Seasonal change detection of riparian zones with remote sensing images and genetic programming in a semi-arid watershed

Makkeasorn, Ammarin; Chang, Ni‐Bin; Li, Jiahong

doi:10.1016/j.jenvman.2008.04.004

Cited by 58 publications

(43 citation statements)

References 43 publications

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“…Described as transition zones between aquatic and terrestrial environments, with high fluxes of material, water and energy [3][4][5], RE are often considered biodiversity hotspots [6,7] as well as ecosystem services hotspots [3,[8][9][10][11][12]. However, human activities such as livestock ranching, agricultural development and urbanization could result in the modification and degradation of these systems, diminishing their capacity to sustain their ecological function and thus provide services in the future.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Landscape Trends on Agriculture, Introduced Exotic Grasslands and Riparian Ecosystems in Arid Regions of Mexico

et al. 2016

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Riparian Zones are considered biodiversity and ecosystem services hotspots. In arid environments, these ecosystems represent key habitats, since water availability makes them unique in terms of fauna, flora and ecological processes. Simple yet powerful remote sensing techniques were used to assess how spatial and temporal land cover dynamics, and water depth reflect distribution of key land cover types in riparian areas. Our study area includes the San Miguel and Zanjon rivers in Northwest Mexico. We used a supervised classification and regression tree (CART) algorithm to produce thematic classifications (with accuracies higher than 78%) for 1993, 2002 and 2011 using Landsat TM scenes. Our results suggest a decline in agriculture (32.5% area decrease) and cultivated grasslands (21.1% area decrease) from 1993 to 2011 in the study area. We found constant fluctuation between adjacent land cover classes and riparian habitat. We also found that water depth restricts Riparian Vegetation distribution but not agricultural lands or induced grasslands. Using remote sensing combined with spatial analysis, we were able to reach a better understanding of how riparian habitats are being modified in arid environments and how they have changed through time.

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Section: Introductionmentioning

confidence: 99%

Analyzing Landscape Trends on Agriculture, Introduced Exotic Grasslands and Riparian Ecosystems in Arid Regions of Mexico

et al. 2016

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“…SVM approach is used for land cover change detection (Nemmour, Chibani, 2006) and forest cover change analysis (Huang et al, 2008). Some other machine learning techniques are applied for change detection via learning to change and non-change separation: decision tree (Im, Jensen, 2005), genetic programming (Makkeasorn et al, 2009), random forest (Smith, 2008) and cellular automata (Yang et al, 2008). Object-based techniques operate with objects instead of pixels.…”

Section: Related Workmentioning

confidence: 99%

Change Detection via Selective Guided Contrasting Filters

Vizilter

Rubis

Zheltov

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Change detection scheme based on guided contrasting was previously proposed. Guided contrasting filter takes two images (test and sample) as input and forms the output as filtered version of test image. Such filter preserves the similar details and smooths the nonsimilar details of test image with respect to sample image. Due to this the difference between test image and its filtered version (difference map) could be a basis for robust change detection. Guided contrasting is performed in two steps: at the first step some smoothing operator (SO) is applied for elimination of test image details; at the second step all matched details are restored with local contrast proportional to the value of some local similarity coefficient (LSC). The guided contrasting filter was proposed based on local average smoothing as SO and local linear correlation as LSC. In this paper we propose and implement new set of selective guided contrasting filters based on different combinations of various SO and thresholded LSC. Linear average and Gaussian smoothing, nonlinear median filtering, morphological opening and closing are considered as SO. Local linear correlation coefficient, morphological correlation coefficient (MCC), mutual information, mean square MCC and geometrical correlation coefficients are applied as LSC. Thresholding of LSC allows operating with non-normalized LSC and enhancing the selective properties of guided contrasting filters: details are either totally recovered or not recovered at all after the smoothing. These different guided contrasting filters are tested as a part of previously proposed change detection pipeline, which contains following stages: guided contrasting filtering on image pyramid, calculation of difference map, binarization, extraction of change proposals and testing change proposals using local MCC. Experiments on real and simulated image bases demonstrate the applicability of all proposed selective guided contrasting filters. All implemented filters provide the robustness relative to weak geometrical discrepancy of compared images. Selective guided contrasting based on morphological opening/closing and thresholded morphological correlation demonstrates the best change detection result.

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“…The Support Vector Machine (SVM) approach based on (Vapnik, 2000) considers the finding change and no-change regions as a problem of binary classification in a space of spectral features (Huang et al, 2008;Bovolo et al, 2008). Other machine learning techniques applied for change detection are: decision tree (Im and Jensen, 2005), genetic programming (Makkeasorn et al, 2009), random forest (Smith, 2010) and cellular automata (Yang et al, 2008).…”

Section: Related Workmentioning

confidence: 99%

Change Detection via Morphological Comparative Filters

Vizilter

Rubis

Zheltov

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this paper we propose the new change detection technique based on morphological comparative filtering. This technique generalizes the morphological image analysis scheme proposed by Pytiev. A new class of comparative filters based on guided contrasting is developed. Comparative filtering based on diffusion morphology is implemented too. The change detection pipeline contains: comparative filtering on image pyramid, calculation of morphological difference map, binarization, extraction of change proposals and testing change proposals using local morphological correlation coefficient. Experimental results demonstrate the applicability of proposed approach.

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Seasonal change detection of riparian zones with remote sensing images and genetic programming in a semi-arid watershed

Cited by 58 publications

References 43 publications

Analyzing Landscape Trends on Agriculture, Introduced Exotic Grasslands and Riparian Ecosystems in Arid Regions of Mexico

Analyzing Landscape Trends on Agriculture, Introduced Exotic Grasslands and Riparian Ecosystems in Arid Regions of Mexico

Change Detection via Selective Guided Contrasting Filters

Change Detection via Morphological Comparative Filters

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