scholarly journals Land and Forest Management by Land Use/ Land Cover Analysis and Change Detection Using Remote Sensing and GIS

2016 ◽  
Vol 9 (1) ◽  
pp. 63-77 ◽  
Author(s):  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Forest Management ◽  
Land Cover ◽  
Spatial Data ◽  
Management Strategies ◽  
Geographical Information ◽  
Spatial Data Analysis ◽  
Study Region ◽  
Rugged Terrain

Abstract Remote sensing and Geographical Information System (GIS) are the most effective tools in spatial data analysis. Natural resources like land, forest and water, these techniques have proved a valuable source of information generation as well as in the management and planning purposes. This study aims to suggest possible land and forest management strategies in Chakia tahsil based on land use and land cover analysis and the changing pattern observed during the last ten years. The population of Chakia tahsil is mainly rural in nature. The study has revealed that the northern part of the region, which offers for the settlement and all the agricultural practices constitutes nearly 23.48% and is a dead level plain, whereas the southern part, which constitute nearly 76.6% of the region is characterized by plateau and is covered with forest. The southern plateau rises abruptly from the northern alluvial plain with a number of escarpments. The contour line of 100 m mainly demarcates the boundary between plateau and plain. The plateau zone is deeply dissected and highly rugged terrain. The resultant topography comprises of a number of mesas and isolated hillocks showing elevation differences from 150 m to 385 m above mean sea level. Being rugged terrain in the southern part, nowadays human encroachment are taking place for more land for the cultivation. The changes were well observed in the land use and land cover in the study region. A large part of fallow land and open forest were converted into cultivated land.

scholarly journals REMOTE SENSING ANALYSIS OF THE CHANGES IN VEGETATED COASTLAND: THE CASE OF SOUTHEASTERN EUROPE

2021 ◽  
Vol V-4-2021 ◽  
pp. 55-59
Author(s):  
K. Laze
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Spatial Data ◽  
Methodological Approach ◽  
Driving Factors ◽  
Spatial Data Analysis ◽  
Study Region ◽  
Spatial Changes ◽  
Explicit Analysis ◽  
Abiotic And Biotic Factors

Abstract. The coastlands are changing in the Southeastern European countries. Yet, temporal and spatial changes in natural-vegetated land, urban-vegetated land and agriculture-vegetated land use on coastal surfaces, respectively, are missing. The aim is to understand the changes in vegetated land on the coastal surface by retrieving spatial data from remote sensing using a new approach by reducing errors in data processed and by collecting data using surveys to understand the driving (abiotic and biotic) factors of vegetated land changes and land dynamics at regional level. These spatial and survey data are then to be employed at spatially explicit analysis to define driving factors, plausibly explaining spatial and temporal changes of vegetated land on coastal surfaces in the Southeastern European study region including Albania, Bosnia and Herzegovina, Croatia, and Montenegro and Slovenia for the last fifty years. The expected main result is a new methodological approach of remote sensing spatial data analysis by reducing data errors and by identifying driving factors of the changes in vegetated land. The findings may serve to understand the effects of land management and land use on the changes in vegetated land-use on coastal surfaces, and to potentially uncover the least disturbed vegetated land areas by human intervention for vegetation conservation.

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 Land Use and Land Cover Change Detection and Prediction in the Kathmandu District of Nepal Using Remote Sensing and GIS

2020 ◽  
Vol 12 (9) ◽  
pp. 3925 ◽  
Author(s):  
Sonam Wangyel Wang ◽  
Belay Manjur Gebru ◽  
Munkhnasan Lamchin ◽  
Rijan Bhakta Kayastha ◽  
Woo-Kyun Lee
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Agricultural Land ◽  
Water Bodies ◽  
Geographical Information ◽  
Land Cover Changes ◽  
Rapid Urbanization ◽  
Urbanized Areas

Understanding land use and land cover changes has become a necessity in managing and monitoring natural resources and development especially urban planning. Remote sensing and geographical information systems are proven tools for assessing land use and land cover changes that help planners to advance sustainability. Our study used remote sensing and geographical information system to detect and predict land use and land cover changes in one of the world’s most vulnerable and rapidly growing city of Kathmandu in Nepal. We found that over a period of 20 years (from 1990 to 2010), the Kathmandu district has lost 9.28% of its forests, 9.80% of its agricultural land and 77% of its water bodies. Significant amounts of these losses have been absorbed by the expanding urbanized areas, which has gained 52.47% of land. Predictions of land use and land cover change trends for 2030 show worsening trends with forest, agriculture and water bodies to decrease by an additional 14.43%, 16.67% and 25.83%, respectively. The highest gain in 2030 is predicted for urbanized areas at 18.55%. Rapid urbanization—coupled with lack of proper planning and high rural-urban migration—is the key driver of these changes. These changes are associated with loss of ecosystem services which will negatively impact human wellbeing in the city. We recommend city planners to mainstream ecosystem-based adaptation and mitigation into urban plans supported by strong policy and funds.

scholarly journals Classification of Land Use/Land Cover Changes Using GIS and Remote Sensing Technique in Lake Kenyir Basin, Terengganu, Malaysia

2018 ◽  
Vol 7 (3.14) ◽  
pp. 12 ◽  
Author(s):  
Mohd Khairul Amri Kamarudin ◽  
Kabir Abdulkadir Gidado ◽  
Mohd Ekhwan Toriman ◽  
Hafizan Juahir ◽  
Roslan Umar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Water Body ◽  
Geographical Information ◽  
Landsat Images ◽  
Gis And Remote Sensing ◽  
Remote Sensing Technique ◽  
Multi Temporal ◽  
Vegetation Water

Geographical information system (GIS) techniques and Remote Sensing (RS) data are fundamental in the study of land use (LU) and land cover (LC) changes and classification. The aim of this study is to map and classify the LU and LC change of Lake Kenyir Basin within 40 years’ period (1976 to 2016). Multi-temporal Landsat images used are MSS 1976, 1989, ETM+ 2001 and OLI 8 2016. Supervised Classification on Maximum Likelihood Algorithm method was used in ArcGIS 10.3. The result shows three classes of LU and LC via vegetation, water body and built up area. Vegetation, which is the dominant LC found to be 100%, 88.83%, 86.15%, 81.91% in 1976, 1989, 2001 and 2016 respectively. While water body accounts for 0%, 11.17%, 12.36% and 13.62% in the years 1976, 1989, 2001 and 2016 respectively and built-up area 1.49% and 4.47 in 2001 and 2016 respectively. The predominant LC changes in the study are the water body and vegetation, the earlier increasing rapidly at the expense of the later. Therefore, proper monitoring, policies that integrate conservation of the environment are strongly recommended. 

scholarly journals Analysis of Spatiotemporal Land Use and Land Cover Changes using Remote Sensing and GIS: A Review

2018 ◽  
Vol 7 (4.34) ◽  
pp. 159
Author(s):  
Kabir Abdulkadir Gidado ◽  
Mohd Khairul Amri Kamarudin ◽  
Nik Ahmad Firdausaq ◽  
Aliyu Muhammad Nalado ◽  
Ahmad Shakir Mohd Saudi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Agricultural Land ◽  
Accuracy Assessment ◽  
Geographical Information ◽  
Lulc Changes ◽  
Detection Analysis ◽  
Socio Economic Factors

The land-use and land-cover (LULC) pattern of an area is an outcome of natural and socio-economic factors and their use spatially by man; this LULC varies from the forest, water body, agricultural land and so on. Remote Sensing (RS) and Geographical Information System (GIS) studies have predominantly focused on providing the technical knowledge of, where, and the type of LULC change that has occurred and its impacts on man and the environment. Knowledge about LULC changes is essential for understanding the relationships and interfaces between humans and the natural environment. The purpose of this article is to review the previous studies of the spatiotemporal LULC changes. However, thirty (30) articles were reviewed from 2011 to 2017. However, these articles studied the LULC, classification, changes and change detection analysis, using different methods and software of RS and G.I.S. The finding shows that these articles have overall accuracy assessment ranges from 75% to 95% validations. Also, supervised classification in Maximum Likelihood Algorithm method was mostly employed for the LULC classification. Moreover, these reviewed articles confirmed that LULC changes are imminent as a result of both natural and human factors which lead to increase and decrease of one LULC cover to another. Therefore proper monitoring of LULC changes when applied help the relevant government bodies, agencies and environmental managers utilise the environment to the fullest.  

scholarly journals Evaluating Land Use and Land Cover Change in the Gaborone Dam Catchment, Botswana, from 1984–2015 Using GIS and Remote Sensing

2019 ◽  
Vol 11 (19) ◽  
pp. 5174 ◽  
Author(s):  
Botlhe Matlhodi ◽  
Piet K. Kenabatho ◽  
Bhagabat P. Parida ◽  
Joyce G. Maphanyane
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Water Resource Management ◽  
Driving Forces ◽  
Landsat Imagery ◽  
Global Environmental Change ◽  
Geographical Information ◽  
Lulc Changes ◽  
Bare Land

Land use land cover (LULC) change is one of the major driving forces of global environmental change in many developing countries. In this study, LULC changes were evaluated in the Gaborone dam catchment in Botswana between 1984 and 2015. The catchment is a major source of water supply to Gaborone city and its surrounding areas. The study employed Remote Sensing and Geographical Information System (GIS) using Landsat imagery of 1984, 1995, 2005 and 2015. Image classification for each of these imageries was done through supervised classification using the Maximum Likelihood Classifier. Six major LULC categories, cropland, bare land, shrub land, built-up area, tree savanna and water bodies, were identified in the catchment. It was observed that shrub land and tree savanna were the major LULC categories between 1984 and 2005 while shrub land and cropland dominated the catchment area in 2015. The rates of change were generally faster in the 1995–2005 and 2005–2015 periods. For these periods, built-up areas increased by 59.8 km2 (108.3%) and 113.2 km2 (98.5%), respectively, while bare land increased by 50.3 km2 (161.1%) and 99.1 km2 (121.5%). However, in the overall period between 1984 and 2015, significant losses were observed for shrub land, 763 km2 (29.4%) and tree savanna, 674 km2 (71.3%). The results suggest the need to closely monitor LULC changes at a catchment scale to facilitate water resource management and to maintain a sustainable environment.

scholarly journals Research on Urban Carrying Capacity Based on Multisource Data Fusion—A Case Study of Shanghai

2021 ◽  
Vol 13 (14) ◽  
pp. 2695
Author(s):  
Xiangyang Cao ◽  
Yishao Shi ◽  
Liangliang Zhou
Keyword(s):  
Land Use ◽  
Data Fusion ◽  
Spatial Pattern ◽  
Spatial Data ◽  
Carrying Capacity ◽  
Urban Spaces ◽  
Geographical Information ◽  
Spatial Data Analysis ◽  
Spatial Pattern Analysis ◽  
Local Conditions

Taking Shanghai as an example, this paper uses remote sensing (RS) and geographical information systems (GIS) technology to conduct multisource data fusion and a spatial pattern analysis of urban carrying capacity at the micro scale. The main conclusions are as follows: (1) based on the “production, living and ecology” land functions framework and land use data, Shanghai is divided into seven types of urban spaces to reveal their heterogeneity and compatibility in terms of land use functions. (2) We propose an urban carrying capacity coupling model (UCCCM) based on multisource data. The model incorporates threshold and saturation effects, which improve its power to explain urban carrying capacity. (3) Using the exploratory spatial data analysis (ESDA) technique, this paper studies the spatial pattern of carrying capacity in different urban spaces of Shanghai. (4) We analyse the causes of the cold spots in each urban space and propose strategies to improve the urban carrying capacity according to local conditions.

scholarly journals Assessment of Land Use And Land Cover Change Detection And Prediction Using Remote Sensing And CA Markov In The Northern Coastal Districts of Tamil Nadu, India

2021 ◽  
Author(s):  
Devanantham abijith ◽  
Subbarayan Saravanan
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Environmental Sustainability ◽  
Tamil Nadu ◽  
Google Earth ◽  
Geographical Information ◽  
Change Analysis ◽  
Lulc Change ◽  
Study Region ◽  
Barren Land

Abstract Land use and land cover (LULC) change analysis and forecasting aids the upcoming generation in research and evaluate the global climate change for managing and controlling environmental sustainability. This research analyzes the Northern TN coast, which is under both natural and anthropogenic stress. The analysis of LULC changes and LULC projections for the region between 2009-2019 and 2019-2030 was performed utilizing Google Earth Engine (GEE), TerrSet, and Geographical Information System (GIS) tools. LULC image is generated from Landsat images and classified in GEE using Random Forest (RF). LULC maps were then framed with the CA- Markov model to forecast future LULC change. The CA-Markov’s Land change modeler (LCM) was set up to create future LULC. It was carried out in four steps: (1) Change analysis, (2) Transition potential, (3) Change prediction, and (4) Model validation. For analyzing change statistics, the study region is divided into zone 1 and zone 2. In both zones, the water body shows a decreasing trend, and built-up areas are in increasing trend. Barren land and vegetation classes are under stress and developing into built-up. The overall accuracy was above 89%, and the kappa coefficient was above 87% for all three years. This region is highly susceptible to inland floods, coastal floods, and other natural disasters; thus, this study’s results support future development plans and decision-making.

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