Spectral mixture analysis (SMA) and change vector analysis (CVA) methods for monitoring and mapping land degradation/desertification in arid and semiarid areas (Sudan), using Landsat imagery

Salih, Abdelrahim; Ganawa, El-Tyeb; Elmahl, Anwer Alsadat

doi:10.1016/j.ejrs.2016.12.008

Cited by 41 publications

(27 citation statements)

References 12 publications

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“…This represents an exciting potential for making continental-scale assessments of inundation frequency and extent, nevertheless, thresholds used to generate the thematic map can be adapted by the user and adapted for specific regions if needed. Indeed, the nomenclature for the thematic map was designed for ecohydrological characterisation, but this can be adapted for other class types to target other applications such as mapping terrestrial vegetation loss, vegetation fragmentation [90], and desertification (e.g., [91][92][93]).…”

Section: Discussionmentioning

confidence: 99%

Tropical Wetland (TropWet) Mapping Tool: The Automatic Detection of Open and Vegetated Waterbodies in Google Earth Engine for Tropical Wetlands

2020

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Knowledge of the location and extent of surface water and inundated vegetation is vital for a range of applications including flood risk management, biodiversity monitoring, quantifying greenhouse gas emissions, and mapping water-borne disease risk. Here, we present a new tool, TropWet, which enables users of all abilities to map wetlands in herbaceous dominated regions based on simple unmixing of optical Landsat satellite imagery in the Google Earth Engine. The results demonstrate transferability throughout the African continent with a high degree of accuracy (mean 91% accuracy, st. dev 2.6%, n = 10,800). TropWet demonstrated considerable improvements over existing globally available surface water datasets for mapping the extent of important wetlands like the Okavango, Botswana. TropWet was able to provide frequency inundation maps as an indicator of malarial mosquito aquatic habitat extent and persistence in Barotseland, Zambia. TropWet was able to map flood extent comparable to operational flood risk mapping products in the Zambezi Region, Namibia. Finally, TropWet was able to quantify the effects of the El Niño/Southern Oscillation (ENSO) events on the extent of photosynthetic vegetation and wetland extent across Southern Africa. These examples demonstrate the potential for TropWet to provide policy makers with crucial information to help make national, regional, or continental scale decisions regarding wetland conservation, flood/disease hazard mapping, or mitigation against the impacts of ENSO.Landsat) with microwave systems (e.g., AMSR: Advanced Microwave Scanning Radiometer, SMAP: Soil Moisture Active Passive) [17,20]. Similarly, there is growing evidence that information from global navigation systems (GNSS-R: Global Navigation Satellite System Reflectometry) can provide timely and reliable classifications of wetlands by exploiting signals over both open water and vegetated water surfaces [21][22][23][24]. These approaches provide valuable tools for quantifying wetland dynamics at continental scales with important applications such as characterising greenhouse gas flux [17,18,24,25]. However, assessments of wetland extent and dynamics using these approaches tend to be generated at relatively coarse spatial resolutions (e.g., 25-36 km) and have limited applicability for informing decisions at a national or sub-national level, particularly related to more fine-scale environmental challenges such as those related to biodiversity, public health, and flood hazard.In terms of inundation mapping, perhaps the most mature area of research is the use of EO satellite imagery for mapping flood water hazards [2][3][4][5]7,[26][27][28][29][30][31][32]. Radar imagery provides one of the most reliable means of detecting flood water mainly due to the fact that this imagery is: (i) independent of cloud cover, (ii) relatively high resolution (e.g., Sentinel-1: 10 m), (iii) relatively high revisit times (e.g., Sentinel-1: 6-12 days), and (iv) there is a strong signal (low backscatter) over relatively smooth water surfaces. ...

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Section: Discussionmentioning

confidence: 99%

Tropical Wetland (TropWet) Mapping Tool: The Automatic Detection of Open and Vegetated Waterbodies in Google Earth Engine for Tropical Wetlands

2020

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“…In this study, soil and vegetation were defined as two endmembers [39]. Then samples of these endmembers were visually selected from the scene by identifying the representative areas of each component based on the knowledge of study area [16].…”

Section: Image Processing and Spectral Mixture Analysismentioning

confidence: 99%

Assessment of forest cover and carbon stock changes in sub-tropical pine forest of Azad Jammu & Kashmir (AJK), Pakistan using multi-temporal Landsat satellite data and field inventory

Khan

Baig

et al. 2020

PLoS ONE

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“…Understanding the distribution of land cover is crucial to the better understanding of the earth's fundamental characteristics and processes, including productivity of the land, the diversity of plant and animal species, and the biogeochemical and hydrological cycles (Giri, 2012). Several authors have reported that remote sensing data (Rader and Optical) are become an important tools for gathering, monitoring and mapping land cover types using different methods and techniques (Pilesjo, 1992, Chen et al, 2016, Osman, 1996, Lillesand and Kiefer, 1989, Salih et al, 2017, Sobrino et al, 2004, Erener et al, 2011. For example, Osman (1996) suggest that the application of nonparametric methods or knowledge-based image analysis methods to increase the degree of classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Remotely Sensed Data for Mapping Land Cover Types by Applying a Maximum Likelihood Classifier Algorithm, in Al-Ahsaa Oasis, Eastern Region, Saudi Arabia

Salih¹

2018

Preprint

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Accurate, detailed and recent Information about land cover/use is important and much more needed for different aspects of sustainable development and environmental management. As remote sensing datasets are becomes one of the most important and effective tools to generate such information, this study aimed to generating land cover map for sub area in Al-Ahasaa Oasis, Saudi Arabia, by using and classifying a subset of Landsat-ETM+ image of the selected study area, as bases and required input for future studies and researches. Different image preprocessing techniques in addition to a will-known and widely used classification method (i.e., Maximum Likelihood classifier) were applied. To be reliable with the final product, accuracy assessment was carried out with 89% agreement and accepted according to the applied method. Different land cover classes were found in the study area, which includes (Sand dunes, Water bodies, Sabakha, Bare soil, Urban, and Agricultural lands). The study also revealed that the dominant land cover class is sand dunes with approximately ± 70% in area. The study strongly indicated that the area has long been affected by sand movement. Finally, the study suggested that, further researches with more advanced methods rather than traditional methods are needed in the future to support the findings of this study, with high degree of accuracy.

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Spectral mixture analysis (SMA) and change vector analysis (CVA) methods for monitoring and mapping land degradation/desertification in arid and semiarid areas (Sudan), using Landsat imagery

Cited by 41 publications

References 12 publications

Tropical Wetland (TropWet) Mapping Tool: The Automatic Detection of Open and Vegetated Waterbodies in Google Earth Engine for Tropical Wetlands

Tropical Wetland (TropWet) Mapping Tool: The Automatic Detection of Open and Vegetated Waterbodies in Google Earth Engine for Tropical Wetlands

Assessment of forest cover and carbon stock changes in sub-tropical pine forest of Azad Jammu & Kashmir (AJK), Pakistan using multi-temporal Landsat satellite data and field inventory

Analysis of Remotely Sensed Data for Mapping Land Cover Types by Applying a Maximum Likelihood Classifier Algorithm, in Al-Ahsaa Oasis, Eastern Region, Saudi Arabia

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