scholarly journals Multi-criteria potential groundwater contamination and human activities: Araras watershed, Brazil

RBRH ◽  
2017 ◽  
Vol 22 (0) ◽  
Author(s):  
Fabíola Geovanna Piga ◽  
Nícolas Guerra Rodrigues Tão ◽  
Mayara Herrmann Ruggiero ◽  
Darlan de Souza Marquezola ◽  
Welliton Leandro de Oliveira Boina ◽  
...  
Keyword(s):  
Land Use ◽  
Groundwater Contamination ◽  
Human Activities ◽  
Land Use Land Cover ◽  
Surface Water Resources ◽  
Contaminant Sources ◽  
Potential Groundwater ◽  
Water Resources Protection ◽  
Clastic Sedimentary ◽  
Very High

ABSTRACT Assessment of groundwater contamination potential using geological, hydrological and hydrogeological attributes, is an efficient mechanism of sub-surface water resources protection and conservation. However, usually this method does not take into consideration the potentially polluting human activities, either in punctual or diffuse sources, or because the relative importance of the attributes are not considered. The paper proposes a multi-criteria approach as a way of solving this gap, reducing subjectivity and considering land use/cover due human actions influence in the process. The study was developed in Araras river watershed (Paraná Basin, Brazil) using rock, groundwater, relief, soils, and land use/land cover classification, employing multi-criteria analysis and data of contamination sources. The potential contamination classification showed the predominance (54% of the area) of very high and high potential, especially due to geological environment conditions (exploitation areas and groundwater recharge of the Itararé Aquifer - clastic sedimentary and free flow). The contaminant sources analyzed are located mainly in higher potential contamination areas.

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 Land Use/Land Cover Change Detection and Urban Sprawl Analysis in the Mount Makiling Forest Reserve Watersheds and Buffer Zone, Philippines

Environments ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 9
Author(s):  
Merlyn Soriano ◽  
Noba Hilvano ◽  
Ronald Garcia ◽  
Aldrin Hao ◽  
Aldin Alegre ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Urban Sprawl ◽  
Human Activities ◽  
Land Development ◽  
Buffer Zone ◽  
Land Conversion ◽  
Land Use Land Cover ◽  
Forest Reserve

Ecologically Valuable Areas play an important role in providing ecosystem services, however, human activities such as land conversion and urban sprawl pose pressures and threats to these areas. The study assessed the land use/land cover and urban sprawl in the Mount Makiling Forest Reserve (MMFR) Watersheds and Buffer Zone from 1992 to 2015 using remote sensing and Geographic Information System (GIS). Results showed that the land use/cover within the MMFR buffer zone has changed from 1992 to 2015 with built-up areas increasing by 117% despite Proclamation 1257, s. 1998 which regulates human activities in the zone. Based on the Shannon entropy analysis the land development in the MMFR buffer zone tends to be dispersed and sprawling. However, when the magnitude of change of urban sprawl in the buffer zone from 2002 to 2015 was calculated, a decrease in the entropy value was observed which implies a compacting pattern as the human settlement in the buffer zone increases over time. Proclamation 1257, s. 1998 needs to be strengthened to protect MMFR and its buffer zone from further encroachment and pressure. Moreover, remote sensing and GIS proved to be useful tools for assessing urban sprawl in ecologically valuable areas such as MMFR.

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.

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

scholarly journals Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3120 ◽  
Author(s):  
Guoyin Cai ◽  
Huiqun Ren ◽  
Liuzhong Yang ◽  
Ning Zhang ◽  
Mingyi Du ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
High Resolution ◽  
Classification Scheme ◽  
Urban Land Use ◽  
Land Use Land Cover ◽  
Large Urban Area ◽  
Object Based ◽  
Medium Resolution ◽  
Very High

Urban Land Use/Land Cover (LULC) information is essential for urban and environmental management. It is, however, very difficult to automatically extract detailed urban LULC information from remote sensing imagery, especially for a large urban area. Medium resolution imagery, such as Landsat Thematic Mapper (TM) data, cannot uncover detailed LULC information. Further, very high resolution (VHR) satellite imagery, such as IKONOS and QuickBird data, can only be applied to a small area, largely due to the data unavailability and high computation cost. As a result, little research has been conducted to extract detailed urban LULC information for a large urban area. This study, therefore, developed a three-layer classification scheme for deriving detailedurban LULC information by integrating newly launched Chinese GF-1 (medium resolution) and GF-2 (very high resolution) satellite imagery and synthetically incorporating geometry, texture, and spectral information through multi-resolution image segmentation and object-based image classification (OBIA). Homogeneous urban LULC types such as water bodies or large areas of vegetation could be derived from GF-1 imagery with 16 m and 8 m spatial resolutions, while heterogeneous urban LULC types such as industrial buildings, residential buildings, and roads could be extracted from GF-2 imagery with 3.2 m and 0.8 m spatial resolutions. The multi-resolution segmentation method and a random forest algorithm were employed to perform image segmentation and object-based image classification, respectively. An analysis of the results suggests an overall accuracy of 0.89 and 0.87 were achieved for the second and third level urban LULC classification maps, respectively. Therefore, the three-layer classification scheme has the potential to derive high accuracy urban LULC information through integrating medium and high-resolution remote sensing imagery.

scholarly journals How Climate Change and Land Use/Land Cover Change Affect Domestic Water Vulnerability in Yangambi Watersheds (D. R. Congo)

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 165
Author(s):  
David Ushindi Chishugi ◽  
Denis Jean Sonwa ◽  
Jean-Marie Kahindo ◽  
Destin Itunda ◽  
Josué Bahati Chishugi ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Quality ◽  
Land Use ◽  
Water Resources ◽  
Human Activities ◽  
Edge Density ◽  
Land Use Land Cover ◽  
Domestic Water ◽  
Dense Forest ◽  
Water Vulnerability

In the tropics, the domestic water supply depends principally on ecosystem services, including the regulation and purification of water by humid, dense tropical forests. The Yangambi Biosphere Reserve (YBR) landscape is situated within such forests in the Democratic Republic of Congo (DRC). Surprisingly, given its proximity to the Congo River, the YBR is confronted with water issues. As part of its ecosystem function, the landscape is expected to reduce deterioration of water quality. However, environmental consequences are increasing due to conversion of its dense forest into other types of land use/land cover (LULC) in response to human activities. It is therefore important to check how the physicochemical quality parameters of water resources are influenced by landscape parameters—and to know if the population can adapt to this water vulnerability. To do this, we analyzed the watershed typology (including morphometric and LULC characteristics) and the physical and chemical parameters of water within the principal watershed’s rivers. We also analyzed data from surveys and the Yangambi meteorological station. We found that some landscape indices related to LULC significantly influence water quality deterioration in Yangambi. On average, each person in the Yangambi landscape uses 29–43 liters of water per day. Unfortunately, this falls short of World Health Organization standards regarding some parameters. The best fitted simple linear regression model explains the variation in pH as a function of edge density of perturbed forest, edge density of crop land and patch density of dense forest up to 94%, 92% and 90%, respectively. While many researchers have identified the consequences of climate change and human activities on these water resources, the population is not well-equipped to deal with them. These results suggest that water management policies should consider the specificities of the Yangambi landscape in order to develop better mitigation strategies for a rational management of water resources in the YBR in the context of climate change.

scholarly journals Impacts of Climatic Variation and Human Activity on Runoff in Western China

2022 ◽  
Vol 14 (2) ◽  
pp. 942
Author(s):  
Yinge Liu ◽  
Keke Yu ◽  
Yaqian Zhao ◽  
Jiangchuan Bao
Keyword(s):  
Land Use ◽  
Relative Humidity ◽  
Human Activities ◽  
Hydrological Cycle ◽  
Climatic Variation ◽  
Western China ◽  
Land Use Land Cover ◽  
Sunshine Hours ◽  
Weihe River ◽  
Weihe River Basin

Hydrological cycle is sensitively affected by climatic variation and human activity. Taking the upper- and middle-stream of the Weihe River in western China as an example, using multiple meteorological and hydrological elements, as well as land-use/land-cover change (LUCC) data, we constructed a sensitivity model of runoff to climatic elements and human activities based on the hydro-thermal coupling equilibrium equation, while a cumulative slope was used to establish a comprehensive estimation model for the contributions of climatic variation and human activities to the changes of runoff. The results showed that the above function model established could be well applied to quantitatively study the elasticity of runoff’s response to climatic variation and human activities. It was found that the annual average precipitation, evaporation, wind velocity, sunshine hours, relative humidity and runoff showed decreasing trends and that temperature increased. While in the hydrological cycle, precipitation and relative humidity had a non-linear positive driving effect on runoff, while temperature, evaporation, sunshine hours, wind velocity, and land-use/land-cover change (LUCC) have non-linearly negatively driven the variation of runoff. Moreover, runoff has a strong sensitive response to precipitation, evaporation and LUCC. In areas with strong human activities, the sensitivity of runoff to climatic change was decreasing, and runoff has a greater elastic response to underlying surface parameters. In addition, the analysis showed that the abrupt years of climate and runoff changes in the Weihe River Basin were 1970, 1985 and 1993. Before 1985, the contribution rate of climatic variation to runoff was 68.3%, being greater than that of human activities to runoff, and then the contribution rates of human activities to runoff reached 75.1%. The impact of natural climate on runoff was weakened, and the effect of human activities on runoff reduction increased. Under 30 hypothetical climatic scenarios, the evaluation of runoff in the future showed that the runoff in the Weihe River Basin will be greatly reduced, and the reduction will be more significant during the flood season. Comparing the geographically fragile environments and intense human activities, it was believed that climatic variation had a dramatic effect on driving the water cycle of precipitation and evaporation and affected regional water balance and water distribution, while human activities had driven the hydrological processes of the underlying surface, thus becoming the main factors in the reduction of runoff. This study provided scientific tools for regional climate change and water resources assessment.

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>

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