more important than LAI features for quantifying biodiversity from remote sensing time-series data.

Forest tree species distribution for Europe 2000–2020: mapping potential and realized distributions using spatiotemporal machine learning

“…As any ML-derived product, our predictions would benefit from having more and better quality data on tree species, in particular those that come from NFI plots: it is now crucial to have such data freely available to monitor processes such as species compositional changes, niche shifts, forest regrowth and degradation, as recently stated by Nabuurs et al (2022) . Exploring more sophisticated and different ML algorithms such as Deep Learning (DL) techniques ( Lakshminarayanan, Pritzel & Blundell, 2016 ) for our ensemble framework is also another area of improvement given the wide variety of applications these methods possess and the results obtained in comparison with other conventional ML algorithms ( Choe, Chi & Thorne, 2021 ; Deneu et al, 2021 ; Anand et al, 2021 ).…”

Forest tree species distribution for Europe 2000-2020: mapping potential and realized distributions using spatiotemporal Machine Learning

“…As any ML-derived product, our predictions would benefit from having more and better quality data on tree species, in particular those that come from NFI plots: it is now crucial to have such data freely available to monitor processes such as species compositional changes, niche shifts, forest regrowth and degradation, as recently stated by Nabuurs et al (2022). Exploring more sophisticated and different ML algorithms such as Deep Learning (DL) techniques (Lakshminarayanan et al, 2016) to our ensemble framework is also another area of improvement given the wide variety of applications these methods possess and the results obtained in comparison with other conventional ML algorithms (Choe et al, 2021;Deneu et al, 2021;Anand et al, 2021).…”

WetCH4: A Machine Learning-based Upscaling of Methane Fluxes of Northern Wetlands during 2016–2022

“…Land surface reflectance was utilized to map key information to emergent vegetation, vegetation composition, and inundation dynamics (Alonso et al, 2020;Murray-Hudson et al, 2015). Surface reflectance contains information about the water-vegetation complex that affects the production and transport of CH4 to the atmosphere (Choe et al, 2021). Thus, we included MODIS NBAR (MCD43A4v061) products as predictor variables to represent the vegetation layer in the grid-level model in order to enhance our model predictive performance in vegetated wetlands.…”