Landsat Thematic Mapper Research Articles

Land Use and Land Cover Change Detection in the Bhavani Basin of India through Remote Sensing Approach

Using remote sensing data to detect changes in land use and land cover (LULC) is a valuable source of information for various decision support systems. Land use and land cover identification data was used to analyse land conservation, sustainable development, and water resource management. This research aims to determine how the Bhavani basin land use and land cover have changed over the period of time. Land cover changes were detected using Landsat Thematic Mapper (TM) 30 m resolution images in the GIS environment and with image processing techniques for the four years 1999, 2007, 2014, and 2020. The differences in the landuse and land cover classes are described using ERDAS imagine version 2015 and ARC GIS software. The four land cover classes viz. water body, built-up land, barren land, and vegetation were used to classify the region. The accuracy evaluation was assessed separately using the kappa coefficient after carefully examining the image pre-processing and classification. The overall accuracy in the basin was found to be 83.23%, 86.45%, 85.83%, and 88.75 % with a kappa coefficient of 0.79, 0.81, 0.87, 0.85 for the years 1999, 2007, 2014, and 2020 respectively. The Bhavani basin is mostly covered by barren and vegetation. According to the findings, the basin's built-up area has risen by 1.5 percentage to 3.5 percentage in the last 20 years. The increase in the vegetation area and reduction in the barren area may lead to low soil erosion.

Change detection of Jamuna River and its impact on the local settlements

ABSTRACT Spatio-temporal change detection analysis of Jamuna River using low and medium-resolution satellite data between the years of 1972 and 2013 are dynamic to assess river spatio-temporal changes and their impacts on the local settlements over the specific time. This study employs remote sensing and geographic information system (GIS) proficiencies to identify the river bank shifting, erosion, and depositional features along with the population displacement. The satellite image of LANDSAT multispectral (MSS), thematic mapper (TM), and operational land imager (OLI) and thermal infrared sensor (TIRS) were used in this study. Supervised and unsupervised classification techniques have been used to identify land cover types and detect the changes using ArcGIS Pro 2.3.2. Supervised classifications performed well by validating ≥90% overall accuracy. The result shows total erosion was 3356 ha from 1972 to 2013, while the deposition was 5342 ha. Erosion and deposition cause the bank line shifting, river widening, and settlement displacement. Hundreds of displaced people suffered from loss of land, livelihood, increased poverty, food insecurity, lack of sanitation, and drinking water facilities. Thus, this study will be earnest in assisting the environmental management and associated planning including necessary measures.

Analysis and Estimation of Net Primary Productivity of Vegetation in Nanjing Using Multi-Sourced Remote Sensing Data

Net primary productivity (NPP) is an important indicator of Earth’s ecosystem. However, NPP estimation of cities by high spatial resolution is limited in China, especially Nanjing, and NPP evolution and quantitative analysis of influencing factors in time series are insufficient. Nanjing, as one of the central cities in China, plays a major role in China’s economic development. In this paper, high spatial resolution data combined with Landsat Thematic Mapper (TM) data are used for NPP estimation and analysis in Nanjing City. The research contents of this paper are as follows: (1) To improve the passive effect of “red border region” on GF-1 WFV forest and farmland classification ability, the multispectral and high-resolution images are fused, and the image fusion effect was also evaluated. (2) The NPP of four seasons in Nanjing in 2017 was estimated using the CASA model. (3) The results of NPP estimation are analyzed based on statistical principles. The advantage of this paper lies in the comprehensive utilization of multi-source data and the quantitative analysis of the response mechanism between meteorological data and the NPP estimation value.

Effects of urban land use transitions on ecosystem services: Implication on Chinese urban planning

Urban land use transitions along with the rapid urbanization development cause obvious negative effects on the local ecological system. This paper explored the dynamic patterns of land use in central China’s largest city Wuhan, using high-resolution Landsat Thematic Mapper (TM) data in 1990, 2000 and 2010. The aggregate ecosystem services value, its dynamics, and spatial features were discussed based on the equivalent value per unit area approach, with the revision of Xie et al.’s (2015) unit parameters, adapted to Wuhan’s local situation. The results indicated that Wuhan’s ecosystem services value had been experiencing a continuous decrease during 1990 and 2000 and the following decade. The decrease was mainly due to urban expansion. However, Wuhan’s ecosystem services value decreased less between 2000 and 2010 with a much faster urban expansion pace than those in the former decade. The results illustrated that urban development severely affected ponds, rivers/lakes in their contribution to the ecosystem services that they naturally provided. Ponds and rivers/lakes contributed importantly to urban ecosystem services, even with a small proportion of area. Farmland contributed highly to urban expansion space as well as highly to the loss of related ecosystem services such as food provision and nutrient cycle maintenance. Forest land followed with an important contribution to climate regulation, soil conservation and waste regulation. The optimization of land use structure and configuration could improve urban ecosystem services. The authors conclude that even though the awareness of ecological resources conservation and sustainable development planning is emphasized, the practices are still in a challenge. The integration of ecosystem services dynamics to urban planning is an essential way that the large cities of China can realize sustainable development during its rapid urbanization.

Study of morphometry and land coverage to help urban planning in Barbosa and Barbosinha Brooks watershed, Lins - SP

Urbanization in hydrographic basins promotes changes in the hydrological cycle through the impermealized areas. Not only surface runoff is increased, reducing infiltration, but also the susceptibility to extreme hydrological events. The objective of this study is to analyze the natural susceptibility of Córregos Barbosa e Barbosinha (Barbosa and Barbosinha Brooks) watershed to flooding. Morphometric parameters of land use and occupation were analyzed, in order to subsidize management and planning in an area of urban expansion. The analysis of land use and occupation was based on the supervised classification method of Landsat-5 Thematic Mapper (TM) sensor and Landsat-8 Operational Land Imager sensor (OLI) satellite images dated 1990, 2006 and 2020. Morphometric indices were calculated using the SPRING 5.4.3 software and a SRTM image with a spatial resolution of 30 meters. The results indicate that the natural susceptibility to flooding of the study area is medium to high, and can be intensified by the dynamics of land use and occupation and increasing impervious areas in the basin. Over the period of study, the growth of impervious areas was 133% relative to 1990.

Assessment of spatio-temporal direction of impervious surface area surface temperature in Pretoria, South Africa

Over the years, rapid urban growth has led to the conversion of natural lands into large man-made landscapes due to enhanced political and economic growth. This study assessed the spatio-temporal change characteristics of impervious surface area (ISA) expansion using its surface temperature (LST) at selected administrative subplace units (i.e., local region scale). ISA was estimated for 1995, 2005 and 2015 from Landsat-5 Thematic Mapper (TM) and Landsat-8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) images using a Random Forest (RF) algorithm. The spatio-temporal trends of ISA were assessed using an optimal analytical scale to aggregate ISA LST coupled with weighted standard deviational ellipse (SDE) method. The ISA was quantified with high predictive accuracy (i.e., AUROC = 0.8572 for 1995, AUROC = 0.8709 for 2005, AUROC = 0.8949 for 2015) using RF classifier. More than 70% of the selected administrative subplaces in Pretoria experienced an increase in growth rate (415.59%) between 1995 and 2015. LST computations from the Landsat TIRS bands yielded good results (RMSE = ∼1.44OC, 1.40OC, ∼0.86OC) for 1995, 2005 and 2015 respectively. Based on the hexagon polygon grid (90x90), the aggregated ISA surface temperature weighted SDE analysis results indicated ISA expansion in different directions at the selected administrative subplace units. Our findings can represent useful information for policymakers in evaluating urban development trends in Pretoria, City of Tshwane (COT).

Landsat observations of chlorophyll-a variations in Lake Taihu from 1984 to 2019

Long-term datasets of chlorophyll-a (Chla) are necessary to evaluate changes in eutrophication and to assist in lake management; however, current aquatic remote sensing datasets usually start after 2000. Here, a 36-year Chla dataset was assembled from Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Landsat Imager (OLI) imagery for Lake Taihu (China) over the period from 1984 to 2019. TM, ETM+ and OLI reflectances were compared to those using the MODerate resolution Imaging Spectroradiometer (MODIS) on Terra, and agreement was found within a mean absolute difference of 15%. An algorithm for Chla retrieval developed by a machine learning approach (XGBoost) had good performance (mean absolute percentage error = 35%, mean absolute error = 9%) and outperformed random forest and support vector machine regressors and existing empirical algorithms for Lake Taihu. Landsat-derived mean Chla ranged between 12.8 µg L-1 and 32.3 µg L-1 and indicates that Lake Taihu has been eutrophic from 1984 to 2019. Chla in the northern region was higher than that in other areas over the 36 years. With the limited number of Landsat images each year, we found that the annual variation in Chla had high values during the periods of 1984–1992 and 1994–1997 and significant increases in 1999–2009 and from 2012 to 2019. The spatial and temporal variations in Chla for Lake Taihu were correlated with dissolved nutrients and air temperature. This research illustrates the use of machine learning approaches to generate long-term datasets of water quality from multiple Landsat instruments, for extending watercolor archives for lakes.

Multi-Source Hydrological Data Products to Monitor High Asian River Basins and Regional Water Security

This project explored the integrated use of satellite, ground observations and hydrological distributed models to support water resources assessment and monitoring in High Mountain Asia (HMA). Hydrological data products were generated taking advantage of the synergies of European and Chinese data assets and space-borne observation systems. Energy-budget-based glacier mass balance and hydrological models driven by satellite observations were developed. These models can be applied to describe glacier-melt contribution to river flow. Satellite hydrological data products were used for forcing, calibration, validation and data assimilation in distributed river basin models. A pilot study was carried out on the Red River basin. Multiple hydrological data products were generated using the data collected by Chinese satellites. A new Evapo-Transpiration (ET) dataset from 2000 to 2018 was generated, including plant transpiration, soil evaporation, rainfall interception loss, snow/ice sublimation and open water evaporation. Higher resolution data were used to characterize glaciers and their response to environmental forcing. These studies focused on the Parlung Zangbo Basin, where glacier facies were mapped with GaoFeng (GF), Sentinal-2/Multi-Spectral Imager (S2/MSI) and Landsat8/Operational Land Imager (L8/OLI) data. The geodetic mass balance was estimated between 2000 and 2017 with Zi-Yuan (ZY)-3 Stereo Images and the SRTM DEM. Surface velocity was studied with Landsat5/Thematic Mapper (L5/TM), L8/OLI and S2/MSI data over the period 2013–2019. An updated method was developed to improve the retrieval of glacier albedo by correcting glacier reflectance for anisotropy, and a new dataset on glacier albedo was generated for the period 2001–2020. A detailed glacier energy and mass balance model was developed with the support of field experiments at the Parlung No. 4 Glacier and the 24 K Glacier, both in the Tibetan Plateau. Besides meteorological measurements, the field experiments included glaciological and hydrological measurements. The energy balance model was formulated in terms of enthalpy for easier treatment of water phase transitions. The model was applied to assess the spatial variability in glacier melt. In the Parlung No. 4 Glacier, the accumulated glacier melt was between 1.5 and 2.5 m w.e. in the accumulation zone and between 4.5 and 6.0 m w.e. in the ablation zone, reaching 6.5 m w.e. at the terminus. The seasonality in the glacier mass balance was observed by combining intensive field campaigns with continuous automatic observations. The linkage of the glacier and snowpack mass balance with water resources in a river basin was analyzed in the Chiese (Italy) and Heihe (China) basins by developing and applying integrated hydrological models using satellite retrievals in multiple ways. The model FEST-WEB was calibrated using retrievals of Land Surface Temperature (LST) to map soil hydrological properties. A watershed model was developed by coupling ecohydrological and socioeconomic systems. Integrated modeling is supported by an updated and parallelized data assimilation system. The latter exploits retrievals of brightness temperature (Advanced Microwave Scanning Radiometer, AMSR), LST (Moderate Resolution Imaging Spectroradiometer, MODIS), precipitation (Tropical Rainfall Measuring Mission (TRMM) and FengYun (FY)-2D) and in-situ measurements. In the case study on the Red River Basin, a new algorithm has been applied to disaggregate the SMOS (Soil Moisture and Ocean Salinity) soil moisture retrievals by making use of the correlation between evaporative fraction and soil moisture.

Fires in Amazonian Blackwater Floodplain Forests: Causes, Human Dimension, and Implications for Conservation

The Amazon basin is being increasingly affected by anthropogenic fires, however, most studies focus on the impact of fires on terrestrial upland forests and do not consider the vast, annually inundated floodplains along the large rivers. Among these, the nutrient-poor, blackwater floodplain forests (igapós) have been shown to be particularly susceptible to fires. In this study we analyzed a 35-year time series (1982/1983–2016/2017) of Landsat Thematic Mapper from the Jaú National Park (Central Amazonia) and its surroundings. Our overall objective was to identify and delineate fire scars in theigapófloodplains and relate the resulting time series of annual burned area to the presence of human populations and interannual variability of regional hydroclimatic factors. We estimated hydroclimatic parameters for the study region using ground-based instrumental data (maximum monthly temperature–Tmax, precipitation–P, maximum cumulative water deficit–MCWD, baseflow index–BFI, minimum water level–WLmin90of the major rivers) and large-scale climate anomalies (Oceanic Niño Index–ONI), considering the potential dry season of the non-flooded period of theigapófloodplains from September to February. Using a wetland mask, we identified 518,135 ha ofigapófloodplains in the study region, out of which 17,524 ha (3.4%) burned within the study period, distributed across 254 fire scars. About 79% of the fires occurred close to human settlements (<10 km distance), suggesting that human activities are the main source of ignition. Over 92.4% of the burned area is associated with El Niño events. Non-linear regression models indicate highly significant relationships (p< 0.001) with hydroclimatic parameters, positive withTmax(R2adj. = 0.83) and theONI(R2adj. = 0.74) and negative withP(R2adj. = 0.88),MCWD(R2adj. = 0.90),WLmin90(R2adj. = 0.61) andBFI(R2adj. = 0.80). Hydroclimatic conditions were of outstanding magnitude in particular during the El Niño event in 2015/2016, which was responsible for 42.8% of the total burned floodplain area. We discuss these results under a historical background of El Niño occurrences and a political, demographic, and socioeconomic panorama of the study region considering the past 400 years, suggesting that disturbance ofigapósby fires is not a recent phenomenon. Concluding remarks focus on current demands to increase the conservation to prevent and mitigate the impacts of fire in this vulnerable ecosystem.

Assessing the Effect of Landuse/Land Cover (1986-2020) on Soil Loss in Alluvium Geologic Formation Using Geospatial Technology in Southern Nigeria

The occurrence of soil loss is a continuous process and occurs spatially across the earth’s surface. The study of soil loss is a necessity for proper understanding of the processes and the rate of soil loss for conservational purpose. Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) image data was acquired for 1986, 2003 and 2020 were used to derive the C factor of the RUSLE model while other factors of the model were kept fixed for the years considering their inability to change easily. The RUSLE model was used to determine the trend of the soil loss on the alluvium geologic formation considering their land use/land cover changes for 1986, 2003 and 2020. The rainfall erosivity of the study area had an average of 8201.45MJmmha-1h-1yr-1. The soil erodibility index of the soils obtained from Alluvium had an average of 0.150tons MJ-1 hmm-1. The slope length and steepness factor of the study area range from 0 to 2.574. the crop cover factor of for 1986 range from 0.52 to 0.87, 2003 range from 0.52 to 0.87 and 2020 range from 0.62 to 0.92. No active field conservation was found out within the study area as described by Wischmeier and Smith. The results obtained show that 1986, 2003 and 2020 had a soil loss of 1966.3, 2167.85 and 3361.14 tonha-1yr-1 respectively. The results show that the study area is experiencing an increased trend of soil loss. This result can serve as guide into understanding the past and current rate of soil loss for soil resource planning and management

INFLUENCES OF URBAN GROWTH ON URBAN HEAT ISLAND IN KUCHING CITY IN YEAR OF 2005 UNTIL 2017

Urbanization has contributed significantly to global warming. Kuching city has grown rapidly in recent years as a result of favourable socioeconomic, political, and physical factors that will undoubtedly affect the urban climate and environment. Landsat Thematic Mapper (ETM+) imagery from 2005, 2010, 2016, and 2017 was used to determine the urban heat island effect by examining the relationship between land-use changes and land surface temperature (LST). The study discovered a significant increase in the built-up area of about 17.1 percent between 2005 and 2017 and a decline in barren land and vegetation areas of about 41.6 percent and 5.1 percent, respectively. Additionally, this study discovered that as land use changed from 2005 to 2017, the LST increased year after year. The findings of this study demonstrate that they are effective tools for urban planners and environmental scientists because they provide critical data for monitoring urban growth patterns and their impact on urban climate.

Environmental degradation through mining for energy resources: The case of the shrinking Laguna Santa Rosa wetland in the Atacama Region of Chile

As the world strives to replace fossil fuels with clean energy – reflected by global surge in the number of electric vehicles – the environmental impact of mining all the lithium (Li) needed to enable such a transformation has become a problem in its own right. In South America, the biggest problem is the excessive use of water in Li extraction process in one of the driest areas in the world. Apart from causing severe friction over water rights between local communities and mining companies, it puts enormous pressure on the region's fragile and poorly understood wetland ecosystems. Wetlands are one of the most threatened ecosystems on the Earth, despite their great importance as habitats for a vast number of species and corresponding to one of the most productive environments. Present work is the outcome of an investigation on Laguna Santa Rosa's (LSR) vulnerability resulting from ongoing metal mining operations in its surroundings that require a large volume of water extracted from local aquifers, in addition to a high evaporation rate in the area. This groundwater-dependent wetland will soon be further vulnerable with the commencement of water-intensive Li mining at Salar de Maricunga, with the extraction project already having an environmental impact study approved. Sustainability of this wetland protected under the Ramsar Convention and home to numerous wildlife species habiting this highland sector of the Andean Cordillera is vital for the associated ecosystems. The variation in the water surface area of LSR has been deciphered through the application of a Landsat-5 Thematic Mapper (TM) satellite image collection. This has been done both for the hydrological year 2016–2017 and 1993-2014 period. A decreasing trend in the water surface area of LSR, a hypersaline lake at the southern extreme of Salar de Maricunga, a salt flat in the Atacama Region of Chile, was observed through the study of satellite images between 1993 and 2014, showing shrinking of this wetland. However, this shrinking trend was punctuated by extreme rainfall and consequent flood with accumulated precipitation exceeding 40 mm in a single day and hourly mean rainfall rates higher than 10 mm/h recorded at several locations in the hyperarid Atacama Desert on March 25, 2015. This raised the lake's water level, and thus the areal extent of the lake water observed in the satellite images of the 2016–2017 hydrological year following the extreme weather events of 2015. The interplay of such extreme weather events and groundwater extraction for mining activities in the area complicates the assessment of the latter's effect on LSR, as the evaluation is constrained to the intervals between successive extreme weather events. In addition, the water level increases in summer because of the thawing of the snow. Close monitoring of the wetlands like LSR is essential to ensure their sustainability by considering both anthropic and climatic factors.

Spatio-Temporal Analysis of Ecological Vulnerability and Driving Factor Analysis in the Dongjiang River Basin, China, in the Recent 20 Years

The global ecological environment faces many challenges. Landsat thematic mapper time-series, digital elevation models, meteorology, soil types, net primary production data, socio-economic data, and auxiliary data were collected in order to construct a comprehensive evaluation system for ecological vulnerability (EV) using multi-source remote sensing data. EV was divided into five vulnerability levels: potential I, slight II, mild III, moderate IV, and severe V. Then, we analyzed and explored the spatio-temporal patterns and driving mechanisms of EV in the region over the past 20 years. Our research results showed that, from 2001 to 2019, the DRB was generally characterized as being in the severe vulnerability class, with higher upstream and downstream EV classes and a certain amount of reduction in the midstream EV classes. Moreover, EV in the DRB continues to decrease. The spatio-temporal EV patterns in the DRB were significantly influenced by the relative humidity, average annual temperature, and vegetation cover over the past 20 years. Our work can provide a basis for decision-making and technical support for ecosystem protection, ecological restoration, and ecological management in the DRB.

The NIEER AVHRR snow cover extent product over China – a long-term daily snow record for regional climate research

Abstract. A long-term Advanced Very High Resolution Radiometer (AVHRR) snow cover extent (SCE) product from 1981 until 2019 over China has been generated by the snow research team in the Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences. The NIEER AVHRR SCE product has a spatial resolution of 5 km and a daily temporal resolution, and it is a completely gap-free product, which is produced through a series of processes such as the quality control, cloud detection, snow discrimination, and gap-filling (GF). A comprehensive validation with reference to ground snow-depth measurements during snow seasons in China revealed the overall accuracy is 87.4 %, the producer's accuracy was 81.0 %, the user's accuracy was 81.3 %, and the Cohen's kappa (CK) value was 0.717. Another validation with reference to higher-resolution snow maps derived from Landsat-5 Thematic Mapper (TM) images demonstrates an overall accuracy of 87.3 %, a producer's accuracy of 86.7 %, a user's accuracy of 95.7 %, and a Cohen's kappa value of 0.695. These accuracies were significantly higher than those of currently existing AVHRR products. For example, compared with the well-known JASMES AVHRR product, the overall accuracy increased approximately 15 %, the omission error dropped from 60.8 % to 19.7 %, the commission error dropped from 31.9 % to 21.3 %, and the CK value increased by more than 114 %. The new AVHRR product is already available at https://doi.org/10.11888/Snow.tpdc.271381 (Hao et al., 2021).

Remote Sensing-Based Urban Sprawl Modeling Using Multilayer Perceptron Neural Network Markov Chain in Baghdad, Iraq

The global and regional land use/cover changes (LUCCs) are experiencing widespread changes, particularly in Baghdad City, the oldest city of Iraq, where it lacks ecological restoration and environmental management actions at present. To date, multiple land uses are experiencing urban construction-related land expansion, population increase, and socioeconomic development. Comprehensive evaluation and understanding of the effect of urban sprawl and its rapid LUCC are of great importance to managing land surface resources for sustainable development. The present research applied remote sensing data, such as Landsat-5 Thematic Mapper and Landsat-8 Operation Land Imager, on selected images between July and August from 1985 to 2020 with the use of multiple types of software to explore, classify, and analyze the historical and future LUCCs in Baghdad City. Three historical LUCC maps from 1985, 2000, and 2020 were created and analyzed. The result shows that urban construction land expands quickly, and agricultural land and natural vegetation have had a large loss of coverage during the last 35 years. The change analysis derived from previous land use was used as a change direction for future simulation, where natural and anthropogenic factors were selected as the drivers’ variables in the process of multilayer perceptron neural network Markov chain model. The future land use/cover change (FLUCC) modeling results from 2030 to 2050 show that agriculture is the only land use type with a massive decreasing trend from 1985 to 2050 compared with other categories. The entire change in urban sprawl derived from historical and FLUCC in each period shows that urban construction land increases the fastest between 2020 and 2030. The rapid urbanization along with unplanned urban growth and rising population migration from rural to urban is the main driver of all transformation in land use. These findings facilitate sustainable ecological development in Baghdad City and theoretically support environmental decision making.

Assessment of Land Use/Cover Change Using Remote Sensing and GIS Techniques: A Case of Osogbo and Its Peripheral Areas in Nigeria

The importance of accurate and timely information describing the nature and extent of land resources and changes over time is increasing, especially in rapidly growing city areas. Landsat satellite imageries of three different time periods, i.e., Landsat Thematic Mapper (TM) of 1982, 2000 and 2018 were acquired by Global Land Cover Facility Site (GLCF) and earth explorer site, quantify the changes in the Osogbo and its peripheral areas from 1982 to 2018 over a period of 36 years. These data sets were imported in ArcGIS 10.3, ERDAS Imagine and IDRIS Selva, satellite image processing softwares to create a false colour composite (FCC), supervised classification methodology was employed using maximum likelihood technique. The images of the study area were categorized into four different classes namely Core-urban, Peri-urban, Vegetation, water body. The results indicate that during the last thirty-six (36) years, Core-Urban land and water body have been increased by 2.74% (38.20 km2) and 0.98% (13.69 km2) while Peri-Urban land, and vegetation cover have decreased by 0.35% (5.00 km2), and 3.36 % (46.87 km2), respectively. The results quantify the land cover change patterns in the city and its peripheral area and demonstrate the potential of multitemporal Landsat data to provide an accurate, economical means to map and analyse changes in land cover over time that can be used as inputs to land management and policy decisions.

LAND SURFACE TEMPERATURE (LST) ESTIMATION AT KUSHTIA DISTRICT, BANGLADESH

Land Surface Temperature (LST) is a key phenomenon in worldwide climate change. The knowledge of surface temperature is important to a range of issues and themes in earth sciences, central to urban climatology, global environmental change, and human-environment interactions. In this study, LST for Kushtia District, Khulna division, Bangladesh, is derived using Arc-GIS software version from the images of Landsat 8 Optical Land Imager (OLI) of 30 m resolution and Thermal Infrared Sensor (TIR) data of 100 m resolution, Landsat-7 Enhanced Thematic Mapper plus (ETM+) with opto-mechanical sensor and Spatial Resolution of 30 m (60 m – thermal, 15-m panchromatic) and Landsat-5 Thematic MAPPER (TM) satellites. A total time span of 20 years, starting from 1998 to 2018 is selected. At every 5 years interval starting from 1998, air temperature, LST, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) have been calculated. Using the equation from Landsat user’s handbook, the digital number of thermal infrared band is converted into spectral radiance. Plank’s Inverse Function is used to obtain the effective at-sensor brightness temperature from the spectral radiance. The surface emissivity based on NDVI classes is used to retrieve the final LST. The study reveals that LST is increasing with the passage of time. Maximum values of LST are found along the North-East and North-West regions of Kushtia district. NDVI is found to have positive correlation with LST. Also, it has been found that NDWI has little influence on LST. The reasons behind the rise and fall of LST in different years are explained from changes in total vegetation coverage and total abundance of water body coverage viewpoint. The spatial distribution figures of air temperature, LST, NDVI and NDWI could be used as a guideline for urban planning, strategies for quality improvement of urban environment and a smart solution to the reduction of LST.

Detection of Land Use Land Cover Changes Using Remote Sensing and GIS Techniques in a Secondary City in Bangladesh

This study aims at classifying land use land cover (LULC) patterns and detect changes in a 'secondary city' (Savar Upazila) in Bangladesh for 30 years i.e., from 1990 to 2020. Two distinct sets of Landsat satellite imagery, such as Landsat Thematic Mapper (TM) 1990 and Landsat 7 ETM+ 2020, were collected from the United States Geological Survey (USGS) website. Using ArcMap 10.3, the maximum likelihood algorithm was used to perform a supervised classification methodology. The error matrix and Kappa Kat were done to measure the mapping accuracy. Both images were classified into six separate classes: Cropland, Barren land, Built-up area, Vegetation, Waterbody, and Wetlands. From 1990 to 2020, Cropland, Barren land, Waterbody, and Wetlands have been decreased by 30.63%, 11.26%, 23.54%, and 21.89%, respectively. At the same time, the Built-up area and Vegetation have been increased by 161.16% and 5.77%, respectively. The research revealed that unplanned urbanization had been practiced in the secondary city indicated by the decreases in Cropland, Barren land, Wetland, and Waterbody, which also showed direct threats to food security and freshwater scarcity. An increase in Vegetation (mostly homestead vegetation) indicates some environment awareness programs that encourage people to maintain homestead and artificial gardens. The study argues for the sustainable planning of a secondary city for a developing country's future development.

Assessing Drought Vegetation Dynamics in Semiarid Grass- and Shrubland Using MESMA

Drought intensity and duration are expected to increase over the coming century in the semiarid western United States due to anthropogenic climate change. Historic data indicate that megadroughts in this region have resulted in widespread ecosystem transitions. Landscape-scale monitoring with remote sensing can help land managers to track these changes. However, special considerations are required: traditional vegetation indices such as NDVI often underestimate vegetation cover in semiarid systems due to short and multimodal green pulses, extremely variable rainfall, and high soil fractions. Multi-endmember spectral mixture analysis (MESMA) may be more suitable, as it accounts for both green and non-photosynthetic soil fractions. To determine the suitability of MESMA for assessing drought vegetation dynamics in the western US, we test multiple endmember selection and model parameters for optimizing the classification of fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil (S) in semiarid grass- and shrubland in central New Mexico. Field spectra of dominant vegetation species were collected at the Sevilleta National Wildlife Refuge over six field sessions from May–September 2019. Landsat Thematic Mapper imagery from 2009 (two years pre-drought), and Landsat Operational Land Imager imagery from 2014 (final year of drought), and 2019 (five years post-drought) was unmixed. The best fit model had high levels of agreement with reference plots for all three classes, with R2 values of 0.85 (NPV), 0.67 (GV), and 0.74 (S) respectively. Reductions in NPV and increases in GV and S were observed on the landscape after the drought event, that persisted five years after a return to normal rainfall. Results indicate that MESMA can be successfully applied for monitoring changes in relative vegetation fractions in semiarid grass and shrubland systems in New Mexico.

Evaluating the Applicability of Thermal Infrared Remote Sensing in Estimating Water Potential of the Karst Aquifer: A Case Study in North Adriatic, Croatia

One of the most prominent tourist destinations in the Adriatic coast, the city of Opatija, is facing a problem concerning seasonal drinking water shortages. The existing water resources are no longer sufficient, and attention is being given to alternative resources such as the underlying karstic aquifer and several coastal springs in the city itself. However, the water potential of the area still cannot be estimated due to the insufficient hydrological data. The goal of this research was to evaluate the use of thermal infrared (TIR) remote sensing as the source of valuable information that will improve our understanding of the groundwater discharge dynamics. Ten Landsat ETM+ (enhanced thematic mapper plus) and two Landsat TM (thematic mapper) images of the north Adriatic, recorded during 1999–2004 at the same time as the field discharge measurements, were used to derive sea surface temperature (SST) and to analyze freshwater outflows seen as the thermal anomaly in the TIR images. The approach is based on finding the functional relationship between the size of the freshwater thermal signatures and the measured discharge data, and to estimate the water potential of the underlying aquifer. It also involved analyzing the possible connection between the adjusted size of the spring’s thermal signatures and groundwater level fluctuations in the deeper karst hinterland. The proposed methodology resulted in realistic discharge estimates, as well as a good fit between thermal anomalies with measured discharges and the groundwater level. It should be emphasized that the results are site specific and based on a limited data set. However, they confirm that the proposed method can provide additional information on groundwater outflow dynamics and coastal springs’ freshwater quantification.