scholarly journals REMOTELY SENSED IMAGE FAST CLASSIFICATION AND SMART THEMATIC MAP PRODUCTION

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 43-50
Author(s):  
E. Alcaras ◽  
P. P. Amoroso ◽  
C. Parente ◽  
G. Prezioso
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Confusion Matrix ◽  
Bare Soil ◽  
Remotely Sensed ◽  
Landsat 8 ◽  
Thematic Map ◽  
Remote Sensing Classification ◽  
Maximum Likelihood Methods ◽  
Remotely Sensed Image

Abstract. Apps available for Smartphone, as well as software for GNSS/GIS devices, permit to easily mapping the localization and shape of an area by acquiring the vertices coordinates of its contour. This option is useful for remote sensing classification, supporting the detection of representative sample sites of a known cover type to use for algorithm training or to test classification results. This article aims to analyse the possibility to produce smart maps from remotely sensed image classification in rapid way: the attention is focalized on different methods that are compared to identify fast and accurate procedure for producing up-to-date and reliable maps. Landsat 8 OLI multispectral images of northern Sicily (Italy) are submitted to various classification algorithms to distinguish water, bare soil and vegetation. The resulting map is useful for many purposes: appropriately inserted in a larger database aimed at representing the situation in a space-time evolutionary scenario, it is suitable whenever you want to capture the variation induced in a scene, e.g. burnt areas identification, vegetated areas definition for tourist-recreational purposes, etc. Particularly, pixel-based classification approaches are preferred, and experiments are carried out using unsupervised (k-means), vegetation index (NDVI, Normalized Difference Vegetation Index), supervised (minimum distance, maximum likelihood) methods. Using test sites, confusion matrix is built for each method, and quality indices are calculated to compare the results. Experiments demonstrate that NDVI submitted to k-means algorithm allows the best performance for distinguishing not only vegetation areas but also water bodies and bare soils. The resulting thematic map is converted for web publishing.

scholarly journals Evaluation of Water Indices for Surface Water Extraction in a Landsat 8 Scene of Nepal

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2580 ◽  
Author(s):  
Tri Acharya ◽  
Anoj Subedi ◽  
Dong Lee
Keyword(s):  
Surface Water ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Pure Water ◽  
Confusion Matrix ◽  
Threshold Value ◽  
Environmental Noise ◽  
Water Extraction ◽  
Landsat 8 ◽  
Water Indices

Accurate and frequent updates of surface water have been made possible by remote sensing technology. Index methods are mostly used for surface water estimation which separates the water from the background based on a threshold value. Generally, the threshold is a fixed value, but can be challenging in the case of environmental noise, such as shadow, forest, built-up areas, snow, and clouds. One such challenging scene can be found in Nepal where no such evaluation has been done. Taking that in consideration, this study evaluates the performance of the most widely used water indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), and Automated Water Extraction Index (AWEI) in a Landsat 8 scene of Nepal. The scene, ranging from 60 m to 8848 m, contains various types of water bodies found in Nepal with different forms of environmental noise. The evaluation was conducted based on measures from a confusion matrix derived using validation points. Comparing visually and quantitatively, not a single method was able to extract surface water in the entire scene with better accuracy. Upon selecting optimum thresholds, the overall accuracy (OA) and kappa coefficient (kappa) was improved, but not satisfactory. NDVI and NDWI showed better results for only pure water pixels, whereas MNDWI and AWEI were unable to reject snow cover and shadows. Combining NDVI with NDWI and AWEI with shadow improved the accuracy but inherited the NDWI and AWEI characteristics. Segmenting the test scene with elevations above and below 665 m, and using NDVI and NDWI for detecting water, resulted in an OA of 0.9638 and kappa of 0.8979. The accuracy can be further improved with a smaller interval of categorical characteristics in one or multiple scenes.

scholarly journals COVERAGE CHANGES DETECTION AT CIÉNAGA GRANDE, SANTA MARTA – COLOMBIA USING AUTOMATIC CLASSIFICATION

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 195-200
Author(s):  
J. S. Vinasco ◽  
D. A. Rodríguez ◽  
S. Velásquez ◽  
D. F. Quintero ◽  
L. R. Livni ◽  
...  
Keyword(s):  
Random Forest ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Confusion Matrix ◽  
Automatic Classification ◽  
Detection Methods ◽  
Landsat 8 ◽  
High Resolution Data ◽  
Water Index ◽  
Normalized Difference Water Index

Abstract. The Ciénaga Grande, Santa Marta is the largest and most diverse ecosystem of its kind in Colombia. Its primary function is acting as a filter for the organic carbon cycle. Recently, this place has been suffering disruptions due to the anthropic activities taking place in its surroundings. The present study, the changes in the surface of Ciénaga Grande, Santa Marta, Magdalena, Colombia between 2013 and 2018 were determined using semiautomatic detection methods with high resolution data from remote sensors (Landsat 8). The zone of studies was classified in six kinds of surfaces: 1) artificial territories, 2) agricultural territories, 3) forests and semi-natural areas, 4) wet areas, 5) deep water surfaces & 6) wich is related to clouds as a masking method. Random Forest classifiers were utilized and the Feed For Ward multilayer perceptron neuronal network (ANN) was simultaneously assessed. The training stage for both methods was performed with 300 samples, distributed in equal quantities, over each coverage class. The semi-automatic classification was carried out with an annual frequency, but the monitoring was carried out throughout the analysis period through the performance of three indicators Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI). It was found from the confusion matrix that the Random Forest method more accurately classified four classes while Neural Networks Analysis (NNA) just three. Finally, taking the Random Forest results into account, it was found that the agricultural expansion increased from 7% to 9% and the urban zone increased from 20% to 30% of the total area. As well as a decrease of damp areas from 27% to 12% and forests from 4% to 3% of the total area of study.

scholarly journals Modelling and Delineation of Hydrocarbon Micro - Seepage Prone Zones on Soil and Sediment in Ugwueme, South Eastern Nigeria with Soil Adjustment Vegetation Index (SAVI)

2020 ◽  
pp. 13-33
Author(s):  
Mfoniso Asuquo Enoh ◽  
Uzoma Chinenye Okeke ◽  
Needam Yiinu Barinua
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Bare Soil ◽  
Landsat 8 ◽  
Landsat Images ◽  
Landsat 7 ◽  
Soil And Sediment

Remote Sensing is an excellent tool in monitoring, mapping and interpreting areas, associated with hydrocarbon micro-seepage. An important technique in remote sensing known as the Soil Adjusted Vegetation Index (SAVI), adopted in many studies is often used to minimize the effect of brightness reflectance in the Normalized Difference Vegetation Index (NDVI), related with soil in areas of spare vegetation cover, and mostly in areas of arid and semi–arid regions. The study aim at analyzing the effect of hydrocarbon micro – seepage on soil and sediments in Ugwueme, Southern Eastern Nigeria, with SAVI image classification method. To achieve this aim, three cloud free Landsat images, of Landsat 7 TM 1996 and ETM+ 2006 and Landsat 8 OLI 2016 were utilized to produce different SAVI image classification maps for the study.  The SAVI image classification analysis for the study showed three classes viz Low class cover, Moderate class cover and high class cover.  The category of high SAVI density classification was observed to increase progressive from 31.95% in 1996 to 34.92% in 2006 and then to 36.77% in 2016. Moderately SAVI density classification reduced from 40.53% in 1996 to 38.77% in 2006 and then to 36.96% in 2016 while Low SAVI density classification decrease progressive from 27.51% in 1996 to 26.31% in 2006 and then increased to 28.26% in 2016. The SAVI model is categorized into three classes viz increase, decrease and unchanged. The un – changed category increased from 12.32km2 (15.06%) in 1996 to 17.17 km2 (20.96%) in 2006 and then decelerate to 13.50 km2 (16.51%) in 2016.  The decrease category changed from 39.89km2 (48.78%) in 1996 to 40.45 km2 (49.45%) in 2006 and to 51.52 km2 (63.0%) in 2016 while the increase category changed from 29.57km2 (36.16%) in 1996 to 24.18 km2 (29.58%) in 2006 and to 16.75 km2 (20.49%) in 2016. Image differencing, cross tabulation and overlay operations were some of the techniques performed in the study, to ascertain the effect of hydrocarbon micro - seepage.  The Markov chain analysis was adopted to model and predict the effect of the hydrocarbon micro - seepage for the study for 2030.  The study expound that the SAVI is an effective technique in remote sensing to identify, map and model the effect of hydrocarbon micro - seepage on soil and sediment particularly in areas characterized with low vegetation cover and bare soil cover.

scholarly journals Estimation of soil organic carbon in agricultural fields: A remote sensing approach

2022 ◽  
Vol 43 (1) ◽  
pp. 73-84
Author(s):  
R. Madugundu ◽  
◽  
K.A. Al-Gaadi ◽  
E. Tola ◽  
M. Edrris ◽  
...  
Keyword(s):  
Linear Regression ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Prediction Models ◽  
Bare Soil ◽  
Agricultural Fields ◽  
Landsat 8 ◽  
Linear Regression Models

Aim: In view of the importance of Soil Organic Carbon (SOC) in agricultural management, a study was conducted to develop a digital SOC map using remotely sensed spectral indices. The present study was conducted on the Tawdeehiya Farms, located in the central region of Saudi Arabia between Al-Kharj and Haradh cities. Methodology: Landsat-8 (L8) and Sentinel-2 (S2A) satellite images were used for the characterization of SOC stocks in the topsoil layer (0-10 cm) of the experimental fields. Soil samples were randomly collected from six (50 ha each) agricultural fields and analyzed in the laboratory for SOC (SOCA) following Walkley and Black method. While, vegetation indices (VI), such as the Normalized Difference Vegetation Index (NDVI), NDVIRedEdge, Enhanced Vegetation Index (EVI), Bare Soil Index (BSI), and Reduced Simple Ratio (RSR) were computed and subsequently used for the development of SOC prediction models. Results: Univariate linear regression technique was employed for the recognition of a suitable band/VI for SOC (SOCP) mapping. The SWIR-1 band of both L8 (R2 = 0.86) and S2A (R2 = 0.77) data was promising for predicting SOC with 16% (S2A) and 18% (L8) of BIAS. Interpretation: The NDVI and BSI (for L8 data) and BSI and RSR (for S2A data) were found most suitable VI in the prediction of SOC. The R2 values of linear regression models were 0.68 (BSI) and 0.78 (RSR), indicating that nearly 68% and 78% of the SOC could be predicted through L8 and S2A datasets, respectively.

TEMPERATURA DE SUPERFÍCIE E A RELAÇÃO COM O ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA (NDVI) NA MICROBACIA DO RIO DA BATATEIRAS, CRATO-CE

2019 ◽  
Vol 21 (2) ◽  
pp. 1310-1320
Author(s):  
Cícera Celiane Januário da Silva ◽  
Vinicius Ferreira Luna ◽  
Joyce Ferreira Gomes ◽  
Juliana Maria Oliveira Silva
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Temperature Measurement ◽  
Rainy Season ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Low Density ◽  
Difference Map

O objetivo do presente trabalho é fazer uma comparação entre a temperatura de superfície e o Índice de Vegetação por Diferença Normalizada (NDVI) na microbacia do rio da Batateiras/Crato-CE em dois períodos do ano de 2017, um chuvoso (abril) e um seco (setembro) como também analisar o mapa de diferença de temperatura nesses dois referidos períodos. Foram utilizadas imagens de satélite LANDSAT 8 (banda 10) para mensuração de temperatura e a banda 4 e 5 para geração do NDVI. As análises demonstram que no mês de abril a temperatura da superfície variou aproximadamente entre 23.2ºC e 31.06ºC, enquanto no mês correspondente a setembro, os valores variaram de 25°C e 40.5°C, sendo que as maiores temperaturas foram encontradas em locais com baixa densidade de vegetação, de acordo com a carta de NDVI desses dois meses. A maior diferença de temperatura desses dois meses foi de 14.2°C indicando que ocorre um aumento da temperatura proporcionado pelo período que corresponde a um dos mais secos da região, diferentemente de abril que está no período de chuvas e tem uma maior umidade, presença de vegetação e corpos d’água que amenizam a temperatura.Palavras-chave: Sensoriamento Remoto; Vegetação; Microbacia.                                                                                  ABSTRACTThe objective of the present work is to compare the surface temperature and the Normalized Difference Vegetation Index (NDVI) in the Batateiras / Crato-CE river basin in two periods of 2017, one rainy (April) and one (September) and to analyze the temperature difference map in these two periods. LANDSAT 8 (band 10) satellite images were used for temperature measurement and band 4 and 5 for NDVI generation. The analyzes show that in April the surface temperature varied approximately between 23.2ºC and 31.06ºC, while in the month corresponding to September, the values ranged from 25ºC and 40.5ºC, and the highest temperatures were found in locations with low density of vegetation, according to the NDVI letter of these two months. The highest difference in temperature for these two months was 14.2 ° C, indicating that there is an increase in temperature provided by the period that corresponds to one of the driest in the region, unlike April that is in the rainy season and has a higher humidity, presence of vegetation and water bodies that soften the temperature.Key-words: Remote sensing; Vegetation; Microbasin.RESUMENEl objetivo del presente trabajo es hacer una comparación entre la temperatura de la superficie y el Índice de Vegetación de Diferencia Normalizada (NDVI) en la cuenca Batateiras / Crato-CE en dos períodos de 2017, uno lluvioso (abril) y uno (Septiembre), así como analizar el mapa de diferencia de temperatura en estos dos períodos. Las imágenes de satélite LANDSAT 8 (banda 10) se utilizaron para la medición de temperatura y las bandas 4 y 5 para la generación de NDVI. Los análisis muestran que en abril la temperatura de la superficie varió aproximadamente entre 23.2ºC y 31.06ºC, mientras que en el mes correspondiente a septiembre, los valores oscilaron entre 25 ° C y 40.5 ° C, y las temperaturas más altas se encontraron en lugares con baja densidad de vegetación, según el gráfico NDVI de estos dos meses. La mayor diferencia de temperatura de estos dos meses fue de 14.2 ° C, lo que indica que hay un aumento en la temperatura proporcionada por el período que corresponde a uno de los más secos de la región, a diferencia de abril que está en la temporada de lluvias y tiene una mayor humedad, presencia de vegetación y cuerpos de agua que suavizan la temperatura.Palabras clave: Detección remota; vegetación; Cuenca.

scholarly journals Evaluation of the Consistency of Simultaneously Acquired Sentinel-2 and Landsat 8 Imagery on Plastic Covered Greenhouses

2020 ◽  
Vol 12 (12) ◽  
pp. 2015 ◽  
Author(s):  
Manuel Ángel Aguilar ◽  
Rafael Jiménez-Lao ◽  
Abderrahim Nemmaoui ◽  
Fernando José Aguilar ◽  
Dilek Koc-San ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Remote Sensing Techniques ◽  
Image Pairs ◽  
Study Sites ◽  
High Level ◽  
Sentinel 2

Remote sensing techniques based on medium resolution satellite imagery are being widely applied for mapping plastic covered greenhouses (PCG). This article aims at testing the spectral consistency of surface reflectance values of Sentinel-2 MSI (S2 L2A) and Landsat 8 OLI (L8 L2 and the pansharpened and atmospherically corrected product from L1T product; L8 PANSH) data in PCG areas located in Spain, Morocco, Italy and Turkey. The six corresponding bands of S2 and L8, together with the normalized difference vegetation index (NDVI), were generated through an OBIA approach for each PCG study site. The coefficient of determination (r2) and the root mean square error (RMSE) were computed in sixteen cloud-free simultaneously acquired image pairs from the four study sites to evaluate the coherence between the two sensors. It was found that the S2 and L8 correlation (r2 > 0.840, RMSE < 9.917%) was quite good in most bands and NDVI. However, the correlation of the two sensors fluctuated between study sites, showing occasional sun glint effects on PCG roofs related to the sensor orbit and sun position. Moreover, higher surface reflectance discrepancies between L8 L2 and L8 PANSH data, mainly in the visible bands, were always observed in areas with high-level aerosol values derived from the aerosol quality band included in the L8 L2 product (SR aerosol). In this way, the consistency between L8 PANSH and S2 L2A was improved mainly in high-level aerosol areas according to the SR aerosol band.

ANALISIS PERBANDINGAN METODE NORMALIZED VEGETATION INDEX DAN MAXIMUM LIKELIHOOD CLASSIFICATION UNTUK ANALISIS RUANG TERBUKA HIJAU (Studi Kasus di Kota Pekalongan, Jawa Tengah)

2021 ◽  
pp. 513
Author(s):  
Mohammad Slamet Sigit Prakoso ◽  
Rizki Dwi Safitri
Keyword(s):  
Maximum Likelihood ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Maximum Likelihood Classification

Ruang Terbuka Hijau (RTH) adalah suatu tempat yang luas dan terbuka yang dimaksudkan untuk penghijauan suatu kota, di mana di dalamnya ditumbuhi pepohonan. Dalam analisis ruang terbuka hijau dapat menggunakan beberapa metode, di antaranya yaitu metode Normalized Difference Vegetation Index (NDVI) dan metode Maximum Likelihood Classification. Tujuan penelitian ini untuk mengetahui perbedaan hasil dari analisis metode NDVI dan Maximum Likelihood Classification yang digunakan untuk mengetahui ruang terbuka hijau di Kota Pekalongan. Metode yang digunakan pada penelitian ini yaitu dengan menggunakan metode NDVI dan metode Maximum Likelihood Classification. Data yang digunakan yaitu Citra Landsat 8 OLI. Pengolahan data menggunakan software Arcgis 10.3. Hasil dari pengolahan berupa peta ruang terbuka hijau dari masing - masing metode. Secara kuantitatif dari hasil perhitungan luas metode NDVI, luas permukiman sebesar 3.016,53 ha, persawahan 609,39 ha, hutan kota 573,3 ha, dan badan air seluas 482,04 ha. Sedangkan untuk metode Maximum Likelihood Classification didapatkan hasil luas permukiman 2.278,26 ha, persawahan 1.141,83 ha, hutan kota 738,18 ha, dan badan air seluas 522,99 ha. Berdasarkan luasan RTH terhadap luas Kota Pekalongan, pada metode NDVI sebesar 25,2%, sedangkan untuk metode Maximum Likelihood Classification sebesar 40,1%. Dari hasil analisis diperoleh perbedaan luasan yang cukup signifikan yaitu pada luasan persawahan dan permukiman. Perbedaan hasil analisis terjadi akibat perbedaan klasifikasi warna citra pada saat pengolahan data.

scholarly journals ESTIMASI PRODUKSI PADI DENGAN ANALISIS CITRA SATELIT LANDSAT 8 DI KABUPATEN KLUNGKUNG PROVINSI BALI

2018 ◽  
Vol 12 (1) ◽  
pp. 93
Author(s):  
Made Arya Bhaskara Putra ◽  
I Wayan Nuarsa ◽  
I Wayan Sandi Adnyana
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Image Data ◽  
Research Area ◽  
Rice Production ◽  
Landsat 8 ◽  
Estimation Model ◽  
Accuracy Test ◽  
Accuracy Level

Rice crop is one of the important commodities that must always be available, so estimation of rice production becomes very important to do before harvesting time to know the food availability. The technology that can be used is remote sensing technology using Landsat 8 Satellite. The aims of this study were (1) to obtain the model of estimation of rice production with Landsat 8 image analysis, and (2) to know the accuracy of the model that obtained by Landsat 8. The research area is located in three sub-districts in Klungkung regency. Analysis in this research was conducted by single band analysis and analysis of vegetation index of satellite image of Landsat 8. Estimation model of rice production was developed by finding the relationship between satellite image data and rice production data. The final stage is the accuracy test of the rice production estimation model, with t test and regression analysis. The results showed: (1) estimation of rice production can be calculated between 67 to 77 days after planting; (2) there was a positive correlation between NDVI (Normalized Difference Vegetation Index) vegetation index value with rice yield; (3) the model of rice production estimation is y = 2.0442e1.8787x (x is NDVI value of Landsat 8 and y is rice production); (4) The results of the model accuracy test showed that the obtained model is suitable to predict rice production with accuracy level is 89.29% and standard error of production estimation is + 0.443 ton/ha. Based on research results, it can be concluded that Landsat 8 Satellite image can be used to estimate rice production and the accuracy level is 89.29%. The results are expected to be a reference in estimating rice production in Klungkung Regency.

scholarly journals Consideration of NDVI thematic changes in density analysis and floristic composition of Wadi Yalamlam, Saudi Arabia

2018 ◽  
Vol 7 (4) ◽  
pp. 297-306 ◽  
Author(s):  
Amal Y. Aldhebiani ◽  
Mohamed Elhag ◽  
Ahmad K. Hegazy ◽  
Hanaa K. Galal ◽  
Norah S. Mufareh
Keyword(s):  
Saudi Arabia ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Floristic Composition ◽  
Water Source ◽  
Shannon Index ◽  
Landsat 8 ◽  
Main Stream

Abstract. Wadi Yalamlam is known as one of the significant wadis in the west of Saudi Arabia. It is a very important water source for the western region of the country. Thus, it supplies the holy places in Mecca and the surrounding areas with drinking water. The floristic composition of Wadi Yalamlam has not been comprehensively studied. For that reason, this work aimed to assess the wadi vegetation cover, life-form presence, chorotype, diversity, and community structure using temporal remote sensing data. Temporal datasets spanning 4 years were acquired from the Landsat 8 sensor in 2013 as an early acquisition and in 2017 as a late acquisition to estimate normalized difference vegetation index (NDVI) changes. The wadi was divided into seven stands. Stands 7, 1, and 3 were the richest with the highest Shannon index values of 2.98, 2.69, and 2.64, respectively. On the other hand, stand 6 has the least plant biodiversity with a Shannon index of 1.8. The study also revealed the presence of 48 different plant species belonging to 24 families. Fabaceae (17 %) and Poaceae (13 %) were the main families that form most of the vegetation in the study area, while many families were represented by only 2 % of the vegetation of the wadi. NDVI analysis showed that the wadi suffers from various types of degradation of the vegetation cover along with the wadi main stream.

scholarly journals Assessing and Simulating Impacts of Land Use Land Cover Changes on Land Surface Temperature in Mymensingh City, Bangladesh

2021 ◽  
Vol 20 (2) ◽  
pp. 1-19
Author(s):  
Tahmid Anam Chowdhury ◽  
◽  
Md. Saiful Islam ◽  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Bare Soil ◽  
Land Use Land Cover ◽  
Remarkable Change

Urban developments in the cities of Bangladesh are causing the depletion of natural land covers over the past several decades. One of the significant implications of the developments is a change in Land Surface Temperature (LST). Through LST distribution in different Land Use Land Cover (LULC) and a statistical association among LST and biophysical indices, i.e., Urban Index (UI), Bare Soil Index (BI), Normalized Difference Builtup Index (NDBI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI), this paper studied the implications of LULC change on the LST in Mymensingh city. Landsat TM and OLI/TIRS satellite images were used to study LULC through the maximum likelihood classification method and LSTs for 1989, 2004, and 2019. The accuracy of LULC classifications was 84.50, 89.50, and 91.00 for three sampling years, respectively. From 1989 to 2019, the area and average LST of the built-up category has been increased by 24.99% and 7.6ºC, respectively. Compared to vegetation and water bodies, built-up and barren soil regions have a greater LST each year. A different machine learning method was applied to simulate LULC and LST in 2034. A remarkable change in both LULC and LST was found through this simulation. If the current changing rate of LULC continues, the built-up area will be 59.42% of the total area, and LST will be 30.05ºC on average in 2034. The LST in 2034 will be more than 29ºC and 31ºC in 59.64% and 23.55% areas of the city, respectively.

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