scholarly journals An Integrated Approach of Remote Sensing and Gis for Land Use and Land Cover Change Detection: A Case Study of Banjar River Watershed Of Madhya Pradesh, India

2017 ◽  
Vol 12 (1) ◽  
pp. 157-164 ◽  
Author(s):  
Jagriti Tiwari ◽  
S.K. Sharma ◽  
R.J Patil
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Water Body ◽  
Madhya Pradesh ◽  
Fallow Land ◽  
River Watershed ◽  
Detection Techniques ◽  
Waste Land ◽  
Open Land

Land use and land cover of a region is one of the prime concerns in current strategies for the evaluationof the watersheds and development of decision making policies. An uncertain increase in the population of the country along with the increasing demands has imposed an immense pressure on the land threatening the sustainability of the natural resources especially in the developing countries like India. The study was carried out to detect the land use and land cover changes observed in the Banjar River watershed lies in between Balaghat and Mandla districts of Madhya Pradesh, India using the multi spectral satellite dataset for two different years i.e. 2009 & 2013. The supervised classification was done in ERDAS IMAGINE software. The images of the study area was classified into seven classes namely, river, water body, waste land, habitation, forest, agriculture/other vegetation, open land/fallow land/barren land. The result indicates that during the last five years, forest, water body, waste land and open land/fallow land/barren has been increased by 2.26%, 0.55%, 0.23% and 0.48% respectively and the river, habitation and agriculture/other vegetation has been decreased by 0.26%, 0.04%, and 3.22% respectively. Accuracy assessment was also performed in this study to determine the quality of land use/cover map and overall accuracy was found 89.70% for 2009 and 91.91% for 2013.Present study emphasizes on digital change detection techniques for recognizing temporal changes in land use/land cover of the watershed. Outcomes of this study indicate need of implementing conservation and management practices for the socioeconomic development.

scholarly journals Time Series Land Cover Mapping and Change Detection Analysis Using Geographic Information System and Remote Sensing, Northern Ethiopia

2018 ◽  
Vol 11 ◽  
pp. 117862211775160 ◽  
Author(s):  
Gebiaw T Ayele ◽  
Aschalew K Tebeje ◽  
Solomon S Demissie ◽  
Mulugeta A Belete ◽  
Mengistu A Jemberrie ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Population Growth ◽  
Change Detection ◽  
Land Cover Change ◽  
Training Data ◽  
Steady Increase ◽  
Northern Ethiopia ◽  
Detection Techniques

Land use planners require up-to-date and spatially accurate time series land resources information and changing pattern for future management. As a result, assessing the status of land cover change due to population growth and arable expansion, land degradation and poor resource management, partial implementation of policy strategies, and poorly planned infrastructural development is essential. Thus, the objective of the study was to quantify the spatiotemporal dynamics of land use land cover change between 1995 and 2014 using 5 multi-temporal cloud-free Landsat Thematic Mapper images. The maximum likelihood (ML)-supervised classification technique was applied to create signature classes for significant land cover categories using means and variances of the training data to estimate the probability that a pixel is a member of a class. The final Bayesian ML classification resulted in 12 major land cover units, and the spatiotemporal change was quantified using post-classification and statistical change detection techniques. For a period of 20 years, there was a continuously increasing demand for arable areas, which can be represented by an exponential growth model. Excepting the year 2009, the built-up area has shown a steady increase due to population growth and its need for infrastructure development. There was nearly a constant trend for water bodies with a change in slope significantly less than +0.01%. The 2014 land cover change statistics revealed that the area was mainly covered by cultivated, wood, bush, shrub, grass, and forest land mapping units accounting nearly 63%, 12%, 8%, 6%, 4%, and 2% of the total, respectively. Land cover change with agro-climatic zones, soil types, and slope classes was common in most part of the area and the conversion of grazing land into plantation trees and closure area development were major changes in the past 20 years.

scholarly journals Land use and Land cover Mapping Based on Normalized Difference Vegetation Index using Remote Sensing and Geographical Information System in Banjar River Watershed of Narmada Basin

2017 ◽  
Vol 12 (3) ◽  
pp. 678-684
Author(s):  
Jagriti Tiwari ◽  
S.K. Sharma ◽  
R.J. Patil
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Information System ◽  
Land Cover ◽  
Geographical Information System ◽  
Water Body ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Geographical Information ◽  
River Watershed

The spatial analysis of land use and land cover (LULC) dynamics is necessary for sustainable utilization and management of the land resources of an area. Remote sensing along with Geographical Information System emerged as an effective technique for mapping the LU/LC categories of an area in an efficient and cost-effective manner. The present study was conducted in Banjar river watershed located in Balaghat and Mandla district of Madhya Pradesh, India. The Normalized Difference Vegetation Index (NDVI) approach was adopted for LU/LC classification of study area. The Landsat-8 satellite data of year 2013 was selected for the classification purpose. The NDVI values were generated in ERDAS Imagine 2011 software and LU/LC map was prepared in ARC GIS environment. On the basis of NDVI values five LU/LC classes were recognized in the study area namely river & water body, waste land & habitation, forest, agriculture/other vegetation, open land/fallow land/barren land. The forest cover was found to be highly distributed in the study area with an extent of 115811 ha and least area was found to be covered under river and water body (4057.28 ha). This research work will be helpful for the policy makers for proper formulation and implementation of watershed developmental plans.

scholarly journals Anthropogenic Land Use/Cover Change Detection And Its Impacts On Hydrological Responses of Genale Catchment, Ethiopia

2021 ◽  
Author(s):  
Tufa Feyissa Negewo ◽  
Arup Kumar Sarma
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Water Body ◽  
Land Development ◽  
Geographical Information ◽  
Hydrological Response ◽  
Runoff Water ◽  
Lulc Change ◽  
Hydrological Responses

Abstract Change in land use land-cover (LULC) is a paramount dynamic present-day challenging landscape process capable of altering the hydrological responses in the catchment. As the land use planners require updated and high-resolution land resources information, understanding land cover change-induced status due to anthropogenic activities is significant. In this study, multitemporal cloud-free satellite imageries for periods (1990, 2002, and 2013) were used to quantify the spatiotemporal dynamics of land-use change detection and examine the effect on hydrological response using Geographical Information System (GIS) and Soil and Water Assessment Tool (SWAT) model in the Genale watershed, Ethiopia. The model performance was evaluated through sensitivity, uncertainty analysis, calibration, and validation process. The analysis of LULC change patterns for the area under study over 24 years showed that most parts of the green forest, barren land, and range shrubs were changed into agriculture, built up, wetlands, and water body with an increase of agriculture by 60%, built up 68%, pasture 37%, range shrubs 9%, and water body 57% over (1990 to 2013), which increased surface runoff, water yield, and sediment yield in the catchment. Significant changes in hydrological elements were observed at the sub-basins scale, mainly associated with the uneven spatial distribution of LULC changes compared to the whole watershed. The impacts of individual LULC change on hydrological response show a good correlation matrix. The regional government needs to modify land development policies and sustainable plans for examining LULC change detection using satellite imagery to avoid illegal land expansion activities.

scholarly journals New Change Detection Techniques to monitor land cover dynamics in mine environment

2014 ◽  
Vol XL-8 ◽  
pp. 875-879 ◽  
Author(s):  
S. Pathak
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Environmental Changes ◽  
Time Interval ◽  
Land Use Patterns ◽  
Detection Techniques ◽  
Use Patterns ◽  
Object Based ◽  
Ideal Situation

Land use and land cover are dynamic and is an important component in understanding the interactions of the human activities with the environment and thus it is necessary to simulate environmental changes. Land use/cover (LU/LC) change detection is very essential for better understanding of landuse dynamic during a known period of time for sustainable management. Mining is one of the most dynamic processes with direct as well as indirect impact on the environment. Hence, mine area provides ideal situation for evaluating the chronological changes in land-use patterns. Digital change detection of satellite data at different time interval helps in analyzing the changes in the spatial extent of mine along with the associated activities. In present study, various algorithms Iteratively Re-weighted Multivariate Alteration Detection (MAD) on raw data where class wise comparison becomes a difficult proposition and object based segmentation and change detection as post classification comparison were assessed.

scholarly journals Assessment of Land use /Land cover changes using Landsat data series: A case study of Chandauli district

2020 ◽  
Vol 66 (3) ◽  
pp. 298-305
Author(s):  
Kaushalendra Prakash Goswami ◽  
◽  
Sushil Kumar Yadav ◽  
Himanshu Shekher ◽  
◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Water Bodies ◽  
Land Cover Changes ◽  
Fallow Land ◽  
Land Use Land Cover ◽  
Dense Forest ◽  
Open Forest ◽  
Classi Cation

The rapid growth of population, urbanization, economic activities and natural phenomena have affected and simultaneously changed the land use land cover pattern. The main aim of this study is to gain a quantitative understanding of land use land cover changes in Chandauli district from 2000 to 2019. The maximum likelihood supervised classication in ERDAS imagine and ARC GIS software is applied in this study for the preparation of land use land cover maps and analysis of the pattern of land cover through satellite data for the years 2000, 2010 and 2019. The classication of land use land cover is divided into nine major classes i.e. water bodies, sand, cropland, built-up land, fallow land, wasteland, dense forest, open forest and scrub forest. Change detection analysis was also included in this analysis. The general pattern of LULC in this area includes an expansion of Fallow land (18.31 per cent), built-up land (13.43 per cent), open forest and water bodies as well as a reduction in the wasteland (12.59 percent) and dense forest areas in the reference period (2000-2019). The result also indicates that the dominating forest cover exists in southern Chandauli district. The mapping of land use land cover classes is also helpful in the study of change detection and natural resource management.

scholarly journals Land use/Land Cover and Change Detection in Chikodi Taluk, Belagavi District, Karnataka using Object based Image Classification

2019 ◽  
Vol 9 (1) ◽  
pp. 1222-1227
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Image Classification ◽  
Water Body ◽  
Accuracy Assessment ◽  
Confusion Matrix ◽  
Google Earth ◽  
Land Use Land Cover ◽  
Object Based

The study examines land use land cover and change detection in Chikodi taluk, Belagavi district, Karnataka. Land use land cover plays an important role in the study of global change. Due to fast urbanization there is variation in natural resources such as water body, agriculture, wasteland land etc. These environment problems are related to land use land cover changes. And for the sustainable development it is mandatory to know the interaction of human activities with the environment and to monitor the change detection. In present study for image classification Object Based Image Analysis (OBIA) method was adapted using multi-resolution segmentation for the year 1992, 1999 and 2019 imagery and classified into four different classes such as agriculture, built-up, wasteland and water-body. Random points (200) were generated in ArcGIS environment and converted points into KML layer in order to open in Google Earth. For the accuracy assessment confusion matrix was generated and result shows that overall accuracy of land use land cover for 2019 is 83% and Kappa coefficient is 0.74 which is acceptable. These outcomes of the result can provide critical input to decision making environmental management and planning the future.

scholarly journals An Automatic and Operational Method for Land Cover Change Detection Using Spatiotemporal Analysis of MODIS Data: A Northern Ontario (Canada) Case Study

2021 ◽  
Vol 10 (5) ◽  
pp. 325
Author(s):  
Ima Ituen ◽  
Baoxin Hu
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Basic Unit ◽  
Operational Method ◽  
Northern Ontario ◽  
Modis Data ◽  
Forest Fire Management ◽  
Land Cover Change Detection

Mapping and understanding the differences in land cover and land use over time is an essential component of decision-making in sectors such as resource management, urban planning, and forest fire management, as well as in tracking of the impacts of climate change. Existing methods sometimes pose a barrier to the effective monitoring of changes in land cover and land use, since a threshold parameter is often needed and determined based on trial and error. This study aimed to develop an automatic and operational method for change detection on a large scale from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Super pixels were the basic unit of analysis instead of traditional individual pixels. T2 tests based on the feature vectors of temporal Normalized Difference Vegetation Index (NDVI) and land surface temperature were used for change detection. The developed method was applied to data over a predominantly vegetated area in northern Ontario, Canada spanning 120,000 sq. km from 2001–2016. The accuracies ranged between 78% and 88% for the NDVI-based test, from 74% to 86% for the LST-based test, and from 70% to 86% for the joint method compared with manual interpretation. Our proposed method for detecting land cover change provides a functional and viable alternative to existing methods of land cover change detection as it is reliable, repeatable, and free from uncertainty in establishing a threshold for change.

scholarly journals Evaluating the Potentiality of Sentinel-2 for Change Detection Analysis Associated to LULUCF in Wallonia, Belgium

Land ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 55
Author(s):  
Odile Close ◽  
Sophie Petit ◽  
Benjamin Beaumont ◽  
Eric Hallot
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Change Detection ◽  
Signal To Noise Ratio ◽  
Land Conversion ◽  
Sustainable Resources ◽  
Detection Techniques ◽  
Detection Analysis ◽  
Conversion Rates ◽  
Sentinel 2

Land Use/Cover changes are crucial for the use of sustainable resources and the delivery of ecosystem services. They play an important contribution in the climate change mitigation due to their ability to emit and remove greenhouse gas from the atmosphere. These emissions/removals are subject to an inventory which must be reported annually under the United Nations Framework Convention on Climate Change. This study investigates the use of Sentinel-2 data for analysing lands conversion associated to Land Use, Land Use Change and Forestry sector in the Wallonia region (southern Belgium). This region is characterized by one of the lowest conversion rates across European countries, which constitutes a particular challenge in identifying land changes. The proposed research tests the most commonly used change detection techniques on a bi-temporal and multi-temporal set of mosaics of Sentinel-2 data from the years 2016 and 2018. Our results reveal that land conversion is a very rare phenomenon in Wallonia. All the change detection techniques tested have been found to substantially overestimate the changes. In spite of this moderate results our study has demonstrated the potential of Sentinel-2 regarding land conversion. However, in this specific context of very low magnitude of land conversion in Wallonia, change detection techniques appear to be not sufficient to exceed the signal to noise ratio.

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