scholarly journals Demarcation of Groundwater Potential Zones Using Geospatial Technology in Edappadi Block, Salem District, Tamil Nadu, India

2021 ◽  
Vol 10 (2) ◽  
pp. 36-49
Author(s):  
Santhosh M ◽  
Thirukumaran V
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Ground Water ◽  
Water Potential ◽  
Tamil Nadu ◽  
Drainage Density ◽  
Potential Zone ◽  
Land Use Land Cover ◽  
Visual Interpretation ◽  
Salem District

Groundwater is one of the world's most valuable resources, which contributes 85% of drinking water supplies. It is imperative to explore ground-water zone for the utilization to the people. Edappadi block in Salem District, Tamil Nadu, is rocky terrain largely depends on groundwater for drinking and irrigation. One of the most useful tools for locating ground water potential zones is remote sensing and geographic information system (GIS). Different types of thematic maps, such as lithology, geomorphology, drainage density, slope, lineament, and land use/land cover, can be easily created by visual interpretation of IRS-1C, LISS-III data and maps are prepared using GIS. The water potential zones are determined using a rank and weightage approach. In order to demarcate the water potential zones, the vector overlay method is used. Lithology is given more weight than geomorphology, followed by lineament density, lineament frequency, lineament intersection, slope and land use/land cover. Based on the overall results, the potential zone of groundwater in the research region is divided into five groups: Excellent, Very Good, Good, Moderate, and Poor.

scholarly journals Land Use/ Land Cover Dynamics Study and Prediction In Jaipur City us­ing CA Markov Model Integrated with Road Network

2021 ◽  
Author(s):  
Nitesh Kumar Mourya ◽  
Sana Rafi ◽  
Saima Shamoo
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Road Network ◽  
Markov Chain Model ◽  
Chain Model ◽  
Land Use Land Cover ◽  
Visual Interpretation ◽  
Lulc Change ◽  
Barren Land ◽  
Jaipur City

Abstract Land Use Land Cover (LULC) dynamics analysis is critical and should be done regularly. It draws attention to LULC developments that can be addressed before they become unmanageable disasters or circumstances. For the years 2000, 2010, and 2020, LULC change analysis was carried out in Jaipur City, Rajasthan, India. The LULC maps were created using Landsat data through a visual interpretation technique at a scale of 1:50,000. These maps were classified into vegetation, agriculture, built-up areas, barren land, and water bodies. LULC was predicted by extrapolating the current LULC change pattern. Using a Cellular Automata-Markov Chain Model (CA Markov) integrated with road network, the current LULC change trend was extrapolated and utilized to estimate the LULC map for the years 2020, 2030, 2040, and 2050. The strategy was validated by estimating LULC change for 2020 and comparing it to the actual LULC map for that year. The urban area contributed to 4. 75% in 2000 of the total area in Jaipur city. The percentage of area under urban class has increased to 9.68% in 2010 and 12.96% in 2020. The prediction based on 2000-2010 and 2010-2020 has shown an unprecedented decadal growth in the built-up area till 2050. The prediction based on the 2000-2010 period has shown a rise of 92.04 % during 2020-2030, 77.13 % during 2030-2040 and, 64.34 % during 2040-2050. The prediction based on the 2010-2020 period has shown a rise of 102.42% during 2020-2030, 73.56% during 2030-2040 and, 54.47 % during 2040-2050. This study is, therefore, calls for policy interventions to manage population and urban growth.

scholarly journals Land Use/Land Cover Mapping Using Geospatial Technology for Environmental Management

2019 ◽  
Vol 8 (9) ◽  
pp. 168-172
Keyword(s):  
Environmental Management ◽  
Land Use ◽  
Land Cover ◽  
Spatial Information ◽  
Decision Makers ◽  
Land Use Land Cover ◽  
Land Resources ◽  
Visual Interpretation ◽  
Management Planning ◽  
Cropping Patterns

The aim of the attempt was to study the Land use/Land cover attributes for environmental management planning for socio economic growth of study area. Evaluation of Land Resources in given study area by Remote sensing and Geographic Information System (GIS) technologies help to generate the spatial information to study the current conditions deliberate to the past conditions data and estimate the future requirements. The IRS-P6 satellite Imagery and Survey of India toposheets data, visual interpretation technique, Arc/Info and Arc View GIS software’s are used to prepare the final Land use/Land cover information. This data is useful for environment and natural resources development management. This type of land information study helps to prepare the Land and water Resources Action plans for conservation of suitable cropping patterns, and improved productivity of the study area and to provide the primary requirements of farmers, to enhance their background conditions and help to develop or enhance decision makers for sustainable development

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 Land use Land Cover Change Detection using K-means Clustering and Maximum Likelihood Classification Method in the Javadi Hills, Tamil Nadu, India

2019 ◽  
Vol 9 (1S3) ◽  
pp. 51-56 ◽  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Tamil Nadu ◽  
Hybrid Approach ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Change Analysis ◽  
Supervised Classifiers ◽  
Liss Iii

Land use/Land cover (LU/LC) change analysis is the present-day challenging task for the researchers in defining the environmental change across the world in the field of remote sensing and GIS (Geographic Information System). This paper analyzes the LU/LC changes between the years 2009 and 2019 in the region of Javadi Hills located in Tamil Nadu, India. Images from the Indian remote sensing satellite Resourcesat-1 LISS III and American earth observation satellite Landsat-8 were used for analyzing the LU/LC change for the study area. In this work, the classification was performed by using the hybrid approach of unsupervised and supervised classifiers. The classified LU/LC map for the study area defines forest and non-forest covered region. The key objective of this work was to identify the percentage of LU/LC change occurred in our study area for the years 2009 to 2014 and 2014 to 2019. Observing and examining the changes occurred in the study area provides a clear view to the land resources management to take effective measures in protecting the environment.

scholarly journals GIS-Based Study To Develop Landslide Susceptibility Zonation Map Of District Mandi, Himachal Pradesh

2021 ◽  
Author(s):  
Sachin Verma ◽  
Vidya Sagar Khanduri
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Digital Elevation Model ◽  
Landslide Susceptibility ◽  
Drainage Density ◽  
Land Use Land Cover ◽  
Landslide Susceptibility Zonation ◽  
Zonation Map ◽  
Digital Elevation ◽  
Elevation Model

Abstract Rising Incidents of landslide at district Mandi is issue of concern in Himachal Pradesh. Every year many people losses their life and property in these landslide event. This study is conducted with aim to preparation of landslide susceptibility zonation map of district Mandi using method of frequency ratio. Causative factor of landslide involved in preparation of Landslide susceptibility zonation map is Lithology, Slope, Drainage density, Aspect and Land use land cover. Slope, Drainage density, Aspect map are extracted through digital elevation model. Source of Digital elevation model used here is based on SRTM data whereas lithology map is based on data of geological survey of India. Land use land cover map is extracted by images of Landsat 8 satellite. Total of 52 existing landslides are used to model final map. LSZ map show 40.42% area is falling under medium susceptibility class, 34.5 % under low and 25.07% is under high susceptibility class which cover tehsils Mandi, Chachyot, Thunag and some part of Padhar, Aut and Bali Chowki. Further to validate these result areas under curve (AUC) method is use which give prediction rate of 76.06%.

scholarly journals RECENT LAND USE AND LAND COVER CHANGE DYNAMICS IN THE GRAN CHACO AMERICANO

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 369-372
Author(s):  
S. Banchero ◽  
D. de Abelleyra ◽  
S. R. Veron ◽  
M. J. Mosciaro ◽  
F. Arévalos ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Machine Learning Algorithms ◽  
Potential Contribution ◽  
Land Use Land Cover ◽  
Visual Interpretation ◽  
Herbaceous Vegetation ◽  
Territorial Planning ◽  
Gran Chaco ◽  
Environmental Interactions

Abstract. Land transformation is one of the most significant human changes on the Earth’s surface processes. Therefore, land use land cover time series are a key input for environmental monitoring, natural resources management, territorial planning enforcement at national scale. We here capitalize from the MapBiomas initiative to characterize land use land cover (LULC) change in the Gran Chaco between 2010 and 2017. Specifically we sought to a) quantify annual changes in the main LULC classes; b) identify the main LULC transitions and c) relate these transitions to current land use policies. Within the MapBiomas project, Landsat based annual maps depicting natural woody vegetation, natural herbaceous vegetation, dispersed natural vegetation, cropland, pastures, bare areas and water. We used Random Forest machine learning algorithms trained by samples produced by visual interpretation of high resolution images. Annual overall accuracy ranged from 0,73 to 0,74. Our results showed that, between 2010 and 2017, agriculture and pasture lands increased ca. 3.7 Mha while natural forestry decreased by 2.3 Mha. Transitions from forests to agriculture accounted for 1.14% of the overall deforestation while 86% was associated to pastures and natural herbaceous vegetation. In Argentina, forest loss occurred primarily (39%) on areas non considered by the territorial planning Law, followed by medium (33%), high (19%) and low (9%) conservation priority classes. These results illustrate the potential contribution of remote sensing to characterize complex human environmental interactions occurring over extended areas and timeframes.

scholarly journals Land use/land cover change detection combining automatic processing and visual interpretation

2017 ◽  
Vol 50 (1) ◽  
pp. 626-635 ◽  
Author(s):  
Jean-François Mas ◽  
Richard Lemoine-Rodríguez ◽  
Rafael González-López ◽  
Jairo López-Sánchez ◽  
Andrés Piña-Garduño ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Automatic Processing ◽  
Land Use Land Cover ◽  
Visual Interpretation ◽  
Land Cover Change Detection

Land-Use/Land-Cover Change Dynamics and Groundwater Quality in and around Shrimp Farming Area in Coastal Watershed, Cuddalore District, Tamil Nadu, India

2017 ◽  
Vol 113 (09) ◽  
pp. 1763 ◽  
Author(s):  
P. Nila Rekha ◽  
R. Gangadharan ◽  
P. Ravichandran ◽  
Shirley Dharshini ◽  
Wilmart Clarke ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Groundwater Quality ◽  
Land Cover Change ◽  
Tamil Nadu ◽  
Shrimp Farming ◽  
Land Use Land Cover ◽  
Coastal Watershed ◽  
Change Dynamics ◽  
Farming Area
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