Multispectral and Texture Feature Application in Image-Object Analysis of Summer Vegetation in Eastern Tajikistan Pamirs

Salas, Eric Ariel L.; Boykin, Kenneth G.; Valdez, Raúl

doi:10.3390/rs8010078

Cited by 39 publications

(28 citation statements)

References 74 publications

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“…Wallis [31] Kelsey et al [53] Schumacher et al [38] Ouma et al [54] Salas et al [33] Zhao et al [21] Variance…”

Section: Modelling Above-ground Grass Biomassmentioning

confidence: 99%

“…The majority of the studies that utilised texture metrics were focused on forest above-ground biomass [6,10,[30][31][32][33]. In addition, most of these studies utilised the moderate resolution Landsat data, which does not capture the minute variations that could be induced by different grass treatments in a grassland landscape that is characterised by high spatial heterogeneity [1].…”

Section: Introductionmentioning

confidence: 99%

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Estimating Biomass of Native Grass Grown under Complex Management Treatments Using WorldView-3 Spectral Derivatives

et al. 2017

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Abstract:The ability of texture models and red-edge to facilitate the detection of subtle structural vegetation traits could aid in discriminating and mapping grass quantity, a challenge that has been longstanding in the management of grasslands in southern Africa. Subsequently, this work sought to explore the robustness of integrating texture metrics and red-edge in predicting the above-ground biomass of grass growing under different levels of mowing and burning in grassland management treatments. Based on the sparse partial least squares regression algorithm, the results of this study showed that red-edge vegetation indices improved above-ground grass biomass from a root mean square error of perdition (RMSEP) of 0.83 kg/m 2 to an RMSEP of 0.55 kg/m 2 . Texture models further improved the accuracy of grass biomass estimation to an RMSEP of 0.35 kg/m 2 . The combination of texture models and red-edge derivatives (red-edge-derived vegetation indices) resulted in an optimal prediction accuracy of RMSEP 0.2 kg/m 2 across all grassland management treatments. These results illustrate the prospect of combining texture metrics with the red-edge in predicting grass biomass across complex grassland management treatments. This offers the detailed spatial information required for grassland policy-making and sustainable grassland management in data-scarce regions such as southern Africa.

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“…Wallis [31] Kelsey et al [53] Schumacher et al [38] Ouma et al [54] Salas et al [33] Zhao et al [21] Variance…”

Section: Modelling Above-ground Grass Biomassmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating Biomass of Native Grass Grown under Complex Management Treatments Using WorldView-3 Spectral Derivatives

et al. 2017

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“…In addition to the object-based features containing spectral information (mean and standard deviation values of all pixels within an object) and several spectral and vegetation indices, the capabilities of the spectral metric Moment Distance Index (MDI) [37,44,45] was explored in this study as one of the predictor variables for greenhouse mapping. To the best knowledge of the authors, it is the first time that this index is tested in plastic covering mapping.…”

Section: Features Used To Carry Out Object-based Classificationmentioning

confidence: 99%

“…Finally the MDI can be computed as shown in Equation (3). More details about MDI formulation can be found in Salas et al [45].…”

Section: Features Used To Carry Out Object-based Classificationmentioning

confidence: 99%

Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series

et al. 2016

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Greenhouse mapping through remote sensing has received extensive attention over the last decades. In this article, the innovative goal relies on mapping greenhouses through the combined use of very high resolution satellite data (WorldView-2) and Landsat 8 Operational Land Imager (OLI) time series within a context of an object-based image analysis (OBIA) and decision tree classification. Thus, WorldView-2 was mainly used to segment the study area focusing on individual greenhouses. Basic spectral information, spectral and vegetation indices, textural features, seasonal statistics and a spectral metric (Moment Distance Index, MDI) derived from Landsat 8 time series and/or WorldView-2 imagery were computed on previously segmented image objects. In order to test its temporal stability, the same approach was applied for two different years, 2014 and 2015. In both years, MDI was pointed out as the most important feature to detect greenhouses. Moreover, the threshold value of this spectral metric turned to be extremely stable for both Landsat 8 and WorldView-2 imagery. A simple decision tree always using the same threshold values for features from Landsat 8 time series and WorldView-2 was finally proposed. Overall accuracies of 93.0% and 93.3% and kappa coefficients of 0.856 and 0.861 were attained for 2014 and 2015 datasets, respectively.

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“…The Kappa coefficients of those dates were 0.94, 0.96, and 0.96, respectively. The most important variable of Landsat bands in the RF model that came out of predictors were band 5 (NIR, 0.85-0.88 nm), band 4 (Red, 640-670 nm), and band 3 (Green, 0.53-0.59), while DEM ranked the top four [52]. Note: P * = Producers accuracy; U * = Users accuracy.…”

Section: Quantifying Landscape Patternmentioning

confidence: 99%

Multi-Criteria Assessment of Land Cover Dynamic Changes in Halgurd Sakran National Park (HSNP), Kurdistan Region of Iraq, Using Remote Sensing and GIS

Hamad

Balzter

Kolo

2017

Land

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Halgurd Sakran National Park (HSNP) is Iraq's first designated national park, located in the Kurdistan Region, which has suffered multiple armed conflicts over the past decades. This study assesses how vegetation dynamics have affected the landscape structure and composition of the core zone of the park over the last 31 years. Spatio-temporal changes in land cover were mapped for three points in time using remote sensing, geographic information systems (GIS), and landscape metrics. Land cover changes were mapped using random forest classifications of satellite images from Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 LDCM acquired in 1984, 1998, and 2015. Five landscape pattern metrics were analysed at class and landscape levels in order to quantify landscape patterns arising from land use and land cover (LULC) change in HSNP using FRAGSTATS 4.2. These landscape pattern metrics were patch metrics, area metrics, shape metrics interspersion/juxtaposition and contagion metrics and diversity metrics. Significant changes in cultivated areas after 1991 were observed, which indicate the role of anthropogenic activities in land cover change. Areas of bare surface and forest lands declined and became more fragmented in 1984 and 1998 while, at the same time, cultivated areas increased, with a continuing fragmentation of pasture land. Internal migration of people was one of the major drivers of LULC change. The results reveal that significant LULC changes in terms of composition and spatial structure over the 31-year period have occurred in the designated protected area. Landscape metrics were able to assess the trend of spatial patchiness over the studied period. A discussion of the significance of changes in land use systems for understanding the causes and consequences of change is provided.

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Multispectral and Texture Feature Application in Image-Object Analysis of Summer Vegetation in Eastern Tajikistan Pamirs

Cited by 39 publications

References 74 publications

Estimating Biomass of Native Grass Grown under Complex Management Treatments Using WorldView-3 Spectral Derivatives

Estimating Biomass of Native Grass Grown under Complex Management Treatments Using WorldView-3 Spectral Derivatives

Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series

Multi-Criteria Assessment of Land Cover Dynamic Changes in Halgurd Sakran National Park (HSNP), Kurdistan Region of Iraq, Using Remote Sensing and GIS

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