Land use/land cover and land surface temperature analysis in Wayanad district, India, using satellite imagery
02 Mar 2020-Annals of Gis: Geographic Information Sciences (Taylor & Francis)-Vol. 26, Iss: 4, pp 343-360
TL;DR: Land Use/Land Cover (LULC) classification and Land Surface Temperature (LST) in Wayanad district during the years 2004 and 2018 are assessed to help the local authorities to implement urban planning regulations for public awareness and policy makers for a sustainable planning and management in forthcoming years.
Abstract: This paper assesses Land Use/Land Cover (LULC) classification and Land Surface Temperature (LST) in Wayanad district during the years 2004 and 2018. The LULC classification of Wayanad district is i...
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21 Dec 1979
TL;DR: The human species has been altering the environment over large geographic areas since the domestication of fire, plants, and animals as mentioned in this paper, and the progression from hunter to farmer to technologist has increased the variety and pace more than the geographic extent of human impact on the environment.
Abstract: The human species has been altering the environment over large geographic areas since the domestication of fire, plants, and animals. The progression from hunter to farmer to technologist has increased the variety and pace more than the geographic extent of human impact on the environment. A number of regions of the earth have experienced significant climatic changes closely related in time to anthropogenic environmental changes. Plausible physical models suggest a causal connection. The magnitudes of probable anthropogenic global albedo changes over the past millennia (and particularly over the past 25 years) are estimated. The results suggest that humans have made substantial contributions to global climate changes during the past several millennia, and perhaps over the past million years; further such changes are now under way. 32 references.
238 citations
07 Jul 2021
TL;DR: In this article, the authors used Remote Sensing (RS) and Geographic Information System (GIS) techniques to evaluate the impact of land use change on the land surface temperature.
Abstract: Urbanization leads to the construction of various urban infrastructures in the city area for residency, transportation, industry, and other purposes, which causes major land use change. Consequently, it substantially affects Land Surface Temperature (LST) by unbalancing the surface energy budget. Higher LST in city areas decreases human thermal comfort for the city dwellers and affects the urban environment and ecosystem. Therefore, a comprehensive investigation is needed to evaluate the impact of land use change on the LST. Remote Sensing (RS) and Geographic Information System (GIS) techniques were used for the detailed investigation. RS data for the years 1993, 2007 and 2020 during summer (March–May) in Dhaka city were used to prepare land cover maps, analyze LST, generate hazard maps and relate the land cover change with LST by using GIS. The results show that the built-up area in Dhaka city increased by 67% from 1993 to 2020 by replacing lowland mainly, followed by vegetation, bare soil and water bodies. LSTs found in the study area were ranged from 23.26 to 39.94 °C, 23.69 to 43.35 °C and 24.44 to 44.58 °C for the years 1993, 2007 and 2020, respectively. The increases of spatially distributed maximum and mean LST were found 4.62 °C and 6.43 °C, respectively, for the study period of 27 years while the change in minimum LST was not substantial. LST increased by around 0.24 °C per year and human thermal discomfort shifted from moderate to strong heat stress for the total study period due to the increase of built-up and bare lands. This study also shows that normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were negatively correlated with LST while normalized difference built-up Index (NDBI) and normalized difference built-up Index (NDBAI) were positively correlated with LST. The methodology developed in this study can be adapted to other cities around the globe.
50 citations
TL;DR: In this paper , support vector machine (SVM) and artificial neural network (ANN) algorithms were used to analyze and predict, respectively, using Landsat 4-5 and 8 images from 1991 to 2021 at 10 years interval.
17 citations
TL;DR: In this article, the authors examined the spatiotemporal variability of land use/land cover changes (LULC), land surface temperature (LST), and heat island (HI) in northwestern Bangladesh.
Abstract: The study examined the spatiotemporal variability of land use/land cover changes (LULC), land surface temperature (LST), and heat island (HI) in northwestern Bangladesh. Landsat images were used for evaluating LULC, LST, and HI for the years 1990, 2002, 2014, and 2018. Unsupervised and index-based classification approaches were used for mapping LULC. The mono-window algorithm was employed to identify the spatiotemporal variability of LST and HI. The analysis suggested that water bodies, forests, and bare land dwindled during these 28 years with an average of about 40%, 70%, and 45%, respectively. Agricultural land had been expanded from 1990 to 2002 and gradually stabilized in recent decades. Settlement areas increased alarmingly from 1990 to 2018. The water bodies, forests and bare lands were reduced due to the widening of agricultural land and rapid growth of urban area. The extents of the HI were found to be spreading out and became most extensive in 2018. LST had risen by around 5.5 °C from 1990 to 2018. The lower temperature zones prevailed in the water bodies, forests and agricultural lands whereas higher temperature zones were visible in the river sand bars and highly urbanized areas. The method used in this study is very successful in sparse built-up areas. The outputs of the study will be a great input in the city masterplan for landscape optimization and urban ecological balance in the study area and provide baseline information for future researches looking for inspecting the impacts of LULC change on a regional scale in plainland regions. Highlights
• Spatiotemporal dynamics of LULC were evaluated in the northwest region of Bangladesh • Heat islands were delineated successfully indicating the rapid growth of urbanization • Enlargement of the urban area is the main cause for the increasing LST phenomenon • The rate of HI expansion validated by the changing thematic areas of LULC • Trends of urbanization and HI growth are alarming within the district town areas.
12 citations
TL;DR: In this article, the authors investigated urban sprawl in Thiruvananthapuram Urban Agglomeration (UA) and attempts to delineate Urban Growth Boundary (UGB) for promoting urban sustenance.
Abstract: This research paper investigates urban sprawl in Thiruvananthapuram Urban Agglomeration (UA) and attempts to delineate Urban Growth Boundary (UGB) for promoting urban sustenance. A 112% rise in the spatial expanse of Thiruvananthapuram UA from 256.22 km2 in 2001 to 542.57 km2 in 2011 might induce urban sprawl in the peripheral areas. The Landsat satellite imagery for the years 1987, 1997, 2007, and 2017 were extracted to examine the spatiotemporal urban growth pattern. Shannon’s entropy index was employed to detect urban sprawl in Thiruvananthapuram UA. The UGB delineation process involved future urban growth prediction using the MOLUSCE (Modules for Land Use Change Simulations) plug-in of QGIS software. ANN-MLP (Artificial Neural Network-Multi Layer Perceptron) and CA (Cellular Automata) model was preferred in MOLUSCE to predict future urban growth for the year 2027. Thereafter, hexagons of one square kilometer were used to demarcate the Contiguous built-up Growth Boundary (CGB), and later, sub-administrative units were selected to delineate UGB. The results revealed a rise in the built-up areas from 36.04 km2 in 1987 to 140.69 km2 in 2017. Shannon’s entropy index indicated the prevalence of urban sprawl in Thiruvananthapuram UA. The future growth prediction by 2027 exhibited a further rise in built-up areas to 173.31 km2. The total area within CGB is 213.58 km2, while UGB accounted for 355.59 km2, which included 16 sub-administrative units. This study exhibited a unique methodology to delineate the urban growth boundary, which optimizes the future land requirements in developing nations.
11 citations
References
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17 Sep 1998
TL;DR: This chapter discusses Accuracy Assessment, which examines the impact of sample design on cost, statistical Validity, and measuring Variability in the context of data collection and analysis.
Abstract: Introduction Why Accuracy Assessment? Overview Historical Review Aerial Photography Digital Assessments Data Collection Considerations Classification Scheme Statistical Considerations Data Distribution Randomness Spatial Autocorrelation Sample Size Sampling Scheme Sample Unit Reference Data Collection Basic Collection Forms Basic Analysis Techniques Non-Site Specific Assessments Site Specific Assessments Area Estimation/Correction Practicals Impact of Sample Design on Cost Recommendations for Collecting Reference Data ASources of Variation in Reference Data Photo Interpretation vs. Ground Visitation Interpreter Variability Observations vs. Measurements What is Correct? Labeling Map vs. Labeling the Reference Data Qualitative vs. Quantitative Analysis Local vs. Regional vs. Global Assessments Advanced Topics Beyond the Error Matrix Modifying the Error Matrix Fuzzy Set Theory Measuring Variability Complex Data Sets Change Detection Multi-Layer Assessments California Hardwood Rangeland Monitoring Project Case Study Balancing Statistical Validity with Practical Reality Bibliography
4,586 citations
TL;DR: The Normalized Difference Water Index (NDWI) as mentioned in this paper is a new method that has been developed to delineate open water features and enhance their presence in remotely-sensed digital imagery.
Abstract: The Normalized Difference Water Index (NDWI) is a new method that has been developed to delineate open water features and enhance their presence in remotely-sensed digital imagery. The NDWI makes use of reflected near-infrared radiation and visible green light to enhance the presence of such features while eliminating the presence of soil and terrestrial vegetation features. It is suggested that the NDWI may also provide researchers with turbidity estimations of water bodies using remotely-sensed digital data.
4,353 citations
"Land use/land cover and land surfac..." refers background or methods in this paper
...Water Index (NDWI) (McFeeters 1996), Green Normalized Difference in Vegetation Index (GNDVI) (Buschmann and Nagel 1993), Vegetation Index Green (VI green) (Gitelson...
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...The NDWI is identified to monitor the presence of water bodies....
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...The equations used for estimation of various vegetation indices are given below: NDVI ¼ ρnir ρred ρnir þ ρred (5) MNDWI ¼ ρgreen ρnir ρgreenþ ρni (6) DVI ¼ ρnir ρred (7) GNDVI ¼ ρnir ρgreen ρnir þ ρgreen (8) VIgreen ¼ ρgreen ρred ρgreenþ ρred (9) NR ¼ ρred ρgreenþ ρred þ ρnir (10) NG ¼ ρgeen ρgreenþ ρred þ ρnir (11) NNIR ¼ ρnir ρgreenþ ρred þ ρnir (12)...
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...Soil may exhibit nearby zero value, and all water body features have negative values (McFeeters 1996)....
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...NDWI 2004. areas....
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TL;DR: It is likely that it is unlikely that a single standardized method of accuracy assessment and reporting can be identified, but some possible directions for future research that may facilitate accuracy assessment are highlighted.
3,800 citations
"Land use/land cover and land surfac..." refers background or methods in this paper
...study, we are using error matrix or confusion matrix technique (Foody 2002) for post-classification comparison method....
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...Radiometric correction is performed using Landsat toolbox developed for ArcGIS 10.1 (Foody 2002) to correct distortions occurred in ETM+ satellite imageries....
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...In this study, we are using error matrix or confusion matrix technique (Foody 2002) for post-classification comparison method....
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...1 (Foody 2002) to correct distortions occurred in ETM+ satellite imageries....
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28 Nov 2003
TL;DR: In this article, the authors highlight the complexity of land-use/cover change and propose a framework for a more general understanding of the issue, with emphasis on tropical regions, and argue that a systematic analysis of local-scale land use change studies, conducted over a range of timescales, helps to uncover general principles that provide an explanation and prediction of new land use changes.
Abstract: We highlight the complexity of land-use/cover change and propose a framework for a more general understanding of the issue, with emphasis on tropical regions. The review summarizes recent estimates on changes in cropland, agricultural intensification, tropical deforestation, pasture expansion, and urbanization and identifies the still unmeasured land-cover changes. Climate-driven land-cover modifications interact with land-use changes. Land-use change is driven by synergetic factor combinations of resource scarcity leading to an increase in the pressure of production on resources, changing opportunities created by markets, outside policy intervention, loss of adaptive capacity, and changes in social organization and attitudes. The changes in ecosystem goods and services that result from land-use change feed back on the drivers of land-use change. A restricted set of dominant pathways of land-use change is identified. Land-use change can be understood using the concepts of complex adaptive systems and transitions. Integrated, place-based research on land-use/land-cover change requires a combination of the agent-based systems and narrative perspectives of understanding. We argue in this paper that a systematic analysis of local-scale land-use change studies, conducted over a range of timescales, helps to uncover general principles that provide an explanation and prediction of new land-use changes.
2,491 citations
TL;DR: The Random Forest classifier uses bagging, or bootstrap aggregating, to form an ensemble of classification and regression tree (CART)-like classifiers, which is computationally much lighter than methods based on boosting and somewhat lighter than simple bagging.
1,634 citations
"Land use/land cover and land surfac..." refers background in this paper
...…Camps-Valls, and Bruzzone 2009), Decision Trees (McIver and Friedl 2002; Jiang et al. 2012), Boosted Trees (Friedl and Brodley 1997), Random Forest (Gislason, Benediktsson, and Sveinsson 2006)) of supervised and two types (k-means (Rollet et al. 1998; Blanzieri and Melgani 2008) and ISO-data…...
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...There are seven types (Maximum Likelihood Classifier, MLC) (Settle and Briggs 1987; Shalaby and Tateishi 2007), Naive Bayes Classifier (Minimum Distance-to-Means Classifier) (Atkinson and Lewis 2000), Mahalanobis Distance Classifier (Deer and Eklund 2003), Neural Networks (Kavzoglu and Mather 2003) Support Vector Machines (SVM) (Huang, Davis, and Townshend 2002; Pal and Mather 2005; Marconcini, Camps-Valls, and Bruzzone 2009), Decision Trees (McIver and Friedl 2002; Jiang et al. 2012), Boosted Trees (Friedl and Brodley 1997), Random Forest (Gislason, Benediktsson, and Sveinsson 2006)) of supervised and two types (k-means (Rollet et al. 1998; Blanzieri and Melgani 2008) and ISO-data (Dhodhi et al. 1999) of unsupervised techniques available in Envisat and are most extensively used techniques....
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