KOPRI Repository

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

Cited 1 time in wos
Cited 1 time in scopus
Title
Mapping Potential Plant Species Richness over Large Areas with Deep Learning, MODIS, and Species Distribution Models
Other Titles
인공지능, 위성영상, 종분포모형을 이용한 광역 미답지역 잠재적 식생 종 풍부도 맵핑 연구
Authors
Choe, Hyeyeong
Chi, Junhwa
Thorne, James H.
Subject
Environmental Sciences & EcologyGeologyRemote SensingImaging Science & Photographic Technology
Keywords
LAIMODISNDVIS-SDMsbiodiversitydata fusiondeep learningmultilayer perceptron (MLP)remote sensingspecies richness
Issue Date
2021-07
Citation
최혜영, Chi, Junhwa, James Thorne. 2021. "Mapping Potential Plant Species Richness over Large Areas with Deep Learning, MODIS, and Species Distribution Models". REMOTE SENSING, 13(13): 2490-2509.
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 obsta-cles 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 MODIS products and modeled potential spe-cies richness by stacking species distribution models (S-SDMs) to ask, “What are the spatial pat-terns 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 dif-ference vegetation index (NDVI) and leaf area index (LAI) from MODIS. The individually de-veloped 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.
URI
https://repository.kopri.re.kr/handle/201206/12992
DOI
http://dx.doi.org/10.3390/rs13132490
Type
Article
Station
해당사항없음
Indexed
SCIE
Appears in Collections  
2021-2021, Preliminary research on Arctic sea ice prediction using artificial intelligence for the extension of Arctic research (21-21) / Chi, Junhwa (PE21420)
Files in This Item

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse