Logo image
Assessing the application of a geographic presence-only model for land suitability mapping
Journal article   Open access   Peer reviewed

Assessing the application of a geographic presence-only model for land suitability mapping

Benjamin W Heumann, Stephen J Walsh and Phillip M McDaniel
Ecological Informatics, Vol.6(5), pp.257-269
2011
pdf
PDF - Author's Accepted Version (Open Access)3.39 MBDownloadView
Accepted VersionPDF - Author Accepted Version (Open Access)CC BY-NC-ND V4.0 Open Access
url
https://doi.org/10.1016/j.ecoinf.2011.04.004View
Published Version

Abstract

Biological Sciences Information and Computing Sciences sence-only land suitability agriculture Thailand MaxEnt
Recent advances in ecological modeling have focused on novel methods for characterizing the environment that use presence-only data and machine-learning algorithms to predict the likelihood of species occurrence. These novel methods may have great potential for land suitability applications in the developing world where detailed land cover information is often unavailable or incomplete. This paper assesses the adaptation and application of the presence-only geographic species distribution model, MaxEnt, for agricultural crop suitability mapping in a rural Thailand where lowland paddy rice and upland field crops predominant. To assess this modeling approach, three independent crop presence datasets were used including a social-demographic survey of farm households, a remote sensing classification of land use/land cover, and ground control points, used for geodetic and thematic reference that vary in their geographic distribution and sample size. Disparate environmental data were integrated to characterize environmental settings across Nang Rong District, a region of approximately 1300 sq. km in size. Results indicate that the MaxEnt model is capable of modeling crop suitability for upland and lowland crops, including rice varieties, although model results varied between datasets due to the high sensitivity of the model to the distribution of observed crop locations in geographic and environmental space. Accuracy assessments indicate that model outcomes were influenced by the sample size and the distribution of sample points in geographic and environmental space. The need for further research into accuracy assessments of presence-only models lacking true absence data is discussed. We conclude that the MaxEnt model can provide good estimates of crop suitability, but many areas need to be carefully scrutinized including geographic distribution of input data and assessment methods to ensure realistic modeling results.

Details

Metrics

16 File views/ downloads
686 Record Views

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Web Of Science research areas
Ecology

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#13 Climate Action
#14 Life Below Water
#15 Life on Land

Source: InCites

Logo image