RANGES improves satellite‐based information and land cover assessments in southwest United States

Qi, Jiaguo; Marsett, Robin; Heilman, Philip; Biedenbender, Sharon; Moran, Susan; Goodrich, David C.; Weltz, Mark A.

doi:10.1029/2002eo000411

Cited by 124 publications

(58 citation statements)

References 2 publications

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“…Due to the issue of cloud cover, a set of images was selected representing only nine months of the year 2014 (January, February, April, May, Jun, July, August, September, October). In order to improve the predictive capability for toxic metals in soils and as we assumed that vegetation cover may provide important information for soil toxic metals' spatial modeling, the following spectral indices were derived from the satellite images: BCI, biophysical composition index [43]; EVI, enhanced vegetation index [44]; LSWI, land surface water index [45]; NDVI, normalized differential vegetation index [46]; SATVI, soil-adjusted total vegetation index [47,48]; SAVI, soil-adjusted vegetation index [49]; TVI, transformed vegetation index [50]; WDVI, weighted difference vegetation index [51,52]; and tasseled cap transformation including brightness, greenness and wetness [53]. The formulas used to derive these indices are shown in Table 2.…”

Section: Remote Sensing Images and Spectral Indicesmentioning

confidence: 99%

Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

Kheir

Adhikari

et al. 2016

Remote Sensing

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After decades of mining and industrialization in Qatar, it is important to estimate their impact on soil pollution with toxic metals. The study utilized 300 topsoil (0-30 cm) samples, multi-spectral images (Landsat 8), spectral indices and environmental variables to model and map the spatial distribution of arsenic (As), chromium (Cr), nickel (Ni), copper (Cu), lead (Pb) and zinc (Zn) in Qatari soils. The prediction model used condition-based rules generated in the Cubist tool. In terms of R 2 and the ratio of performance to interquartile distance (RPIQ), the models showed good predictive capabilities for all elements. Of all of the prediction results, Cu had the highest R 2 = 0.74, followed by As > Pb > Cr > Zn > Ni. This study found that all of the models only chose images from January and February as predictors, which indicates that images from these two months are important for soil toxic metals' monitoring in arid soils, due to the climate and the vegetation cover during this season. Topsoil maps of the six toxic metals were generated. The maps can be used to prioritize the choice of remediation measures and can be applied to other arid areas of similar environmental/socio-economic conditions and pollution causes.

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Section: Remote Sensing Images and Spectral Indicesmentioning

confidence: 99%

Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

Kheir

Adhikari

et al. 2016

Remote Sensing

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“…NDWI (Normalized Difference Water I) (C−NIR2)/(C + NIR2) [46] Texture GLCMh1 GLCM homogeneity sum of all directions from NIR1 and NIR2 [37] GLCMh2 [37] GLCMd1 GLCM dissimilarity sum of all directions from NIR1 and NIR2 [37] GLCMd2 [37] GLCMe1 GLCM entropy sum of all directions from NIR1 and NIR2 [37] GLCMe2 [37] The OBIA software eCognition v. 8.8 was employed for the extraction of the object-based features from both Landsat 8 and WV2 orthoimages shown in Tables 3 and 4 [37][38][39][40][41][42][43][44][45][46]. To this end, the chessboard segmentation algorithm included in eCognition was applied on a thematic layer composed of a previously digitized vector file with the 694 reference greenhouses.…”

Section: Tested Features Description Referencementioning

confidence: 99%

Object-Based Greenhouse Horticultural Crop Identification from Multi-Temporal Satellite Imagery: A Case Study in Almeria, Spain

et al. 2015

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Greenhouse detection and mapping via remote sensing is a complex task, which has already been addressed in numerous studies. In this research, the innovative goal relies on the identification of greenhouse horticultural crops that were growing under plastic coverings on 30 September 2013. To this end, object-based image analysis (OBIA) and a decision tree classifier (DT) were applied to a set consisting of eight Landsat 8 OLI images collected from May to November 2013. Moreover, a single WorldView-2 satellite image acquired on 30 September 2013, was also used as a data source. In this approach, basic spectral information, textural features and several vegetation indices (VIs) derived from Landsat 8 and WorldView-2 multi-temporal satellite data were computed on previously segmented image objects in order to identify four of the most popular autumn crops cultivated under greenhouse in Almería, Spain (i.e., tomato, pepper, cucumber and aubergine). The best classification accuracy (81.3% overall accuracy) was achieved by using the full set of Landsat 8 time series. These results were considered good in the case of tomato and pepper crops, being significantly worse for cucumber and aubergine. These OPEN ACCESSRemote Sens. 2015, 7 7379 results were hardly improved by adding the information of the WorldView-2 image. The most important information for correct classification of different crops under greenhouses was related to the greenhouse management practices and not the spectral properties of the crops themselves.

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“…where NDSVI is the Normalized Difference Senescent Vegetation Index [21]. In addition, the widely used Normalized Difference Vegetation Index (NDVI) was included:…”

Section: Reflectance Measurementsmentioning

confidence: 99%

“…Nevertheless numerous crop residue/soil tillage indices have been reported that use various combinations of the Landsat Thematic Mapper (TM) bands [16,[18][19][20][21][22]. These indices are based on relative differences in broad band reflectance for soils and crop residues.…”

Section: Introductionmentioning

confidence: 99%

Spectral Reflectance of Wheat Residue during Decomposition and Remotely Sensed Estimates of Residue Cover

Daughtry

Serbin²,

Reeves

et al. 2010

Remote Sensing

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Remotely sensed estimates of crop residue cover (fR) are required to assess the extent of conservation tillage over large areas; the impact of decay processes on estimates of residue cover is unknown. Changes in wheat straw composition and spectral reflectance were measured during the decay process and their impact on estimates of fR were assessed. Proportions of cellulose and hemicellulose declined, while lignin increased. Spectral features associated with cellulose diminished during decomposition. Narrow-band spectral residue indices robustly estimated fR, while broad-band indices were inconsistent. Advanced multi-spectral sensors or hyperspectral sensors are required to assess fR reliably over diverse agricultural landscapes.

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RANGES improves satellite‐based information and land cover assessments in southwest United States

Cited by 124 publications

References 2 publications

Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

Object-Based Greenhouse Horticultural Crop Identification from Multi-Temporal Satellite Imagery: A Case Study in Almeria, Spain

Spectral Reflectance of Wheat Residue during Decomposition and Remotely Sensed Estimates of Residue Cover

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