000000014 001__ 14
000000014 005__ 20230824003249.0
000000014 0247_ $$a10.7936/1f9b-f396$$2DOI
000000014 037__ $$aRDM
000000014 041__ $$aeng
000000014 245__ $$aHigh resolution land use classification using combined spectral indexing from single scene remote imagery.
000000014 269__ $$a2019-02-13
000000014 270__ $$mfrachetti@wustl.edu$$pFrachetti, Michael D.
000000014 336__ $$aDataset
000000014 520__ $$aThe availability of high resolution, multispectral satellite imagery has transformed the potential to quantify changes in land use and land cover at increasingly smaller scales, thus shifting the focus of remote land cover reconstruction from broad, summarial characterizations to more nuanced, time-sensitive ecological snapshots. While overview characterizations continue to provide highly useful data, detailed local assessments of short term land cover change offer new opportunities to analyze localized ecological feedback between cultural systems and environmental transformations, for example in tropical jungles of Indonesia affected by environmental disasters. The complex vegetative mosaic of such environments can pose challenges to traditional reclassification approaches that rely heavily on specific vegetation indexes such as NDVI, since localized land cover changes can often go unnoticed within a particular spectral range and alternative indices can be hard to predict. Here we outline a methodology for discriminating “combined index pairs” that accurately distinguish and classify the distribution of nuanced land use types using high-resolution multispectral satellite imagery (tested against known test areas). Our model discriminates the most effective pair-wise combinations of multi-spectral reflectance indices and deploys these pairs to accurately classify land cover in a complex tropical jungle mosaic. Given the extensibility of our approach, the methodology presented here offers a novel technique for identifying an improved means of classifying single-scene images using commonly available spectral indices, creating a versatile tool that yields accurate classifications of nuanced environmental settings using minimal data inputs.
000000014 536__ $$oMcDonnell Academy Global Energy and Environment Partnership
000000014 536__ $$oGeoeye Foundation
000000014 540__ $$aCreative Commons Attribution (CC BY) 4.0 International$$uhttps://creativecommons.org/licenses/by/4.0/
000000014 650__ $$aEarth and related environmental sciences
000000014 6531_ $$aremote sensing
000000014 6531_ $$aspectral imagery
000000014 655__ $$aImage
000000014 7001_ $$aFrachetti, Michael D.$$1https://orcid.org/0000-0001-6906-4334$$uWashington University in St. Louis$$4https://ror.org/01yc7t268$$5ROR
000000014 7001_ $$aCoco, Emily$$1https://orcid.org/0000-0002-9200-8469$$uNew York University$$4https://ror.org/0190ak572$$5ROR
000000014 7001_ $$aNobels, Todd$$uAmerican Research Institute$$4https://ror.org/03gdsp770$$5ROR
000000014 8564_ $$9a3dcff30-f7e3-4d6e-b87a-f30a4698d68b$$s760941081$$uhttps://data.library.wustl.edu/record/14/files/doi1079361f9bf396_frachetti_dip.zip$$ePublic$$24da6ea5e0984ccf30f2e77b8a10c47ba$$01
000000014 904__ $$aec3307@nyu.edu
000000014 904__ $$atonobles@gmail.com
000000014 909CO $$ooai:data.library.wustl.edu:14$$pdataset
000000014 980__ $$aWashU Researcher Data