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Im, Jungho
Intelligent Remote sensing and geospatial Information Science (IRIS) Lab
Research Interests
  • Remote sensing, Geospatial modeling, Disaster monitoring and management, Climate change


Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions

DC Field Value Language Xu, Jin ko Quackenbush, Lindi J. ko Volk, Timothy A. ko Im, Jungho ko 2020-11-19T08:45:15Z - 2020-11-12 ko 2020-09 ko
dc.identifier.citation REMOTE SENSING, v.12, no.18, pp.2934 ko
dc.identifier.issn 2072-4292 ko
dc.identifier.uri -
dc.description.abstract Leaf area index (LAI) is an important vegetation leaf structure parameter in forest and agricultural ecosystems. Remote sensing techniques can provide an effective alternative to field-based observation of LAI. Differences in canopy structure result in different sensor types (active or passive), platforms (terrestrial, airborne, or satellite), and models being appropriate for the LAI estimation of forest and agricultural systems. This study reviews the application of remote sensing-based approaches across different system configurations (passive, active, and multisource sensors on different collection platforms) that are used to estimate forest and crop LAI and explores uncertainty analysis in LAI estimation. A comparison of the difference in LAI estimation for forest and agricultural applications given the different structure of these ecosystems is presented, particularly as this relates to spatial scale. The ease of use of empirical models supports these as the preferred choice for forest and crop LAI estimation. However, performance variation among different empirical models for forest and crop LAI estimation limits the broad application of specific models. The development of models that facilitate the strategic incorporation of local physiology and biochemistry parameters for specific forests and crop growth stages from various temperature zones could improve the accuracy of LAI estimation models and help develop models that can be applied more broadly. In terms of scale issues, both spectral and spatial scales impact the estimation of LAI. Exploration of the quantitative relationship between scales of data from different sensors could help forest and crop managers more appropriately and effectively apply different data sources. Uncertainty coming from various sources results in reduced accuracy in estimating LAI. While Bayesian approaches have proven effective to quantify LAI estimation uncertainty based on the uncertainty of model inputs, there is still a need to quantify uncertainty from remote sensing data source, ground measurements and related environmental factors to mitigate the impacts of model uncertainty and improve LAI estimation. ko
dc.language 영어 ko
dc.publisher MDPI ko
dc.title Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions ko
dc.type ARTICLE ko
dc.identifier.scopusid 2-s2.0-85091877000 ko
dc.identifier.wosid 000581455500001 ko
dc.type.rims ART ko
dc.identifier.doi 10.3390/rs12182934 ko
dc.identifier.url ko
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