Land Cover Change Mapping Research Articles

PERUBAHAN TUTUPAN LAHAN HUTAN DI WILAYAH DAERAH ALIRAN SUNGAI CITARUM HULU PROVINSI JAWA BARAT TAHUN 2009-2017 MENGGUNAKAN CITRA LANDSAT DAN SISTEM INFORMASI GEOGRAFIS

Land use and land cover are important components in understanding the interaction of human activities with the environment. Forest management including planning and supervision in the Upper Citarum Watershed Area of West Java is very important to be done to prevent more severe forest degradation. The purpose of this study is to measure land use change from 2009, 2013 and 2017. This research method is to utilize remote sensing technology and geographic information system (GIS) and then compare 3 map of land cover change. The classification results show that the Citarum watershed area has 13 classes of land cover. The significant land cover change from 2009 to 2017 forest land cover decreased from 64,938.99 Ha to 43,268.00. Th e rice field land cover class is scattered throughout the upper Citarum watershed area. Bandung District , West Bandung District , Bandung City, Cimahi City, Sumedang District and a small part of Garut

Corrections to “Using Land Long Term Data Records to Map Land Cover Changes in China over 1981--2010”

Presents corrections to maps for the paper, [“Using land long term data records to map land cover changes in China over 1981–2010,” (Li, J. et al), IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 10, no. 4, pp. 1372–1389, Apr. 2017.

Land use and land cover change detection using geospatial techniques in the Sikkim Himalaya, India

Mapping and monitoring of land use land cover (LULC) changes in the Himalayas is vital for sustainable development, planning and management. Based on remote sensing (RS) and geographic information system (GIS) techniques, the study is an attempt to monitor the changes in LULC patterns of Rani Khola watershed of Sikkim Himalaya for the periods 1988–1996, 1996–2008 and 2008–2017. Images from Landsat-5 Thematic Mapper (TM) and Sentinel 2A (Multispectral Instrument) MSI data were used to extract land cover maps. Supervised classification using Maximum Likelihood Classifier (MLC) was applied to prepare LULC maps of the watershed. The accuracy of the classified map was assessed using a High Resolution Planet scope image and ground realities have been verified and ascertained through field observations and site specific interviews. As a result of policy changes and traditional agroforestry systems, LULC in the study watershed has undergone a series of complicated changes over the past three decades. Six major LULC classes viz; agriculture, barren land, built-up area, dense forest, open forests and water bodies have been identified and indicate that major land use in the watershed is forestry. Results shows, dense forest, built-up area and water bodies have increased by 16.40% (41.76 km2), 2.13% (5.41 km2) and 0.11% (0.28 km2) while open forest, agriculture and barren land have decreased by −13.98% (−35.59 km2), 2.83% (−7.22 km2) and −1.82% (0.4.64 km2) respectively. The analysis and findings of the study highlights important policy implications for the sustainable LULC management in the Rani Khola watershed of the Sikkim Himalaya.

Mapping land cover change in northern Brazil with limited training data

Deforestation in the Amazon has important implications for biodiversity and climate change. However, land cover monitoring in this tropical forest is a challenge because it covers such a large area and the land cover change often occurs quickly, and sometimes cyclically. Here we adapt a method which eliminates the need to collect new training data samples for each update of an existing land cover map. We use the state-of-the-art probabilistic classifier Import Vector Machines and Landsat 8 Operational Land Imager (OLI) scenes of the area surrounding Novo Progresso, northern Brazil, to create an initial land cover map for 2013 with associated classification probabilities. We then conduct spectral change detection between 2013 and 2015 using a pair of Landsat images in order to identify the areas where land cover has changed between the two dates, and then reclassify these areas using a supervised classification algorithm, using pixels from the unchanged areas of the map as training data. In this study, we use the pixels with the highest classification probabilities to train the classifier for 2015 and compare the results to those obtained when pixels are chosen randomly. The use of probabilities in the selection of training samples improves the results compared to a random selection, with the highest overall accuracy achieved when 250 training samples with high probabilities are used. For training sample sizes greater than 1000, the differences in overall accuracy between the two approaches to training sample selection are reduced. The final updated 2015 map has an overall accuracy of 80.1%, compared to an overall accuracy of 82.5% for the 2013 map. The results show that this probabilistic method has potential to efficiently map the dynamic land cover change in the Amazon with limited training data, although some challenges remain.

Satellite-Based Assessment of Grassland Conversion and Related Fire Disturbance in the Kenai Peninsula, Alaska.

Spruce beetle-induced (Dendroctonus rufipennis (Kirby)) mortality on the Kenai Peninsula has heightened local wildfire risk as canopy loss facilitates the conversion from bare to fire-prone grassland. We collected images from NASA satellite-based Earth observations to visualize land cover succession at roughly five-year intervals following a severe, mid-1990’s beetle infestation to the present. We classified these data by vegetation cover type to quantify grassland encroachment patterns over time. Raster band math provided a change detection analysis on the land cover classifications. Results indicate the highest wildfire risk is linked to herbaceous and black spruce land cover types, The resulting land cover change image will give the Kenai National Wildlife Refuge (KENWR) ecologists a better understanding of where forests have converted to grassland since the 1990s. These classifications provided a foundation for us to integrate digital elevation models (DEMs), temperature, and historical fire data into a model using Python for assessing and mapping changes in wildfire risk. Spatial representations of this risk will contribute to a better understanding of ecological trajectories of beetle-affected landscapes, thereby informing management decisions at KENWR.

Optimal Methodology for Detecting Land Cover Change in a Forestry, Lakeside Environment Using NAIP Imagery

Mapping land cover change is useful for various environmental and urban planning applications, e.g. land management, forest conservation, ecological assessment, transportation planning, and impervious surface control. As the optimal change detection approaches, algorithms, and parameters often depend on the phenomenon of interest and the remote sensing imagery used, the goal of this study is to find the optimal procedure for detecting urban growth in rural, forestry areas using one-meter, four-band NAIP images. Focusing on different types of impervious covers, the authors test the optimal segmentation parameters for object-based image analysis, and conclude that the random tree classifier, among the six classifiers compared, is most optimal for land use/cover change detection analysis with a satisfying overall accuracy of 87.7%. With continuous free coverage of NAIP images, the optimal change detection procedure concluded in this study is valuable for future analyses of urban growth change detection in rural, forestry environments.

Monitoring of secondary forest succession on abandoned farmland using LiDAR point clouds

The purpose of the study was an assessment of LiDAR point clouds for automating the mapping of land use and land cover changes, mainly land abandonment and the process of secondary forest succession. Detailed information about land cover was determined based on airborne laser scanning data. The presented study focuses on the analysis of the spatial range and structure of vegetation. The study area was located in Milicz district in the voivodeship of Lower Silesia – the central west part of Poland. The areas of interest were parcels where agricultural land had been abandoned and forest succession processes had progressed. Analysis of the spatial range of the secondary forest succession was carried out using a reclassified nDSM. Reclassification of the nDSM was done using > 1 m, > 2 m and > 3 m for the pixel values, representing the height of vegetation above the ground. Parameters such as height of vegetation, standard deviation of height and cover density were calculated, to show the process of the increase in forest succession on abandoned agricultural land. The results confirmed a discrepancy between the cadastral data and the actual use of the plots. In the study area, more than three times as much forested and wooded area was detected than had been recorded in official databases. Analyses based on airborne laser scanning point clouds indicated significant diversity in the vertical and horizontal structure of vegetation. The results demonstrated gradual succession of greenery in the research area.

Assessing the accuracy of detected breaks in Landsat time series as predictors of small scale deforestation in tropical dry forests of Mexico and Costa Rica

Tracking the occurrence of deforestation events is an essential task in tropical dry forest (TDF) conservation efforts. Ideally, deforestation monitoring systems would identify a TDF clearing with near real time precision and high spatial detail, and alert park managers and environmental practitioners of illegal forest clearings occurring anywhere in a region of interest. Over the past several years there have been significant advances in the design and application of continuous land cover change mapping algorithms with these capabilities, but no studies have implemented such methods over human dominated TDF environments where small–scale deforestation (<5 ha) is widespread and hard to detect with moderate resolution sensors. The general objective for this research was to evaluate the overall accuracy of the BFASTSpatial R Package for detecting and monitoring small-scale deforestation in four sites located in tropical dry forest landscapes of Mexico and Costa Rica using greenness and moisture spectral indices derived from Landsat time series. Results show a high degree of spatial agreement (90%–94%) between the distribution of TDF clearings occurred during the 2013–2016 period (as indicated by VHR imagery interpretation) and BFASTSpatial outputs. NDMI and NBR2 had the best performance than other indices and this is evidenced by the combined overall, user's and producer's accuracies. In particular, NBR2 were the most accurate predictor of deforestation with an overall accuracy of 94.5%. Our results also imply that monitoring sites at an annual basis is feasible using BFASTSpatial and LTS, but that lower confidence should be given to sub-annual products given significant systematic temporal differences between the BFASTSpatial monthly product and reference data. The possibility of including more clear observations at the spatial resolution of Landsat (30-m) or higher will greatly increase the spatial and temporal accuracies of the method. Given its performance, BFASTSpatial can help monitor hotspots of small-scale TDF loss across Central and North America at little or no cost. Users of the method should have a strong knowledge of the local land use and land cover dynamics and the ecophysiology of vegetation types present in the landscape. This local expertise is necessary for interpreting and validating results as well as communicating its output to decision-makers and stakeholders.

Assessment of vegetation dynamics using remote sensing and GIS: A case of Bosomtwe Range Forest Reserve, Ghana

Changing conditions owing to increasing forest fragmentation make land cover and change detection analysis an extremely important consideration for sustainable forest management. This study applied supervised classification using maximum-likelihood algorithm in Quantum GIS to detect land use land cover changes in the Bosomtwe Range Forest Reserve, Ghana from 1991, 2002 and 2017 using Landsat 4 – TM, Landsat 7 – ETM and Sentinel-2 satellite imageries respectively. Based on the results of the study, it is concluded that land use/cover of Bosomtwe Range Forest Reserve have undergone remarkable changes for over the period of 26 years. The current status of forest cover is estimated to be 2995.45 ± 401.86 ha and 2090.03 ± 412.78 ha of closed and opened forest canopy respectively. Conversely, built-up areas (1531.68 ± 487.13 ha) remains virtually high (20%) though it shows a decrease in comparison to the same area in 2002. The land use land cover change map clearly identified probable areas of forest depletion especially in the north eastern and western portions of the reserve. It is recommended that potential spatial drivers of change should be identified to generate suitable image for change modelling of the reserve, coupled with earmarking of degraded areas for reforestation projects to improve upon the forest cover.

Spatial assessment of urban sprawl in Arua Municipality, Uganda

Arua Municipality is one of the regional municipalities that has been earmarked for transformation to city status. Its population has been growing at rate of 3% per annum 1% higher than the capital, Kampala. Urbanisation provides economic opportunities. However, it also lead to emergence of unplanned urban settlements and urban sprawl. This paper applied remote sensing and geographical information system techniques to map land cover changes from 2001 to 2016, quantify urban sprawl within the period and estimate the urban growth pattern up to 2031. TerrSet’s Land Change Modeller has been used to model the urbanisation and Markov Chain matrices used to predict future changes in the urban composition. Land cover classification accuracy of 85% in 2001, 84% in 2010 and 89.2% in 2016 were obtained. From 2001 to 2016, the four land cover types considered, contributed a total of 11.5% to the composition of built-up area with agricultural land cover type contributing the most at 7.4%. Results of urbanisation analyses indicated that in 2001, 18.2% of the total area were built-up. This increased to 28.8% in 2010 and 40.9% in 2016. Urbanisation is predicted to increase to 57.4% by the year 2031. This prediction indicates that agricultural land cover will be most negatively affected at −10% loss rate while built-up areas will increase by 6%. While urbanisation continues to increase at this rate, the municipal authority must implement sustainable measures to protect agricultural lands and ecosystems against the land consuming urbanisation.

Analyses of Land Cover Change Trajectories Leading to Tropical Forest Loss: Illustrated for the West Kutai and Mahakam Ulu Districts, East Kalimantan, Indonesia

In Indonesia, land cover change for agriculture and mining is threatening tropical forests, biodiversity and ecosystem services. However, land cover change is highly dynamic and complex and varies over time and space. In this study, we combined Landsat-based land cover (change) mapping, pixel-to-pixel cross tabulations and expert knowledge to analyze land cover change and forest loss in the West Kutai and Mahakam Ulu districts in East Kalimantan from 1990–2009. We found that about one-third of the study area changed in 1990–2009 and that the different types of land cover changes in the study area increased and involved more diverse and characteristic trajectories in 2000–2009, compared to 1990–2000. Degradation to more open forest types was dominant, and forest was mostly lost due to trajectories that involved deforestation to grasslands and shrubs (~17%), and to a lesser extent due to trajectories from forest to mining and agriculture (11%). Trajectories from forest to small-scale mixed cropland and smallholder rubber occurred more frequently than trajectories to large-scale oil palm or pulpwood plantations; however, the latter increased over time. About 11% of total land cover change involved multiple-step trajectories and thus “intermediate” land cover types. The combined trajectory analysis in this paper thus contributes to a more comprehensive analysis of land cover change and the drivers of forest loss, which is essential to improve future land cover projections and to support spatial planning.

Analysis Ready Data: Enabling Analysis of the Landsat Archive

Data that have been processed to allow analysis with a minimum of additional user effort are often referred to as Analysis Ready Data (ARD). The ability to perform large scale Landsat analysis relies on the ability to access observations that are geometrically and radiometrically consistent, and have had non-target features (clouds) and poor quality observations flagged so that they can be excluded. The United States Geological Survey (USGS) has processed all of the Landsat 4 and 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) archive over the conterminous United States (CONUS), Alaska, and Hawaii, into Landsat ARD. The ARD are available to significantly reduce the burden of pre-processing on users of Landsat data. Provision of pre-prepared ARD is intended to make it easier for users to produce Landsat-based maps of land cover and land-cover change and other derived geophysical and biophysical products. The ARD are provided as tiled, georegistered, top of atmosphere and atmospherically corrected products defined in a common equal area projection, accompanied by spatially explicit quality assessment information, and appropriate metadata to enable further processing while retaining traceability of data provenance.

Monitoring surface mining belts using multiple remote sensing datasets: A global perspective

Quantifying the spatiotemporal change of land cover and understanding their ecological, environmental, and socioeconomic impacts are important for sustainable development. Surface mining by the minerals industry is one driver of the changes in land cover, leading to loss of natural vegetation and top soils, and interruption of ecosystem service flows. This study investigates the effectiveness of remote sensing datasets to identify and map land cover changes, with the specific goal of understanding the impact of surface mining activities on land cover globally from 1980s to 2013. Diverse remote sensing datasets with long term observations are analyzed, including high-resolution images in Google Earth, Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI), the Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Index (VI) product and Defense Meteorological Satellites Program (DMSP)/Operational Linescan System (OLS) stable night-time light. The results indicated that after entering 21st century, North America (e.g., the United States and Canada) was the only continent to have more surface mining spots categorized as Shrink type (rehabilitated) rather than Expand type. South America (e.g., Chile and Brazil) and Asia (e.g., India and China) had the highest proportions of Expand Type of surface mining spots. Detailed demonstrations on how those remote sensing datasets could help in mining spot monitoring are presented.

Greening and Browning of the Hexi Corridor in Northwest China: Spatial Patterns and Responses to Climatic Variability and Anthropogenic Drivers

The arid region of northwest China provides a unique terrestrial ecosystem to identify the response of vegetation activities to natural and anthropogenic changes. To reveal the influences of climate and anthropogenic factors on vegetation, the Normalized Difference Vegetation Index (NDVI), climate data, and land use and land cover change (LUCC) maps were used for this study. We analyzed the spatiotemporal change of NDVI during 2000–2015. A partial correlation analysis suggested that the contribution of precipitation (PRE) and temperature (TEM) on 95.43% of observed greening trends was 47% and 20%, respectively. The response of NDVI in the eastern section of the Qilian Mountains (ESQM) and the western section of the Qilian Mountains (WSQM) to PRE and TEM showed opposite trends. The multiple linear regressions used to quantify the contribution of anthropogenic activity on the NDVI trend indicated that the ESQM and oasis areas were mainly affected by anthropogenic activities (26%). The observed browning trend in the ESQM was attributed to excessive consumption of natural resources. A buffer analysis and piecewise regression methods were further applied to explore the influence of urbanization on NDVI and its change rate. The study demonstrated that urbanization destroys the vegetation cover within the developed city areas and extends about 4 km beyond the perimeter of urban areas and the NDVI of buffer cities (counties) in the range of 0–4 km (0–3 km) increased significantly. In the range of 5–15 (4–10) km (except for Jiayuguan), climate factors were the major drivers of a slight downtrend in the NDVI. The relationship of land use change and NDVI trends showed that construction land, urban settlement, and farmland expanded sharply by 171.43%, 60%, and 10.41%, respectively. It indicated that the rapid process of urbanization and coordinated urban-rural development shrunk ecosystem services.

Remap: An online remote sensing application for land cover classification and monitoring

Abstract Recent assessments of progress towards global conservation targets have revealed a paucity of indicators suitable for assessing the changing state of ecosystems. Moreover, land managers and planners are often unable to gain timely access to the maps they need to support their routine decision‐making. This deficiency is partly due to a lack of suitable data on ecosystem change, driven mostly by the considerable technical expertise needed to develop ecosystem maps from remote sensing data. We have developed a free and open‐access online remote sensing and environmental modelling application, the Remote Ecosystem Monitoring and Assessment Pipeline (Remap; https://remap-app.org), that enables volunteers, managers and scientists with little or no experience in remote sensing to generate classifications (maps) of land cover and land use change over time. Remap utilizes the geospatial data storage and analysis capacity of Google Earth Engine and requires only spatially resolved training data that define map classes of interest (e.g. ecosystem types). The training data, which can be uploaded or annotated interactively within Remap, are used in a random forest classification of up to 13 publicly available predictor datasets to assign all pixels in a focal region to map classes. Predictor datasets available in Remap represent topographic (e.g. slope, elevation), spectral (archival Landsat image composites) and climatic variables (precipitation, temperature) that are relevant to the distribution of ecosystems and land cover classes. The ability of Remap to develop and export high‐quality classified maps in a very short (&lt;10 min) time frame represents a considerable advance towards globally accessible and free application of remote sensing technology. By enabling access to data and simplifying remote sensing classifications, Remap can catalyse the monitoring of land use and change to support environmental conservation, including developing inventories of biodiversity, identifying hotspots of ecosystem diversity, ecosystem‐based spatial conservation planning, mapping ecosystem loss at local scales and supporting environmental education initiatives.

Mapping Wetlands and Land Cover Change with Landsat Archives: The Added Value of Geomorphologic Data

While classification of remote sensing images has proven useful to quantify wetland losses, wetland mappers have always faced challenges, such as dealing with variable responses of wetlands to meteorological conditions and the obstruction of ground by the canopy in forested wetlands. In this paper, we investigate the added value of using geomorphological data, namely hillslope geometry (through Dikau shapes) and soil drainage classes, as ancillary datasets to map the evolution of wetland cover over the last 35 years. We developed an object-based image analysis method, through a case study in Quebec. Two sets of land cover scenarios (spring and fall) were generated from Landsat archives for 1978, 1985, 1992, 2001, and 2014. Results show that the global accuracy was improved by 19% to 35% when using the geomorphological data, especially soil drainage classes, confirming that their use as ancillary data significantly contributed to the classification process. However, differences were noted between spring and fall scenarios. The wetland cover of the study watershed decreased by 8%–53% between 1978 and 2014 at the expense of an increase in urban areas. Meanwhile, the agricultural land cover decreased (72%–83%) throughout the study period, while forests, water, and bare soil remained stable.

LAND USE/COVER CHANGE AND LANDSCAPE FRAGMENTATION ANALYSES IN KHON KAEN CITY, NORTHEASTERN THAILAND

The aim of this study is to determine the land use and land cover changes in Khon Kaen city, Thailand, using landscape fragmentation analysis together with multi-temporal Landsat data acquired during 1990-2015. Using support vector machine techniques, multi-temporal Landsat imagery was classified to derive a map of land use and land cover change and subsequently analyze the landscape fragmentation to determine the factors driving this change. The results showed that most of the built-up area was converted from anagricultural area, illustrating the expansion of the urban area over the past twenty-five years. Consequently, the landscape has become more highly fragmented as indicated by an increase in the patch number and a decrease in the mean patch size of the agriculture land cover class. Economic development, education system development, population growth and the improvement of traffic infrastructure are main factors driving land use and land cover change in Khon Kaen city.

Acceleration and fragmentation of CORINE land cover changes in the United Kingdom from 2006–2012 detected by Copernicus IMAGE2012 satellite data

The CORINE land cover maps present the longest series of land cover maps with a consistent class labelling system and date back to 1985. This paper presents the results of the CORINE land cover mapping of the United Kingdom for 2012 and the corresponding land cover change map from 2006 to 2012. It compares the rates of change with those of the preceding land cover change map 2000–2006 and finds that land cover change has become smaller in scale, more diverse in types of change and affects more land cover polygons than in the past reporting period. Land cover change from 2006 to 2012 affected almost 60% more land cover polygons than from 2000 to 2006. A greater variety of 165 types of land cover change was detected from 2006 to 2012 than the 67 types of change from 2000 to 2006. The total land cover change area increased by over 21,000 ha or 11% but remained at around 1% of the total land area of the UK. Rotation forestry mostly of conifer forests was a dominant type of land cover change in both periods (53% of overall change from 2000 to 2006 and 54% from 2006 to 2012), followed by growth and replanting of conifer forest. From 2006 to 2012 the replanting rate decreased by almost 15,000 ha compared to 2000–2006 and a smaller decrease in planting of broadleaf and mixed forests was also observed. Urban land take continued from 2006 to 2012 in the UK, with over 16,000 ha of increase in artificial surfaces. The rate of change from other land cover types to artificial surfaces accelerated from 2006 to 2012. However, we urge caution when interpreting the rate of land take, as it includes wind farms in forested areas which leave the forest largely intact apart from an access road and the wind turbine sites. We also found that the inference from the land cover change matrices is dependent on the level of class aggregation (level 1, 2 or 3).

Land cover change mapping using high resolution satellites and unmanned aerial vehicle

Cameron Highlands is increasingly gaining popularity as a tourist attraction both locally and internationally. Unfortunately, due to the high demand of agricultural products and construction of new hotels and settlements, the area is now characterized by land scarcity. This research aims to assess the use of high resolution satellites images and Unmanned Aerial Vehicle (UAV) in detecting land use and land cover changes for a period of 12 years. The high-resolution satellite images of IKONOS and QuickBird were used together with a fixed wing Helang UAV. The object based classification method was applied to classify the images into seven land cover classes. Results indicate that land cover changes were detected in forest areas with a decrease from 196.1 hectares to 180.7 hectares and to 160.1 hectares from 2001 to 2007 and to 2013 respectively. Inversely, the built up areas increased from 47.8 hectares to 58.5 hectares and to 63.4 hectares from 2001 to 2007 and to 2013 respectively. During the same periods, there were slight increases in agriculture and grassland; the water body areas remained almost unchanged while in the bare soil areas there are slightly non-uniform changes. A large landslide area was detected in the UAV image.

Satellite Images and Gaussian Parameterization for an Extensive Analysis of Urban Heat Islands in Thailand

For the first time, an extensive study of the surface urban heat island (SUHI) in Thailand’s six major cities is reported, using 728 MODIS (MODerate Resolution Imaging Spectroradiometer) images for each city. The SUHI analysis was performed at three timescales—diurnal, seasonal, and multiyear. The diurnal variation is represented by the four MODIS passages (10:00, 14:00, 22:00, and 02:00 local time) and the seasonal variation by summer and winter maps, with images covering a 14-year interval (2003–2016). Also, 126 Landsat scenes were processed to classify and map land cover changes for each city. To analyze and compare the SUHI patterns, a least-square Gaussian fitting method has been applied and the corresponding empirical metrics quantified. Such an approach represents, when applicable, an efficient quantitative tool to perform comparisons that a visual inspection of a great number of maps would not allow. Results point out that SUHI does not show significant seasonality differences, while SUHI in the daytime is a more evident phenomenon with respect to nighttime, mainly due to solar forcing and intense human activities and traffic. Across the 14 years, the biggest city, Bangkok, shows the highest SUHI maximum intensities during daytime, with values ranging between 4 °C and 6 °C; during nighttime, the intensities are rather similar for all the six cities, between 1 °C and 2 °C. However, these maximum intensities are not correlated with the urban growth over the years. For each city, the SUHI spatial extension represented by the Gaussian footprint is generally not affected by the urban area sprawl across the years, except for Bangkok and Chiang Mai, whose daytime SUHI footprints show a slight increase over the years. Orientation angle and central location of the fitted surface also provide information on the SUHI layout in relation to the land use of the urban texture.