Multi-temporal Satellite Data Research Articles

Analysis of the Distribution of Carbon Monoxide Gas and Nitrogen Dioxide Gas and Urban Heat Island (UHI) Based on Multi-Temporal Satellite Data During the Implementation of the Covid-19

The research aims to determine the distribution and concentration levels of CO and NO2 gases based on Sentinel-5P Satellite Image processing and UHI levels based on Landsat 8 Image processing during the implementation of Covid-19 Large-Scale Social Restrictions (PSBB) in Pekanbaru City. Data processing begins by extracting CO and NO2 gas data as well as Landsat 8 imagery via cloud computing on Google Earth Engine (GEE). The PSBB policy in Pekanbaru City came into effect in 2020. The PSBB policy will limit community activities, which will affect the concentration of CO and NO2 gases as well as UHI. The research results show that before the 2019 Covid-19 PSBB the respective concentrations of CO and NO2 gases were (CO: 4.16 x 10-3 mol/m2 – 4.70 x 10-3 mol/m2) and (NO2: 4, 16 x 10-5 mol/m2 – 5.68 x 10-5 mol/m2) with a UHI temperature of 34.23 0C. Then, when the PSBB was implemented in 2020, there was a decrease in the concentration of CO and NO2 gases to (CO: 2.87 x 10-3 mol/m2 – 3.20 x 10-3 mol/m2) and (NO2: 2.98 x 10-5 mol/m2 – 5.28 x 10-5 mol/m2) with the UHI temperature decreasing to 30.24 0C. However, CO and NO2 gas concentrations increased again after PSBB in 2021 to (CO: 2.90 x 10-3 mol/m2 – 3.31 x 10-3 mol/m2) and (NO2: 2.87 x 10-5 mol/m2 – 5.76 x 10-5 mol/m2) which was followed by an increase in the UHI temperature to 32.62 0C. Therefore, it is known that the concentration of CO and NO2 gases will affect UHI. If the concentration of CO and NO2 gas increases, the UHI temperature will also increase.

Erosion of Rivers and their Impact on Land Ownership of Vernacular Settlements: Insights from Two Settlements Near the Tigris and Euphrates Rivers in Iraq

It is usual for human settlements to rise along the river banks because rivers provide water: the major source of sustenance of communities. However, riverbank erosion along rivers has huge implication for these settlements that border them. The Tigris and Euphrates rivers in Iraq causes land loss that impacts adjacent properties and land ownership rights. This study examines such erosion and land submersion in two locations in Iraq: along the Euphrates in Ramadi and the Tigris in Baiji. The research employs historical maps and multi-temporal satellite data from 1968-2022. The GIS analysis quantified erosion areas and compared changes to cadastral records to determine direct impacts to property boundaries and land rights. The results identified significant conversion of privately held waterfront land into nonusable flooded river channels, amounting to billions in loss of value. Owners forfeited all functional land access without current provisions for relocations or erosion mitigation planning. The research concludes that intensifying hydrologic shifts could exponentially increase economic disruption for vulnerable property holders without integrated monitoring tools and policy responses to the dynamic river morphologies.

Sabarmati and its connection with the Harappan port Lothal and the Nal corridor: A study using multi-sensor data, cloud-computing and multi-platforms

Lothal is situated approximately 30 km inland from the Gulf of Khambhat on the western coast of India in Gujarat. It served as a bustling port during the Bronze Age Harappan period (2600 BCE to 1900 BCE) and was connected to a series of Harappan production and distribution centres dotted along the Gulf of Kachchh and the Rann of Kachchh. Artefacts of foreign nature found in Lothal attest to its intercultural trade relation. Further, Lothal is renowned for its well-preserved brick-walled dockyard and warehouse. However, different views for and against the dockyard are available in archaeological literature. This study revisits the dockyard theory from the landscape perspective and integrates information extracted from early maps, multi-spectral and multi-temporal satellite data of different spatial resolutions, and Digital Elevation Models to understand the larger context of the palaeolandscape around Lothal, including both fluvial and coastal areas. The findings offer fresh perspectives on the hydrographic dynamics of the region, highlighting an evident direct connection between Lothal and the former course of River Sabarmati, one of the major rivers in Gujarat that currently flows 20 km east of Lothal. The reconstruction of the palaeolandscape, both fluvial and coastal, complements and verifies certain interpretations related to the dockyard theory, partially bridging the ‘credibility gap’ in the broader discussion and strengthening the arguments in favour of the dockyard theory. Additionally, the study underscores the significance of integrating various types of geospatial data to understand past landscapes. Further investigation of the reconstructed palaeolandscape, particularly the complex hydrographic network including rivers and creeks, of this historically important region has the potential to contribute to new insights into existing archaeological and geomorphological interpretations and may guide us about the past human adaptation to the natural dynamics.

Archaeology in the Fourth Dimension: Studying Landscapes with Multitemporal PlanetScope Satellite Data

AbstractFor the last seven years, PlanetScope satellites have started near-daily imaging of parts of the Earth’s surface, making high-density multitemporal, multispectral, 3-m pixel imagery accessible to researchers. Multitemporal satellite data enables landscape archaeologists to examine changes in environmental conditions at time scales ranging from daily to decadal. This kind of temporal resolution can accentuate landscape features on the ground by de-emphasizing non-permanent signatures caused by seasonal or even daily changes in vegetation. We argue that the availability of high spatial and temporal resolution multispectral imagery from Planet Inc. will enable new approaches to studying archaeological visibility in landscapes. While palimpsests are discrete overlapping layers of material accumulation, multitemporal composites capture cyclical and seasonal time and can be used to interpret past landscape histories at multiple scales. To illustrate this perspective, we present three case studies using PlanetScope imagery in tropical environments on the Indian Ocean islands of Madagascar, Mauritius, and Zanzibar.

Optimal Timing of Carrot Crop Monitoring and Yield Assessment Using Sentinel-2 Images: A Machine-Learning Approach

Remotely sensed images provide effective sources for monitoring crop growth and the early prediction of crop productivity. To monitor carrot crop growth and yield estimation, three 27 ha center-pivot irrigated fields were studied to develop yield prediction models using crop biophysical parameters and vegetation indices (VIs) extracted from Sentinel-2A (S2) multi-temporal satellite data. A machine learning (ML)-based image classification technique, the random forest (RF) algorithm, was used for carrot crop monitoring and yield analysis. The VIs (NDVI, RDVI, GNDVI, SIPI, and GLI), extracted from S2 satellite data for the crop ages of 30, 45, 60, 75, 90, 105, and 120 days after plantation (DAP), and the chlorophyll content, SPAD (Soil Plant Analysis Development) meter readings, were incorporated as predictors for the RF algorithm. The RMSE of the five RF scenarios studied ranged from 7.8 t ha−1 (R2 ≥ 0.82 with Scenario 5) to 26.2 t ha−1 (R2 ≤ 0.46 with Scenario 1). The optimal window for monitoring the carrot crop for yield prediction with the use of S2 images could be achieved between the 60 DAP and 75 DAP with an RMSE of 8.6 t ha−1 (i.e., 12.4%) and 11.4 t ha−1 (16.2%), respectively. The developed RF algorithm can be utilized in carrot crop yield monitoring and decision-making processes for the self-sustainability of carrot production.

Assessing the impact of land use land cover changes on soil moisture and vegetation cover in Southern Punjab, Pakistan using multi-temporal satellite data

ABSTRACT Change in land use land cover (LULC) is driven by human activities and drives changes that limit the accessibility of services and products for livestock and humans. This study aimed to develop the spatio-temporal changes in LULC, vegetation cover, and moisture index using multi-temporal satellite data in Southern Punjab, Pakistan. Analysis of satellite data was accomplished using ArcGIS 10.4 software. Based on ground-truthing, the supervised classification technique (maximum likelihood algorithm) was used to achieve the LULC classification. The LULC change analysis revealed that vegetation area converted to the build-up area by 7.17 % and bare soil converted to urban areas by 1.68 % from 2000 to 2021. Average NDVI values were calculated at 0.23, 0.17, 0.19, and 0.14 for 2000, 2007, 2014, and 2021, respectively. In the study area, average NDMI values were observed at 0.28, 0.25, 0.2, and 0.15 for 2000, 2007, 2014, and 2021, respectively. Based on our study, the general trend in Southern Punjab is a decrease in vegetation cover, moisture index, and forest area due to an increase in build-up areas. Assessments of LULC and NDMI changes, as well as estimates of the effect on the environment, are necessary for many policy decisions and planning.

Shoreline Changes and Sediment Transport along Nhat Le Coast, Vietnam

One of the most beautiful beaches in Northern Vietnam, Nhat Le, has recently experienced severe erosion as a result of the ensemble interaction of natural factors, such as tropical cyclones, extreme weather events, and human activities. Consequently, negative impacts on tourism and social and economic development have been recorded. This paper aims to provide a deep understanding of the changes in shoreline and longshore sediment transport at Nhat Le estuary based on two modules of LITDRIFT and LITLINE of the LITPACK software package combined with geospatial analysis. The rate of change statistics is calculated using the Digital Shoreline Analysis System (DSAS) from 30-year multi-temporal satellite data (1989-2019) for multiple historical shoreline positions. The Module of LITDRIFT is employed to estimate sediment transport and the shoreline position calculated from the LITLINE module. These data are then compared with measured topographic data and satellite images. Wave climate conditions are incorporated into the LITDRIFT module to identify the volume of sediment transport along the coast on seasonal and annual bases. The results illustrate that a mean erosion rate of about 2 m per year was observed in the southern sandspit of Nhat Le from 1989 to 2019. This rate reaches 4.5 m per year during 2009-2019.

Identification of Pine Wilt Disease-Infested Stands Based on Single- and Multi-Temporal Medium-Resolution Satellite Data

Pine wilt disease (PWD) is known for its high lethality and rapid transmission, earning it the name “cancer of the pine tree”. The prompt removal of infested pine trees is an effective measure for preventing and controlling pine wilt disease. Accurate and efficient monitoring technologies are crucial for the scientific prevention and control of this plant disease. Currently, numerous remote sensing monitoring studies have been conducted on pine wilt disease. However, there is limited research on the temporal identification of PWD-infested forest stands over large areas. To build classification models, this study utilized three machine learning algorithms: artificial neural network (ANN), random forest (RF), and support vector machine (SVM). We aimed to investigate the effectiveness of single-temporal and multi-temporal Landsat and Sentinel-2 satellite images PWD-infested forest stands detection. The results indicated that, at a spatial resolution of 30 m, Landsat-9 and Sentinel-2 remote sensing images effectively identified PWD-infested forest stands, with classification accuracies of 77.87% and 78.91%, respectively. Higher spatial resolutions in Sentinel-2 remote sensing images were associated with improved identification capabilities. Furthermore, multi-temporal Landsat satellite data (with a classification accuracy of 85.95%) significantly enhanced the performance of the monitoring model compared to single-temporal Landsat satellite data (with a classification accuracy of 77.87%). The RGI difference was found to be the optimal vegetation index. In conclusion, by combining multi-temporal and single-time-phase Landsat remote sensing data, a monitoring model for PWD-infested forest stands was constructed. It achieved a classification accuracy of 88.26%. In this study, a higher accuracy in identifying pine wilt disease and a lower economic cost were achieved by Landsat and Sentinel images, offering valuable insights for the management of pine wilt disease.

Assessing the impact of anthropogenic activities on land use and land cover changes in the semi-arid and arid regions of Algeria.

Land use and land cover are critical factors that influence the environment and human societies. The dynamics of LULC have been constantly changing over the years, and these changes can be analyzed at different spatial and temporal scales to evaluate their impact on the natural environment. This study employs multitemporal satellite data to investigate the spatial and temporal transformations that occurred in Sidi Bel Abbes province, situated in the northwestern region of Algeria, spanning from the early 1990s to 2020. Notably, this province is marked by semi-arid and arid climates and hosts a wide range of areas susceptible to gravitational hazards, especially concerning alterations in land use and forest fires. The interactive supervised classification tool utilized multiple machine learning algorithms including Random Forest, Support Vector Machine, Classification and Regression Tree, and Naïve Bayes to produce land cover maps with six main classes: forest, shrub, agricultural, pasture, water, and built-up. The findings showed that the LULC in the research area is undergoing continuous change, particularly in the forest and agricultural lands. The forest area has decreased significantly from 10.80% in 1990 to 5.25% in 2020, mainly due to repeated fires. Agricultural land has also undergone fluctuations, with a decrease between 1990 and 2000, followed by a fast increase and near stabilization in 2020. At the same time, pasture lands and built-up areas grew steadily, increasing by 11% and 13% respectively. This research highlights the significant impact of anthropogenic activities on LULC changes in the study area and can provide valuable insights for promoting sustainable land use policies.

Assessing tea plantations biophysical and biochemical characteristics in Northeast India using satellite data.

Despite advancements in using multi-temporal satellite data to assess long-term changes in Northeast India's tea plantations, a research gap exists in understanding the intricate interplay between biophysical and biochemical characteristics. Further exploration is crucial for precise, sustainable monitoring and management. In this study, satellite-derived vegetation indices and near-proximal sensor data were deployed to deduce various physico-chemical characteristics and to evaluate the health conditions of tea plantations in northeast India. The districts, such as Sonitpur, Jorhat, Sibsagar, Dibrugarh, and Tinsukia in Assam were selected, which are the major contributors to the tea industry in India. The Sentinel-2A (2022) data was processed in the Google Earth Engine (GEE) cloud platform and utilized for analyzing tea plantations biochemical and biophysical properties. Leaf chlorophyll (Cab) and nitrogen contents are determined using the Normalized Area Over Reflectance Curve (NAOC) index and flavanol contents, respectively. Biophysical and biochemical parameters of the tea assessed during the spring season (March-April) 2022 revealed that tea plantations located in Tinsukia and Dibrugarh were much healthier than the other districts in Assam which are evident from satellite-derived Enhanced Vegetation Index (EVI), Modified Soil Adjusted Vegetation Index (MSAVI), Leaf Area Index (LAI), and Fraction of Absorbed Photosynthetically Active Radiation (fPAR), including the Cab and nitrogen contents. The Cab of healthy tea plants varied from 25 to 35µg/cm2. Pearson correlation among satellite-derived Cab and nitrogen with field measurements showed R2 of 0.61-0.62 (p-value < 0.001). This study offered vital information about land alternations and tea health conditions, which can be crucial for conservation, monitoring, and management practices.

Identification of vulnerable areas using geospatial technologies in the lower Manair River basin of Telangana, Southern India

The present study is undertaken at the lower Manair River basin, the central part of Telangana in southern India, as the Government authorities has done rapid developmental activities over the last seven years demanded identifying change detection in environmental indicators. Geospatial tools, viz., remote sensing and geographic information system, are used to classify and identify land-use/land cover (LULC) changes during 2015–2021 based on the images collected from Landsat 8 OLI/TIRS multispectral and multi-temporal satellite data set. Change detection of environmental indicators such as LULC changes, land surface temperature (LST), and normalized difference vegetation index (NDVI) are crucial parameters that play a vital role in identifying vulnerable areas, which helps in planning and utilizing natural resources. The results revealed that the agricultural cropland filled in the top class with an area of 1124.70 km2 (33.82%) in 2015, amplified to 1442.95 km2 (43.39%) in 2021. The built-up area has marginally raised by 581.45 km2 (17.49%) in the study region. A negative correlation is observed between LST and NDVI. LULC patterns are changing, and its imprint echoed on a surge of LST. It establishes a foundation for evidence-based policymaking and sustainable growth, emphasizing the importance of proactive measures in preserving the delicate balance between development and environmental conservation.

A COMPARISON OF PRE-PROCESSING APPROACHES FOR REMOTELY SENSED TIME SERIES CLASSIFICATION BASED ON FUNCTIONAL ANALYSIS

Abstract. Satellite remote sensing has gained a key role for vegetation mapping distribution. Given the availability of multi-temporal satellite data, seasonal variations in vegetation dynamics can be used trough time series analysis for vegetation distribution mapping. These types of data have a very high variability within them and are subjected by artifacts. Therefore, a pre-processing phase must be performed to properly detect outliers, for data smoothing process and to correctly interpolate the data. In this work, we compare four pre-processing approaches for functional analysis on 4-years of remotely sensed images, resulting in four time series datasets. The methodologies presented are the results of the combination of two outlier detection methods, namely tsclean and boxplot functions in R and two discrete data smoothing approaches (Generalized Additive Model ”GAM” on daily and aggregated data). The approaches proposed are: tsclean-GAM on aggregated data (M01), boxplot-GAM on aggregated data (M02), tsclean-GAM on daily data (M03), boxplot-GAM on daily data (M04). Our results prove that the approach which involves tsclean function and GAM applied to daily data (M03) is ameliorative to the logic of the procedure and leads to better model performance in terms of Overall Accuracy (OA) which is always among the highest when compared with the others obtained from the other three different approaches.

Crop productivity estimation by integrating multisensor satellite, in situ, and eddy covariance data into efficiency-based model.

Accurate and quantitative regional estimates of the carbon budget require an integration of eddy covariance (EC) flux-tower observations and remote sensing in ecosystem models. In this study, a simple remote sensing driven light use efficiency (LUE) model was used to estimate the primary productivity for major cropping systems using multi-temporal satellite data over the Saharanpur district in India.The model is based on radiation absorption and its conversion into biomass. The LUE model was implemented for major crop rotations derived from the time-series of Sentinel-2 and Landsat 8 with monthly satellite-based spatially explicit fields of photosynthetically active radiation (PAR), fraction of absorbed PAR (fAPAR) and down-regulated light use efficiency. Incident PAR and fAPAR were estimated on monthly basis from the ground-calibrated empirical equation using INSAT-3D insolation product and remote sensing-based vegetation indices, respectively. Spatial LUE maps created by down-regulating maximum LUE (EC tower-based) with water and temperature stressors derived from land surface water index (LSWI) and EC-based cardinal temperature, respectively. LUE-based modeled GPP over the sugarcane-wheat system was found higher than the rice-wheat system in Saharanpur district. This is because C4 crop (sugarcane) has very high photosynthetic efficiency compared to C3 crops (rice and wheat). Modeled GPP over the sugarcane-wheat system was found in good agreement with observed EC tower-based GPP (Index of Agreement = 0.93). Further regionally calibrated remote sensing-based LUE model well captures gross photosynthesis rates (GPP) over cropland ecosystem compared to globally modeled MODIS GPP product.

Modeling NO2 air pollution variation during and after COVID-19-regulation using principal component analysis of satellite imagery

By implementing Principal Component Analysis (PCA) of multitemporal satellite data, this paper presents modeling solutions for air pollutant variation in three scenarios related to COVID-19 lockdown: pre, during, and after lockdown. Tropospheric NO2 satellite data from Sentinel-5P was used. Two novel PCA-models were developed: Weighted Principal Component Analysis (WPCA) and Rescaled Principal Component Analysis (RPCA). Model results were tested for goodness-of-fit to empirical NO2 data. The models were used to predict actual near-surface NO2 concentrations. Model-predicted NO2 concentrations were validated with NO2 data acquired at ground monitoring stations. Besides, meteorological bias affecting NO2 was assessed. It was found that the weather component had substantial impact on NO2 built-ups, propitiating air pollutant decrease during lockdown and increase after. WPCA and RPCA models well fitted to observed NO2. Both models accurately estimated near-surface NO2 concentrations. Modeled NO2 variation results evidenced the prolongated effect of the total lockdown (up to half a year). Model-predicted NO2 concentrations were found to highly correlate with monitoring station NO2 data collected on the ground. It is concluded that PCA is reliable in identifying and predicting air pollution variation patterns. The implementation of PCA is recommended when analyzing other pollutant gases.

Automatic detection of crop lodging from multitemporal satellite data based on the isolation forest algorithm

Rapid identification of crop lodging has implications for optimizing disaster prevention and mitigation measures. This study proposes a sequentially integrated algorithm for crop lodging identification, which first automatically extracts lodged and non-lodged samples based on the isolation forest algorithm from the screened features dataset and then uses a supervised classifier for lodging identification. Taking the crop lodging disaster caused by the super typhoon “Maysak” in Bohetai Township as an example, we calculated 162 feature indices from Sentinel-1 and Sentinel-2 satellite data pre- and post-disaster and screened the optimal features. Then, the abnormal scores of the cultivated-land pixels were calculated using the isolation forest algorithm. After that, we calculated the coefficient of variation (CV) of the abnormal scores of each group, extracted the lodged and non-lodged samples based on the cumulative CV contribution rate, and input samples into three supervised classifiers (random forest, maximum likelihood, and support vector machine) to identify lodged crop pixels. The results showed that feature screening increased the identification accuracy of crop lodging by more than 10% and difference features of short-wave infrared (SWIR) and red edge bands contributed significantly to the identification. The accuracy of crop lodging automatic identification was verified using samples obtained from visual interpretation and digitization of UAV images as reference data and the overall accuracy reached 78%. The proposed method can simultaneously recognize lodging of different crop types without the need to select lodged samples and extract lodged ranges for each crop. With low data acquisition costs and high automation, the proposed method has promising applications in agricultural disaster emergency management and agricultural insurance loss assessment.

Analysis of the Shoreline Changes using Geoinformatics in Ghoramara Island of Hugli Estuary, West Bengal in India

Conceptual FrameworkCoastal decrepitude is the loss of topographical resources along the coast as a result of silt, temperature fluctuations, tidal shifts, and wave breaking. Hydraulic activity, scraping, abrasion, and erosion are the major causes of erosion; other factors that affect it include the condition of the shore, rocks, joints, fissures, marine chemical reactions, and wave force. ObjectivesThe primary objectives of this study are to analyze the coastline changes between 1972 and 2022 utilizing remote sensing and GIS data and forecast the changes in the shoreline in 2032. MethodsThe study examined coastal areas from 1972 to 2022 using multi-temporal satellite data from Landsat TM and Landsat OLI/TIRS. Shoreline change and the calculations related to it were examined using the Digital Shoreline Analysis System using near-infrared bands and tasseled cap transformation, to determine the rate of change in the shoreline, the study also employed Shoreline Change Envelope and End Point Rate techniques. ResultsCoastal erosion caused substantial damage to the island between 1972 and 2022. The northern, southeastern, and western regions of the coast are seeing the highest levels of coastal erosion. The south coast experiences considerable erosion, whereas the west and southeast coasts experience the least. The EPR of Ghoramara Island has mean, minimum, and maximum changes of -10.59, -4.13, and -35.93, respectively. A notable inshore shift has occurred in the northern portion of Ghoramara, extending from 676 meters to 855 meters. The study also uses tidal gauge records and Revised Local Reference data from the Haldia Gauge Station to track long-term sea level variations. According to the study, Ghoramara Island's shoreline will change by 2032, with the greatest negative changes expected to occur around the island's western and southern borders. ConclusionThe study shows a significant decrease in Ghoramara's shoreline regions between 1972 and 2022, with a 3,000-population shifted from the place in 2016 due to Lohachara's submergence. Changes in the island's morphology and human activities have reduced cultivation. The study suggests increasing mangrove vegetation to combat coastal erosion and prevent further displacement of people to neighboring islands.

Detection of Land Use/Land Cover Change and Land Surface Temperature in the Eastern Part of Batna City (North East Algeria) Using Remote Sensing Data and GIS

&#x0D; &#x0D; &#x0D; The present paper aims to evaluate the accuracy of classifying Land Use /Land Cover (LULC) types and assesses the trends of their changes in the Eastern Part of Batna City (Northernf Algeria) using remote sensing and GIS. The accuracy of image Land Satellite (Land Sat ) was evaluated using the supervised classification technique, it’s applied in multi spectral and multi temporal satellite data acquired in 2000,2010,2022 and assessed with GOOGLE EARTH PRO and IMAGERY Land Use and topographical map. The second part focused on extraction of LST in three phases and explored the relationship between two land cover indices (NDVI, NDBI) and LST.LU/LC detected, quantified, and stati- cally analyzed, the result indicate that from 2000-2022 the built-up areas increased by 0.34% (6.638km2), the forest area increased by 1.8% (35.144km2), agricultural land cover increased by 1.12% (21.867km2), while bare land decreased by 2.17% (42.368km2). The conversions of areas from bare land to urban land represent the most significant Land Cover changes. The accuracy assessment and correlation coefficient R2 analysis in this study affirms the previous research findings. Even a single land use unit like built-up area, bare land and vegetation also create differences in LST (R2 of NDBI vs. LST ranges from 0.64 to 0.79; NDVI vs. LST ranges from -0.73 to -0.82). With the change of the LU/LC style, its imprint is reflected on the LST. Therefore, immediate reflection on new urbanism must be adopted, initiated and implemented to stop the warming that contributes to climate change in the study area.&#x0D; &#x0D; &#x0D;

Modelling and Monitoring Land Use: Land Cover Change Dynamics of Cooch Behar District of West Bengal using Multi-Temporal Satellite Data

Modelling and Monitoring Land Use: Land Cover Change Dynamics of Cooch Behar District of West Bengal using Multi-Temporal Satellite Data

Monitoring Land Use/ Cover Dynamics of Achanakmar Tiger Reserve (ATR), India by Using Multi-Temporal Satellite Data and Future Scenario

Monitoring Land Use/ Cover Dynamics of Achanakmar Tiger Reserve (ATR), India by Using Multi-Temporal Satellite Data and Future Scenario

Satellite Based Approach for Assessing Land Cover Changes of Modhupur Sal Forest Regions in Bangladesh: Its Dynamics and Impacts on Surface Temperature

Land Use and Land Cover (LULC) changes have a significant impact on climate changes in the local to global scales. In this paper, we studied the impact of LULC changes of Modhupur Sal forest and its adjoining areas in Bangladesh on LST using multi temporal satellite data. Seasonal and temporal values of LST were determined for three decades from 1989 to 2019. It was seen from the LULC study that the Sal forest area has changed a lot over the time period. Through the long-term time series data, it is found that with the change of LULC, LST of the area has increased. An inverse correlation between the NDVI and LST has been observed from 1989 to 2009 in winter and 1993 to 2011 in summer. But in 2019, both in winter and summer, with the increase of NDVI value of the area, LST was seen to increase.