DETECTING THE SPATIAL DISTRIBUTION OF SETTLEMENTS ON VOLCANIC REGION USING IMAGE LANDSAT-8 OLI IMAGERY

Geologically, Indonesia region is on track ring of fire, brings the consequence that the danger of volcanic eruption could occur at any time. Information sites where the settlement is located in the affected areas on emergency response process is needed in quick time. The availability of up to date data is important because it illustrates the actual condition of the region. Active volcanic landforms ranging from the crater to footslope in general is prone area to volcanic eruption, either by the threat of lava flows, pyroclastic falls, or lahars. This study aims to detect the spatial distribution of the settlement on volcanic region using Landsat-8 OLI. Parameters used for the detection of settlements is Normalized Difference Build-up Index (NDBI). Research methods include radiometric correction, delineation of the boundaries of volcanic landforms, NDBI value extraction, extraction of settlement areas, as well as the accuracy assesment.  Study area  is  Sinabung Volcano region located in the province of North Sumatera. Recently, the volcano experienced a devastating and catastrophic eruption. The results showed that the spatial distribution of settlements on volcanic landforms can be detected quickly from Landsat-8 OLI based on NDBI parameters with a sufficient degree of accuracy

Linking thermal variability and change to urban growth in Harare Metropolitan City using remotely sensed data.

Doctor of Philosophy in Environmental Science. University of KwaZulu-Natal. Pietermaritzburg, 2017.Urban growth, which involves Land Use and Land Cover Changes (LULCC), alters land surface thermal properties. Within the framework of rapid urban growth and global warming, land surface temperature (LST) and its elevation have potential significant socio-economic and environmental implications. Hence the main objectives of this study were to (i) map urban growth, (ii) link urban growth with indoor and outdoor thermal conditions and (iii) estimate implications of thermal trends on household energy consumption as well as predict future urban growth and temperature patterns in Harare Metropolitan, Zimbabwe. To achieve these objectives, broadband multi-spectral Landsat 5, 7 and 8, in-situ LULC observations, air temperature (Ta) and humidity data were integrated. LULC maps were obtained from multi-spectral remote sensing data and derived indices using the Support Vector Machine Algorithm, while LST were derived by applying single channel and split window algorithms. To improve remote sensing based urban growth mapping, a method of combining multi-spectral reflective data with thermal data and vegetation indices was tested. Vegetation indices were also combined with socio-demographic data to map the spatial distribution of heat vulnerability in Harare. Changes in outdoor human thermal discomfort in response to seasonal LULCC were evaluated, using the Discomfort Index (DI) derived parsimoniously from LST retrieved from Landsat 8 data. Responses of LST to long term urban growth were analysed for the period from 1984 to 2015. The implications of urban growth induced temperature changes on household air-conditioning energy demand were analysed using Landsat derived land surface temperature based Degree Days. Finally, the Cellular Automata Markov Chain (CAMC) analysis was used to predict future landscape transformation at 10-year time steps from 2015 to 2045. Results showed high overall accuracy of 89.33% and kappa index above 0.86 obtained, using Landsat 8 bands and indices. Similar results were observed when indices were used as stand-alone dataset (above 80%). Landsat 8 derived bio-physical surface properties and socio-demographic factors, showed that heat vulnerability was high in over 40% in densely built-up areas with low-income when compared to “leafy” suburbs. A strong spatial correlation (α = 0.61) between heat vulnerability and surface temperatures in the hot season was obtained, implying that LST is a good indicator of heat vulnerability in the area. LST based discomfort assessment approach retrieved DI with high accuracy as indicated by mean percentage error of less than 20% for each sub-season. Outdoor thermal discomfort was high in hot dry season (mean DI of 31oC), while the post rainy season was the most comfortable (mean DI of 19.9oC). During the hot season, thermal discomfort was very low in low density residential areas, which are characterised by forests and well maintained parks (DI ≤27oC). Long term changes results showed that high density residential areas increased by 92% between 1984 and 2016 at the expense of cooler green-spaces, which decreased by 75.5%, translating to a 1.98oC mean surface temperature increase. Due to surface alterations from urban growth between 1984 and 2015, LST increased by an average of 2.26oC and 4.10oC in the cool and hot season, respectively. This decreased potential indoor heating energy needed in the cool season by 1 degree day and increased indoor cooling energy during the hot season by 3 degree days. Spatial analysis showed that during the hot season, actual energy consumption was low in high temperature zones. This coincided with areas occupied by low income strata indicating that they do not afford as much energy and air conditioning facilities as expected. Besides quantifying and strongly relating with energy requirement, degree days provided a quantitative measure of heat vulnerability in Harare. Testing vegetation indices for predictive power showed that the Urban Index (UI) was comparatively the best predictor of future urban surface temperature (r = 0.98). The mean absolute percentage error of the UI derived temperature was 5.27% when tested against temperature derived from thermal band in October 2015. Using UI as predictor variable in CAMC analysis, we predicted that the low surface temperature class (18-28oC) will decrease in coverage, while the high temperature category (36-45oC) will increase in proportion covered from 42.5 to 58% of city, indicating further warming as the city continues to grow between 2015 and 2040. Overall, the findings of this study showed that LST, human thermal comfort and air-conditioning energy demand are strongly affected by seasonal and urban growth induced land cover changes. It can be observed that urban greenery and wetlands play a significant role of reducing LST and heat transfer between the surface and lower atmosphere and LST may continue unless effective mitigation strategies, such as effective vegetation cover spatial configuration are adopted. Limitations to the study included inadequate spatial and low temporal resolution of Landsat data, few in-situ observations of temperature and LULC classification which was area specific thus difficult for global comparison. Recommendations for future studies included data merging to improve spatial and temporal representation of remote sensing data, resource mobilization to increase urban weather station density and image classification into local climate zones which are of easy global interpretation and comparison

Characterizing the relationship between land use land cover change and land surface temperature

Exploring changes in land use land cover (LULC) to understand the urban heat island (UHI) effect is valuable for both communities and local governments in cities in developing countries, where urbanization and industrialization often take place rapidly but where coherent planning and control policies have not been applied. This work aims at determining and analyzing the relationship between LULC change and land surface temperature (LST) patterns in the context of urbanization. We first explore the relationship between LST and vegetation, man-made features, and cropland using normalized vegetation, and built-up indices within each LULC type. Afterwards, we assess the impacts of LULC change and urbanization in UHI using hot spot analysis (Getis-Ord Gi∗ statistics) and urban landscape analysis. Finally, we propose a model applying non-parametric regression to estimate future urban climate patterns using predicted land cover and land use change. Results from this work provide an effective methodology for UHI characterization, showing that (a) LST depends on a nonlinear way of LULC types; (b) hotspot analysis using Getis Ord Gi∗ statistics allows to analyze the LST pattern change through time; (c) UHI is influenced by both urban landscape and urban development type; (d) LST pattern forecast and UHI effect examination can be done by the proposed model using nonlinear regression and simulated LULC change scenarios. We chose an inner city area of Hanoi as a case-study, a small and flat plain area where LULC change is significant due to urbanization and industrialization. The methodology presented in this paper can be broadly applied in other cities which exhibit a similar dynamic growth. Our findings can represent an useful tool for policy makers and the community awareness by providing a scientific basis for sustainable urban planning and management.First, the authors would like to thank the European Commission and the Erasmus Mundus Consortium for providing the master scholarship in Geospatial Technologies. We acknowledge the USGS-NASA due to their freely accessible Landsat data. Thanks are also due to the Laboratory for Geographic Information Analysis (Department of Geography, Hanoi National University of Education) for providing valuable tools and software. This work has also been partially supported by the Spanish Ministry of Economy under project ESP2013-48458-C4-3- P

Evaluación de la intensidad de la temperatura de la superficie terrestre en el distrito de Thiruvarur

Nature gives way to the emergence of concrete jungles as cities grow around them. The majority of these concrete masses are highly reflective, changing the surrounding temperature. Hence, urbanized regions often have higher average temperatures than their surrounding rural areas. This phenomenon is termed Urban Heat Island (UHI). The intensity of UHI depends up on Land Surface Temperature (LST). This paper intends to study the intensity of LST in the Thiruvarur district and its correlation with Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) using Landsat 8 Imageries (OLI & TIRS) of January 2018. To calculate the LST, we used the mono-window algorithm. The result shows that LST intensity varies from 20.68°C to 32.3°C, with the maximum being in built-up areas and the minimum being in vegetation areas and water bodies. The Pearson regression shows that there is a negative correlation (r = -0.925, P < 0.5) between LST & NDVI and a positive correlation (r = 0.925, P < 0.5) between LST and NDBI. The strong positive correlation of NDBI confirms the influence of urbanization on Surface Urban Heat Island (SUHI). The negative correlation between LST and NDVI shows that green covers can mitigate it. Hence, this study conclusively demonstrates that urbanization can raise temperatures, showing that sustainable development in cities is essential for sustainable growth

IMPLIKASI PERUBAHAN KERAPATAN BANGUNAN DAN KERAPATAN VEGETASI TERHADAP RTH DI KOTA TANGERANG

Kota Tangerang memiliki lokasi yang dekat dengan pusat Ibu Kota, maka banyak masyarakat yang memilih untuk tinggal di Kota Tangerang, agar mendapatkan akses pekerjaan dan pelayanan. Berdasarkan hal tersebut, peningkatan jumlah penduduk di Kota Tangerang rata-rata per-tahun sebesar 1.87% antara tahun 2009-2019. Peningkatan jumlah penduduk akan mempengaruhi kebutuhan ruang untuk mewadahi aktivitas masyarakat. Meningkatnya kebutuhan ruang, akan menyebabkan alih fungsi lahan. Terjadinya alih fungsi lahan vegetasi ke lahan terbangun maka akan memicu perubahan kerapatan bangunan dan vegetasi, sehingga dapat berdampak kepada ruang terbuka hijau. Apabila hal tersebut tidak ditangani dengan tepat maka ruang terbuka hijau di Kota Tangerang akan semakin berkurang

Effect of impervious surface area and vegetation changes on mean surface temperature over Tshwane metropolis, Gauteng Province, South Africa

The Tshwane Metropolis, Gauteng Province, South Africa, continues to experience rapid urbanization as a result of population growth. This has led to the conversion of natural lands into large man-made landscapes i.e., increase in impervious surfaces and a decrease in vegetative cover. This land use or land cover changes are also thought to affect the climate of the Tshwane metropolis as is evidenced by heat waves in 2013 and 2014. This paper describes how vegetation and impervious surface area (ISA) or built up areas were classified from Landsat 8 LCDM, 2013, and Landsat 7 ETM+, 2003 images using thematic spectral indices and mean surface temperatures derived from the thermal bands. The linear relationship between the two land cover types and surface temperature (LST) derived from the thermal bands was also examined. The results of this research reveal that the ISA increase has occurred due to urban sprawl and this has contributed to increase in surface temperature.The Applied Centre for Climate and Earth System Science (ACCESS) and University of Pretoria.http://www.sajg.org.za/index.php/sajgam2016Geography, Geoinformatics and Meteorolog

Effect of impervious surface area and vegetation changes on mean surface temperature over Tshwane metropolis, Gauteng Province, South Africa

The Tshwane Metropolis, Gauteng Province, South Africa, continues to experience rapid urbanization as a result of population growth. This has led to the conversion of natural lands into large man-made landscapes i.e., increase in impervious surfaces and a decrease in vegetative cover. This land use or land cover changes are also thought to affect the climate of the Tshwane metropolis as is evidenced by heat waves in 2013 and 2014. This paper describes how vegetation and impervious surface area (ISA) or built up areas were classified from Landsat 8 LCDM, 2013, and Landsat 7 ETM+, 2003 images using thematic spectral indices and mean surface temperatures derived from the thermal bands. The linear relationship between the two land cover types and surface temperature (LST) derived from the thermal bands was also examined. The results of this research reveal that the ISA increase has occurred due to urban sprawl and this has contributed to increase in surface temperature.The Applied Centre for Climate and Earth System Science (ACCESS) and University of Pretoria.http://www.sajg.org.za/index.php/sajgam2016Geography, Geoinformatics and Meteorolog

Caracterização temporal do município de Campinas usando NDVI, NDBI e temperatura da superfície.

Este estudo tem por objetivo investigar, com o apoio de técnicas de sensoriamento remoto e com base na organização das UTBs (Unidades Territoriais Básicas) do Município de Campinas, as relações entre a temperatura da superfície (TS), o NDVI (índice da diferença normalizada da vegetação) e o NDBI (índice normalizado de diferença de construção) utilizando imagens Landsat 5 TM. As imagens do satélite Landsat 5 foram obtidas em agosto de 1996, 2003 e 2011. Considerando os três anos analisados, o NDVI médio do município aumentou significativamente de 0,18 em 1996 para 0,32 em 2003. Verificou-se redução significativa no valor médio do NDBI, que representou alteração acentuada no uso e na cobertura do solo entre 1996 e 2003 ou 2011. Os resultados identificaram uma relação negativa entre o NDVI e a temperatura da superfície e uma relação positiva entre o NDBI e a temperatura da superfície. Concomitantemente, demonstraram um padrão de ocupação do solo no sentido sul e noroeste do Município de Campinas

Seasonal and Diurnal Variation of Land Surface Temperature distribution and its in Relation to Land Use/Land cover Pattern

The surface urban heat island (SUHI) affects the quality of urban life. Because varying urban structures have varying impacts on SUHI, it is crucial to understand the impact of land use/land cover characteristics for improving the quality of life in cities and urban health. Satellite-based data on land surface temperatures (LST) and derived land use/cover pattern (LUCP) indicators provide an efficient opportunity to derive the required data at a large scale. This study explores the seasonal and diurnal variation of spatial associations from LUCP and LST employing Pearson correlation and ordinary least squares regression analysis. Specifically, Landsat-8 images were utilized to derive LSTs in four seasons, taking Berlin as a case study. The results indicate that: (1) in terms of land cover, hot spots are mainly distributed over transportation, commercial and industrial land in the daytime, while wetlands were identified as hot spots during nighttime; (2) from the land composition indicators, the normalized difference built-up index (NDBI) showed the strongest influence in summer, while the normalized difference vegetation index (NDVI) exhibited the biggest impact in winter; (3) from urban morphological parameters, the building density showed an especially significant positive association with LST and the strongest effect during daytime

Spatial Changes of Urban Heat Island Formation in the Colombo District, Sri Lanka: Implications for Sustainability Planning

The formation of surface urban heat islands (SUHIs) can cause significant adverse impacts on the quality of living in urban areas. Monitoring the spatial patterns and trajectories of UHI formations could be helpful to urban planners in crafting appropriate mitigation and adaptation measures. This study examined the spatial pattern of SUHI formation in the Colombo District (Sri Lanka), based on land surface temperature (LST), a normalized difference vegetation index (NDVI), a normalized difference built-up index (NDBI), and population density (PD) using a geospatial-based hot and cold spot analysis tool. Here, ‘hot spots’ refers to areas with significant spatial clustering of high variable values, while ‘cold spots’ refers to areas with significant spatial clustering of low variable values. The results indicated that between 1997 and 2017, 32.7% of the 557 divisions in the Colombo District persisted as hot spots. These hot spots were characterized by a significant clustering of high composite index values resulting from the four variables (LST, NDVI (inverted), NDBI, and PD). This study also identified newly emerging hot spots, which accounted for 49 divisions (8.8%). Large clusters of hot spots between both time points were found on the western side of the district, while cold spots were found on the eastern side of the district. The areas identified as hot spots are the more urbanized parts of the district. The emerging hot spots were in areas that had undergone landscape changes due to urbanization. Such areas are found between the persistent hot spots (western parts of the district) and persistent cold spots (eastern parts of the district). Generally, the spatial pattern of the emerging hot spots followed the pattern of urbanization in the district, which had been expanding from west to east. Overall, the findings of this study could be used as a reference in the context of sustainable landscape and urban planning for the Colombo District