scholarly journals GIS and Remote Sensing for Malaria Risk Mapping, Ethiopia

2014 ◽  
Vol XL-8 ◽  
pp. 155-161 ◽  
Author(s):  
A. Ahmed
Keyword(s):  
At Risk ◽  
Land Use ◽  
Land Cover ◽  
Malaria Risk ◽  
Hazard Map ◽  
Land Use Land Cover ◽  
Risk Map ◽  
Risk Area ◽  
Element At Risk ◽  
Very High

Integrating malaria data into a decision support system (DSS) using Geographic Information System (GIS) and remote sensing tool can provide timely information and decision makers get prepared to make better and faster decisions which can reduce the damage and minimize the loss caused. This paper attempted to asses and produce maps of malaria prone areas including the most important natural factors. The input data were based on the geospatial factors including climatic, social and Topographic aspects from secondary data. The objective of study is to prepare malaria hazard, Vulnerability, and element at risk map which give the final output, malaria risk map. The malaria hazard analyses were computed using multi criteria evaluation (MCE) using environmental factors such as topographic factors (elevation, slope and flow distance to stream), land use/ land cover and Breeding site were developed and weighted, then weighted overlay technique were computed in ArcGIS software to generate malaria hazard map. The resulting malaria hazard map depicts that 19.2 %, 30.8 %, 25.1 %, 16.6 % and 8.3 % of the District were subjected to very high, high, moderate, low and very low malaria hazard areas respectively. For vulnerability analysis, health station location and speed constant in Spatial Analyst module were used to generate factor maps. For element at risk, land use land cover map were used to generate element at risk map. Finally malaria risk map of the District was generated. Land use land cover map which is the element at risk in the District, the vulnerability map and the hazard map were overlaid. The final output based on this approach is a malaria risk map, which is classified into 5 classes which is Very High-risk area, High-risk area, Moderate risk area, Low risk area and Very low risk area. The risk map produced from the overlay analysis showed that 20.5 %, 11.6 %, 23.8 %, 34.1 % and 26.4 % of the District were subjected to very high, high, moderate, low and very low malaria risk respectively. This help to plan valuable measures to be taken in early warning, monitor, control and prevent malaria epidemics.

scholarly journals URBAN VEGETATION CLASSIFICATION WITH NDVI THRESHOLD VALUE METHOD WITH VERY HIGH RESOLUTION (VHR) PLEIADES IMAGERY

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 237-240 ◽  
Author(s):  
H. Hashim ◽  
Z. Abd Latif ◽  
N. A. Adnan
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Urban Area ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Urban Vegetation ◽  
Land Use Land Cover ◽  
Sensing Data ◽  
Very High

Abstract. Recently the sensing data for urban mapping used is in high demand together with the accessible of very high resolution (VHR) satellite data such as Worldview and Pleiades. This article presents the use of very high resolution (VHR) remote sensing data for urban vegetation mapping. The research objectives were to assess the use of Pleiades imagery to extricate the data of urban vegetation in urban area of Kuala Lumpur. Normalized Difference Vegetation Index (NDVI) were employs with VHR data to find Vegetation Index for classification process of vegetation and non-vegetation classes. Land use classes are easily determined by computing their Normalized Difference Vegetation Index for Land use land cover classification. Maximum likelihood was conducted for the classification phase. NDVI were extracted from the imagery to assist the process of classification. NDVI method is use by referring to its features such as vegetation at different NDVI threshold values. The result showed three classes of land cover that consist of low vegetation, high vegetation and non-vegetation area. The accuracy assessment gained was then being implemented using the visual interpretation and overall accuracy achieved was 70.740% with kappa coefficient of 0.5. This study gained the proposed threshold method using NDVI value able to identify and classify urban vegetation with the use of VHR Pleiades imagery and need further improvement when apply to different area of interest and different land use land cover characteristics. The information achieved from the result able to help planners for future planning for conservation of vegetation in urban area.

scholarly journals Flood Susceptibility Mapping of Makera District and Environs in Kaduna South Local Government Area of Kaduna State-Nigeria

2021 ◽  
Vol 5 (2) ◽  
pp. 420-432
Author(s):  
U.S. Ibrahim ◽  
T.T. Youngu ◽  
B. Swafiyudeen ◽  
A.Z. Abubakar ◽  
A.K. Zainabu ◽  
...  
Keyword(s):  
At Risk ◽  
Land Use ◽  
Local Government ◽  
Land Cover ◽  
Drainage Density ◽  
Local Government Area ◽  
Susceptibility Map ◽  
Land Use Land Cover ◽  
Strict Adherence ◽  
Thematic Layers

The increased flood incidences experienced all over the world due to climate change dynamics call for a concerted effort towards forestalling future hazards. This study thus, identified the areas that are susceptibility to floods in parts of the Makera district of the Kaduna South Local Government Area in Nigeria using geospatial techniques. Geographic Information System (GIS) was used to produce thematic layers of the factors contributing to flooding (elevation, slope, drainage density, rainfall, land use/land cover); and a multi-criteria evaluation particularly the “Analytical Hierarchical process” (AHP) was applied to determine the locations at risk. The various thematic layers were integrated into the weighted overlay tool in the ArcGIS 10.3 environment to generate the final susceptibility map. The overlay tool was also used to determine the elements at risk of flood in the study area. The results show that the areas that were highly susceptible to flood constituted about 39% of the study area, while moderate and low vulnerable areas constituted about 26% and 35%, respectively. The result of the multi-criteria analysis revealed that land use/land cover (0.601) was the factor that contributed the most to flooding in the study area based on the criteria weights followed by rainfall (0.470), drainage density (0.326), elevation (0.144), and slope (0.099), respectively. The study recommends that authorities concerned should ensure strict adherence to land use planning act, such that floodplains are avoided during development of any type.

scholarly journals Use of GIS and Remote Sensing Data to Understand the Impacts of Land Use/Land Cover Changes (LULCC) on Snow Leopard (Panthera uncia) Habitat in Pakistan

2021 ◽  
Vol 13 (7) ◽  
pp. 3590
Author(s):  
Tauheed Ullah Khan ◽  
Abdul Mannan ◽  
Charlotte E. Hacker ◽  
Shahid Ahmad ◽  
Muhammad Amir Siddique ◽  
...  
Keyword(s):  
Remote Sensing ◽  
At Risk ◽  
Land Use ◽  
Land Cover ◽  
Human Population ◽  
Land Cover Changes ◽  
Snow Leopard ◽  
Land Use Land Cover ◽  
Gis And Remote Sensing ◽  
Panthera Uncia

Habitat degradation and species range contraction due to land use/land cover changes (LULCC) is a major threat to global biodiversity. The ever-growing human population has trespassed deep into the natural habitat of many species via the expansion of agricultural lands and infrastructural development. Carnivore species are particularly at risk, as they demand conserved and well-connected habitat with minimum to no anthropogenic disturbance. In Pakistan, the snow leopard (Panthera uncia) is found in three mountain ranges—the Himalayas, Hindukush, and Karakoram. Despite this being one of the harshest environments on the planet, a large population of humans reside here and exploit surrounding natural resources to meet their needs. Keeping in view this exponentially growing population and its potential impacts on at-risk species like the snow leopard, we used geographic information systems (GIS) and remote sensing with the aim of identifying and quantifying LULCC across snow leopard range in Pakistan for the years 2000, 2010, and 2020. A massive expansion of 1804.13 km2 (163%) was observed in the built-up area during the study period. Similarly, an increase of 3177.74 km2 (153%) was observed in agricultural land. Barren mountain land increased by 12,368.39 km2 (28%) while forest land decreased by 2478.43 km2 (28%) and area with snow cover decreased by 14,799.83 km2 (52%). Drivers of these large-scale changes are likely the expanding human population and climate change. The overall quality and quantity of snow leopard habitat in Pakistan has drastically changed in the last 20 years and could be compromised. Swift and direct conservation actions to monitor LULCC are recommended to reduce any associated negative impacts on species preservation efforts. In the future, a series of extensive field surveys and studies should be carried out to monitor key drivers of LULCC across the observed area.

Very High Resolution Object-Based Land Use–Land Cover Urban Classification Using Extreme Gradient Boosting

2018 ◽  
Vol 15 (4) ◽  
pp. 607-611 ◽  
Author(s):  
Stefanos Georganos ◽  
Tais Grippa ◽  
Sabine Vanhuysse ◽  
Moritz Lennert ◽  
Michal Shimoni ◽  
...  
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Gradient Boosting ◽  
Land Use Land Cover ◽  
Urban Classification ◽  
Object Based ◽  
Extreme Gradient Boosting ◽  
Very High

Lands at risk: Land use/land cover change in two contrasting tropical dry regions of Mexico

2018 ◽  
Vol 99 ◽  
pp. 22-30 ◽  
Author(s):  
Leonardo Calzada ◽  
Jorge A. Meave ◽  
Consuelo Bonfil ◽  
Fernanda Figueroa
Keyword(s):  
At Risk ◽  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Land Use Land Cover

scholarly journals OBJECT ORIENTED CLASSIFICATION AND FEATURE EXTRACTION FOR PARTS OF EAST DELHI USING HYBRID APPROACH

2018 ◽  
Vol XLII-5 ◽  
pp. 749-754
Author(s):  
P. Kumar ◽  
S. Ravindranath ◽  
K. G. Raj
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Satellite Data ◽  
Urban Land ◽  
Urban Land Use ◽  
Data Sets ◽  
Land Use Land Cover ◽  
Master Plans ◽  
Very High

<p><strong>Abstract.</strong> Rapid urbanization of Indian cities requires a focused attention with respect to preparation of Master Plans of cities. Urban land use/land cover from very high resolution satellite data sets is an important input for the preparation of the master plans of the cities along with extraction of transportation network, infrastructure details etc. Conventional classifiers, which are pixel based do not yield reasonably accurate urban land use/land cover classification of very high resolution satellite data (usually merged images of Panchromatic &amp;amp; Multispectral). Object Based Image Classification techniques are being used to generate urban land use maps with ease which is GIS compatible while using very high resolution satellite data sets. In this study, Object Based Image Analysis (OBIA) has been used to create broad level urban Land Use / Land Cover (LU/LC) map using high resolution ResourceSat-2 LISS-4 and Cartosat-1 pan-sharpened image on the study area covering parts of East Delhi City. Spectral indices, geometric parameters and statistical textural methods were used to create algorithms and rule sets for feature classification. A LU/LC map of the study area comprising of 4 major LU/LC classes with its main focus on separation of barren areas from built up areas has been attempted. The overall accuracy of the result obtained is estimated to be approximately 70%.</p>

scholarly journals Land use land cover classification using local multiple pattern from very high resolution satellite imagery

2014 ◽  
Vol XL-8 ◽  
pp. 971-976 ◽  
Author(s):  
R. Suresh Kumar ◽  
A. R. Mahesh Balaji
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spatial Resolution ◽  
Spatial Information ◽  
High Spatial Resolution ◽  
Spectral Variation ◽  
Land Use Land Cover ◽  
Texture Information ◽  
Very High Spatial Resolution ◽  
Very High

The recent development in satellite sensors provide images with very high spatial resolution that aids detailed mapping of Land Use Land Cover (LULC). But the heterogeneity in the landscapes often results in spectral variation within the same and spectral confusion among different LU/LC classes at finer spatial resolution. This leads to poor classification performances based on traditional spectral-based classification. Many studies have been addressed to improve this classification by incorporating texture information with multispectral images. Although different methods are available to extract textures from the satellite images, only a limited number of studies compared their performance in classification. The major problem with the existing texture measures is either scale/orientation/illumination variant (Haralick textures) or computationally difficult (Gabor textures) or less informative (Local binary pattern). This paper explores the use of texture information captured by Local Multiple Patterns (LMP) for LULC classification in a spectral-spatial classifier framework. LMP preserve more structural information and involves less computational efforts. Thus LMP is expected to be more promising for capturing spatial information from very high spatial resolution images. The proposed method is implemented with spectral bands and LMP derived from WorldView-2 multispectral imagery acquired for Madurai, India study area. The Multi-Layer-Perceptron neural network is used as a classifier. The proposed classification method is compared with LBP and conventional Maximum Likelihood Classification (MLC) separately. The classification results with 89.5% clarify the improvement offered by the LMP for LULC classification in comparison with the conventional approaches.

Impact of Urbanization on Land Use/Land Cover — Case Study: The Western Part of Khartoum State, Sudan

2017 ◽  
Vol 04 (03) ◽  
pp. 272-277
Author(s):  
Tawhida A. Yousif ◽  
Nancy I. Abdalla ◽  
El-Mugheira M. Ibrahim ◽  
Afraa M. E. Adam
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Use Land Cover ◽  
Impact Of Urbanization
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