Thematic Mapper Images Research Articles

On-land manifestation of along-strike transitioning of the Red Sea from continental rifting to sea floor spreading

This study uses Landsat Thematic Mapper (TM), Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and aeromagnetic data to examine possible reactivation of the Ediacaran, NE-trending Ad Damm Fault Zone (AFZ) and the Fatima Fault Zone (FFZ) in the western Arabian Shield. Reactivation is by latest Paleogene to Neogene and Quaternary structures of the Red Sea. The AFZ is the tectonic boundary between the Tonian Jeddah terrane in the northwest and the Asir terrane to the southeast of the Arabian Shield. The AFZ and FFZ coincide with possible onshore continuation of a transform fault that separates the sea floor spreading segment of the Red Sea in the south from its transitional segment to the north. It has been suggested that the AFZ is a candidate for the formation of a future onshore transform fault that would result in an eastward jump of the Red Sea sea floor spreading center onto the Arabian Shield. The future spreading center will follow the latest Neogene to Quaternary volcanic rocks (Harrat) that are exposed on the eastern side of the Red Sea. The Landsat TM image and the SRTM DEM show that the AFZ dissects the Red Sea topographic escarpment, and both the AFZ and FFZ control paleo-drainage system (wadis). The aeromagnetic data show that there is a ∼100 km wide zone between the AFZ and FFZ dominated by NE-trending short wavelength magnetic anomalies, possibly related to regional tectonic fabric. Additionally, the aeromagnetic data show that both fault zones dissect NW-trending short wavelength magnetic anomalies, interpreted as latest Paleogene to earliest Neogene dike swarms associated with the opening of the Red Sea. This dissection also includes possible Red Sea related narrow marginal grabens and horsts that were formed onshore and offshore parallel to the Red Sea shoreline. Red Sea-related structure might have reactivated the AFZ and FFZ.

Analysis of Trends in Land Utilization and Land Cover Dynamics in the Kwahu West Municipality, Ghana

The connection between people and the environment has been blamed for changes. Planning for sustainable development requires awareness of land use and land cover (LULC), its severity, causes, and implications. The purpose of this study was to generate maps that illustrate the land use and land cover within the Kwahu West Municipality and detect changes to identify and analyse overall patterns in LULC changes in the area. This research employed remote sensing techniques to assess land use and land cover alterations (LULC). This study used Landsat 7 Enhanced Thematic Mapper (ETM+) images from 2001, Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) images from 2015, and Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) images from 2023 to detect and quantify LULC changes that occurred over a 22-year study period. Arc GIS 10.8 and ERDAS Envision 13 were used to pre-process the data. According to the study, built-up land and bare land rose in their respective areas and years by 10.675 km2 (or 2%), 18.421 km2 (or 5%), and 29.096 km (or 7%), respectively, between 2001 and 2015 and 2015 and 2023. The overall change from 2001 to 2023 was correspondingly 29.096 km. The results depicted a drastic change in the study area's forest vegetation, which can be attributed to a sharp rise in population, urbanisation, and other infrastructure projects. These changes have been determined to cause biodiversity loss and water contamination. It is recommended that the Municipality's Lands Commission initiate a public education campaign about creating local planning schemes for lands, especially in undeveloped and underdeveloped areas.

Impact of Changes in Land use and land cover on Agricultural Productivity in Northern Nigeria

The challenges posed by changes in land use and land cover are greatly affecting agricultural productivity in the semi-arid region of Nigeria. Land use and land cover change affects ecosystem functions and services, which leads to people migrating from one place to another, predominantly rural dwellers. The research studies the impact of land use and land cover change on agricultural productivity and the factors influencing the changes. To achieve this, a remote sensing technique was employed. The Landsat Thematic Mapper Image of 1990, Landsat Enhanced Thematic Mapper Image of 2005 and Landsat Enhanced Thematic Mapper Image of 2020 were collected and analysed. Six classes of land use and land cover were obtained using Earth Resource Development Assessment System Imagery 9.1 software. The result shows that there was a decrease in the land area occupied by wetland, shrubland and water body, while there was an increase in the land area occupied by bare lands/dunes, settlement and scattered cultivation from the year 1990 to 2020. Increase in bare land/dune, scattered cultivation and settlement were mainly as a result of climate change resulting from overgrazing, a southward movement of dunes from the Sahara desert, increasing farming activities and increasing demand for shelter by the increasing population. The decrease in shrubland, water body, and wetland were also linked to climate change resulting from increasing deforestation, and increasing use of wetland for farming activities. Based on the results of the analysis, it is recommended that measures should be taken to integrate sustainable land management and climate change adaptation options for sustainable Agriculture.

Predicting land use/land cover changes in the Santchou Wildlife Reserve (Santchou, West-Cameroon) using a CA-Markov model

Over the last three decades, the Santchou Wildlife Reserve (SWR) located in the West region of Cameroon has experienced rapid degradation. Land reclamation and population growth have significantly contributed to meeting the increasing demands for foods and urban development. In this study, Landsat 4–5 Thematic Mapper (TM) images of 1988 and 2005, and Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI-TIRS) image of 2022 are firstly used for calculating and analysing area changes and then spatial distribution of land use/land cover changes (LULCC) in the SWR for a time span 1988–2005–2022. Secondly, the spatial distribution of LULCC from 2022 to 2027 and from 2027 to 2032 was simulated using a Cellular Automata (CA)-Markov model that allows to predict future changes and identify the possible patterns of evolution. Results exhibit six major LULC classes including Mountain Forest, Degraded Lowland Forest, Lowland Forest, Degraded Mountain Forest, Built-up Area and Cultivated Land. The overall accuracies of LULC classification reached 89.71‒92.24 %, with Kappa coefficients of 0.87‒0.92. However, the prediction model was validated with an overall good agreement of 90.51 %. In general, two significant patterns of change were evidenced and characterized by the rapid expansion of Cultivated Land from 158.6 to 736.4 ha between 1988 and 2022, and could reach 809.0 ha in 2032, while Built-up Area displays a surface change from 12.4 to 29.3 ha, which is expected to attend 67.3 ha in 2032. Change patterns from the past to the future are also marked by a decline of Mountain Forest (2107.4 to 1594.4 ha), Degraded Lowland Forest (1450.1 to 1024.5 ha) and Degraded Mountain Forest (954.6 to 860.6 ha) at the expense of Lowland Forest (2332.5 to 2659.8 ha). Human activities influenced by socio-economic issues remain the main factor of changes in the SWR. Despite the limitations of the CA-Markov model due to the non-consideration of socio-economic factors, this study appears as a new opportunity to develop effective strategies for managing LULCC and protecting ecological biodiversity in the SWR.

Remotely sensed environmental measurements detect decoupled processes driving population dynamics at contrasting scales.

The increasing availability of satellite imagery has supported a rapid expansion in forward-looking studies seeking to track and predict how climate change will influence wild population dynamics. However, these data can also be used in retrospect to provide additional context for historical data in the absence of contemporaneous environmental measurements. We used 167 Landsat-5 Thematic Mapper (TM) images spanning 13 years to identify environmental drivers of fitness and population size in a well-characterized population of banner-tailed kangaroo rats (Dipodomys spectabilis) in the southwestern United States. We found evidence of two decoupled processes that may be driving population dynamics in opposing directions over distinct time frames. Specifically, increasing mean surface temperature corresponded to increased individual fitness, where fitness is defined as the number of offspring produced by a single individual. This result contrasts with our findings for population size, where increasing surface temperature led to decreased numbers of active mounds. These relationships between surface temperature and (i) individual fitness and (ii) population size would not have been identified in the absence of remotely sensed data, indicating that such information can be used to test existing hypotheses and generate new ecological predictions regarding fitness at multiple spatial scales and degrees of sampling effort. To our knowledge, this study is the first to directly link remotely sensed environmental data to individual fitness in a nearly exhaustively sampled population, opening a new avenue for incorporating remote sensing data into eco-evolutionary studies.

Multi-temporal assessment of land surface temperatures as an indicator of land use/cover changes and climate variability in the Develi Basin, Turkey.

Land surface temperature (LST) is an important parameter that reflects land surface processes of water and energy balance and has been used in assessment of land use/cover changes. However, the use of LST in monitoring changes in non-urban areas such as agricultural areas and wetlands is still limited. In this study, we aim to determine the spatial and temporal changes in LST in a semi-arid agricultural basin in Turkey (Develi Basin), where land use/cover and climatic conditions showed considerable variability since 1980s. Irrigated agriculture have expanded in the basin since 1987, after the construction of a large irrigation project. The basin hosts an internationally important wetland, called the Sultan Marshes, affected negatively by irrigation expansion. The study covers a 39-year period from 1984 to 2022. Four Landsat Thematic Mapper (TM) images acquired in 1984, 1987, 2003, and 2007 and two Landsat 8 OLI/TIRS images acquired in 2014 and 2022 were used in the analyses. The land use/cover changes were evaluated based on Normalized Difference Vegetation Index (NDVI). LST was estimated through top-of-atmosphere brightness temperature from thermal bands of Landsat images. Climate variability from 2014 to 2022 was analyzed with statistical methods. The results indicated that Develi Basin faced both spatial and temporal land use/cover changes. The area covered with natural steppe vegetation and water bodies decreased in the basin. In contrast, the sparsely and densely vegetated soil covers, which mostly denote agricultural areas, increased. Changes in LST values were observed from 1984 to 2022 as a result of climatic factors and land use/cover changes. LST changes were variable across different land use/cover types; LST decreased in irrigated areas and increased in lakes that went dry over years. LST changes proved useful for evaluating land use/cover changes and climatic variations in agricultural basins.

Land Suitability and Characterization Study of Soils, Natural Vegetation Cover in Wahat Elnukhila area, Northern Darfur State, Sudan

This study was conducted in the Wahat Elnukhila area, about 600 km northeast of El-fashier town, northern Darfur State, to produce broad–base data on the geographical distribution of soils, characterization of soils using some soil indicators (ECe, pH and SAR), explore the natural land cover and evaluate the soil suitability for agricultural activities in the study area. The study area covers (20000 feddans). This research was based on the data and information extracted from the soil survey, remote sensed landsat, Enhanced Thematic Mapper (ETM+) images dated 2018, in addition to field observation aided by GPS receivers and geographic information system were used to generate soil map in the study area. By using the American soil classification system (FAO, 1990 and 2006) study area may be classified into three units, the total area of the three units and natural vegetation cover decreased in the following order: unit1 (12734 feddans) > unit 2 (3994 feddans) > unit 3 (3272 feddans), green covers as a result of the availability of quantities of ground and surface water, which distinguishes it from the rest of the surrounding desert lands. The predominant vegetation cover is Imperata cylindrica (halfa), Tamarix (Tarfa), phragmites (communist)“ Alboes”, Citrullus Colocynthis (Hanzal), Ischaemum muticum and p. dactylifera (date palm). Regarding soil texture, drainage, soil depth, and fertility status for units 1, unit 2, and unit 3, can be described as follows: sandy to loamy sand, well-drained, shallow and low fertile, coarse texture to moderate, moderately drained, shallow to moderate and low fertile, gravelly surface, moderatly drained, shallow to moderate and very low fertile respectively. Soil analysis reveled that there was high inherent risk of soil salinity and sodicity, especially unit 1. In general, the result showed that there were clear aspects of wind erosion represented as large areas affected by the sand encroachment form of crescent dunes and rocks interspersed with hills and plateaus. Furthermore, soils of the targeted area were formed from the accumulation of creeping and portable aeolian sands. The research site lies in the southeastern part of the border triangle between Sudan, Egypt, Libya and Chad, which is severely affected by wind erosion because the site was bare, open with no wind barriers or shelter belts, also this area falls under the sand moving winds of the Sahara. These are the northerly winds that prevail in Northern Darfur State. Furthermore, the prevalent climatic conditions including high temperature, rainless area and relatively high wind speed are conducive to high wind erosion. The area in danger unless serious measures carried out to reduce wind erosion. The area has very low potential suitability for agriculture. The study area was rich in wild life such as deer, rabbits, birds, many insects and snakes.

Effect of urbanization on energy balance and evapotranspiration in an Amazon–Cerrado transition region in Brazil

The Amazon–Cerrado transition region has a high rate of deforestation for agriculture, livestock, logging, and for the increase in urban areas of cities. The development of cities in this region in recent decades has increased the deforestation process and modified the local microclimate and the exchange of energy and water between the surface and the atmosphere. However, the magnitude of the change in energy and water exchanges due to urbanization is unknown for most Brazilian cities, especially in the Amazon–Cerrado region. Thus, the objective was to evaluate the spatiotemporal change in energy balance and in evapotranspiration (ET) patterns over three decades of urbanization in Sinop. The surface energy balance (SEB) and ET were estimated by the SEB algorithms for land with Landsat 5 Thematic Mapper images from the Sinop region, Mato Grosso, Brazil, in 1985, 2000, and 2010. The estimated SEB and ET were validated with measurements in a flux tower in the Amazon–Cerrado transition forest 50 km from the urban area of Sinop. The components of the SEB, except the soil/storage heat flux (G), had a strong correlation, and the ET had a moderate correlation with measurements in the flux tower. The surface albedo (asup), land surface temperature (LST), sensible heat flux (H), and G of the study area increased over the years, while the normalized difference vegetation index, latent heat flux (LE), and ET decreased over the years. Buildup and grassland areas had lower values of NDVI, Rn, LE, and ET and higher values of asup, LST, H, and G than forest areas. Therefore, the urbanization of Sinop has decreased available energy for ET and increased the energy to heat the air and soil.

Mapping and Monitoring Forest Plantations in São Paulo State, Southeast Brazil, Using Fraction Images Derived from Multiannual Landsat Sensor Images

This article presents a method, based on orbital remote sensing, to map the extent of forest plantations in São Paulo State (Southeast Brazil). The proposed method uses the random forest machine learning algorithm available on the Google Earth Engine (GEE) cloud computing platform. We used 30 m annual mosaics derived from Landsat-5 Thematic Mapper (TM) images and from Landsat-8 Operational Land Imager (OLI) images for the 1985 to 1995 and 2013 to 2021 time periods, respectively. These time periods were selected based on the planted areas’ rotation, especially the eucalypt’s short rotation. To classify the forest plantations, green, red, NIR, and MIR spectral bands, NDVI, GNDVI, NDWI, and NBR spectral indices, and vegetation, shade, and soil fractions were used for both sensors. These indices and the fraction images have the advantage of reducing the volume of data to be analyzed and highlighting the forest plantations’ characteristics. In addition, we also generated one mosaic for each fraction image for the TM and OLI datasets by computing the maximum value through the period analyzed, facilitating the classification of areas occupied by forest plantations in the study area. The proposed method allowed us to classify the areas occupied by two forest plantation classes: eucalypt and pine. The results of the proposed method compared with the forest plantation areas extracted from the land use and land cover maps, provided by the MapBiomas product, presented the Kappa values of 0.54 and 0.69 for 1995 and 2020, respectively. In addition, two pilot areas were used to evaluate the classification maps and to monitor the phenological stages of eucalypt and pine plantations, showing the rotation cycle of these plantations. The results are very useful for planning and managing planted forests by commercial companies and can contribute to developing an automatic method to map forest plantations on regional and global scales.

Monitoring Land Cover Change in Katsina Urban Area, Nigeria (1999 -2017) Associated with Urbanization: A Pixel-based Individuality Forms of Image A nalysis

Objectives: To study the rapidly changing urban area of Katsina in Katsina State; Nigeria, to track Land Cover Changes (LCC) and to identify possible impacts of urbanization. Methods: Landsat thematic mapper (TM), enhance thematic mapper (ETM +) images 30m resolution from the United States Geological Survey (USGS) acquired from Jos National Centre for Remote Sensing (NCRS). Satellite images from 1999 and 2017 were used to create a past and current picture of the LCC in the urban area of Katsina. The images were classified using the pixel identity forms of the digital organization of the monitored images with a maximum-likelihood classifier algorithm based on the multispectral image to understand the spectral data presented in the image of the urban area of Katsina. These images were then compared with the modifications that occurred among the dates of images. Findings: The findings on study area showed that the built-up area had increased and added 4.99 square kilometres (percentage change of 22.22) between 1987 -1999. The class of vegetation/agricultural land cover also increased and added 10.4 square kilometres (percent change of 165.8) between 1987 -1999. The Bare-soil had decreased and lost 15.39 square kilometres (percent change of 25.29) between 1987 -1999. Conclusion: Land cover changes exist, either legal or illegal, which indicate the rapid rate of population growth and urbanization in the urban area of Katsina. It also revealed the capability of RS/GIS for managing large information that made it suitable for these types of study. Keywords: Analysis; Based on the pixel individuality; Land Cover Changes (LCC); Monitoring and Urbanisation

Statistical analysis for determination of homogeneous reflectance regions in a coastal lagoon using satellite imagery and bathymetry survey

Coastal lagoons are highly productive ecosystems that offer a range of natural services for the environment and society. However, anthropic pressure in their basins cause water quality loss and endangers their ecosystem. Monitoring water quality constituent by remote sensing can provide spatial and high temporal resolution analysis. Not recognizing how depth influences on the signal received by a sensor causes on significant errors in estimating water constituent’s concentrations. Using a bathymetry survey and Landsat-5 thematic mapper (TM) images, different patterns of depth influence on reflectance were recognized. By means of a statistical analysis, it was able to classify and analyze three different regions of homogeneous spectral behavior for the closest image from the bathymetric survey and for a median image from 2001 to 2011. Nonparametric Kruskal–Wallis test and Dunn’s post hoc test provided great values in differentiating all groups generated for the coastal lagoon to both closest and median image. The methodology proposed here, being based on statistical methods, can be applied with the use of other images to assist studies in coastal lagoons across the globe.

Depletion of Forested Area: Geidam Perspective

Land cover maps provide best understanding of current landscape change over time. One can evaluate past land cover maps for several different years for management decisions as well as gain insight into the possible effects on decisions making. One of the key monitoring areas is how the environment keeps degrading resulting from increased anthropogenic activities such as the removal of the forest covers. This study monitors the pattern changes of the Geidam Yobe state Nigeria, using Landsat images of two different periods from Enhanced Thematic Mapper (ETM+) image of data of 1988 and 2018. The images were geometrically and atmospherically pre-processed then classified, using maximum likelihood (MLC) algorithm to produce land cover maps of the Geidam. The accuracy of the classification was assessed with confusion matrices giving results morethan the minimum 85% required. The results revealed that the built-up and tree area increase by (+30.97%), water body reduced by (-5.06%) and forest reduce by (-23.48%) within the study period. This shows a rapid decrease in the forest, which is partly attributed to deforestation activities and partly to climate change impact.

ARTIFICIAL INTELLIGENCE AND ORBITAL IMAGES APPLICATION FOR ANALYSIS OF SPATIAL LAND USE AND COVERAGE PATTERNS

The study aimed to analyze the performance of different machine learning (ML) algorithms in predicting land use and land cover patterns from time series spectral data from Thematic Mapper (TM) and Operational Land Imager (OLI) sensors. The QGIS software was used, where the import of TM / Landsat 5 images began in 2004 and 2009 and OLI/Landsat 8 for 2015 and 2019, to obtain information to characterize and differentiate usage patterns and land cover. Subsequently, training and testing of the algorithms, Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), and Naive Bayes (NB), were carried out in the proportions of 80%-20%, 70%-30%, 60%-40% in the KNIME software. The performance was analyzed based on global accuracy and the Kappa index. The RF and SVM for the years 2004 and 2009 showed the best performance (global accuracy), while for the years 2015 and 2019, they were the K-NN and the RF. The Kappa index values indicated that the classifications of the algorithms varied from 0.80 – 1.00. The proportion of 60% (training) and 40% (test) was the one that provided the best results for all the dates analyzed. The data from the pixels sampled from the land use and land cover patterns of the TM and OLI sensor images proved to be efficient for the ML process in the KNIME software.

Comparison of Impervious Surface Dynamics through Vegetation/High-Albedo/Low-Albedo/Soil Model and Socio-Economic Factors

Hong Kong and Shenzhen have entirely different land-use development policies, resulting in a disparity in the increase rate of impervious surface area. Impervious surface estimation is a significant method for evaluating urbanization, so that countries and cities can deal with their growing populations. The impervious surface area was estimated through Landsat Thematic Mapper (TM) image extraction, the V-H-L-S (vegetation, high-albedo, low-albedo, and soil) model, and linear spectral un-mixing analysis (LSUM). Changes in fractions of endmembers over periods of time were identified and employed to analyze changes in land use and land cover (LULC). The research adopting the V-H-L-S model for classifying land cover and exploring the association of change in impervious surface areas and socio-economic growth over a period of time is limited. In this study, impervious surface estimations for Hong Kong and Shenzhen in 1995, 2005, and 2016 were compared, selecting vegetation, high-albedo, low-albedo, and soil as endmembers. The change rate of the fractions in the four endmembers was calculated to identify changes in land use and land cover during these three specific time periods. The impervious surface was determined to constitute a combination of high-albedo and low-albedo. Moreover, a proportional relationship exists between the increase in impervious surface area, population rate, GDP, and GDP per capita in both Hong Kong and Shenzhen. However, there was a difference in the increase in impervious surface area between Hong Kong and Shenzhen due to the different land-use policies in the country’s two systems.

Evaluating the Impact of the Influx of Syrian Refugees on Land Use/Land Cover Change in Irbid District, Northwestern Jordan

The refugee movement creates urban and environmental pressures at their destination locations. This pressure often presents in the form of Land Use/Land Cover (LULC) change. This study seeks to understand the impact of the Syrian refugees’ influence on changing the urban and agricultural land dynamics in Irbid district in northwestern Jordan from 1985 to 2021, including the period of the civil war in Syria, using Landsat Thematic Mapper (TM) images for the years 1985 and 2004, and the Landsat-8 Operational Land Imager (OLI) for the years 2013 and 2021. The Google Earth Engine (GEE) platform was used to conduct all image processing and perform calculations and classification analysis using the Random Forest (RF) approach. The study of the classified images compared LULC before and during the Syrian crisis using images from 1985, 2004, 2013, and 2021. The results show that the urban area increased. In parallel, agricultural land increased. During the Syrian refugee crisis, agriculture became a significant livelihood activity for Syrian refugees. In summary, the movement of the refugees to Irbid district caused an increased demand for land and housing, which accelerated the building and construction process.

Identification of Hazardous Glacial Lakes in the Yarlung Zangbo River Basin Based on Lakes Changes Determined Using Google Earth Engine

Glaciers have melted rapidly in the Yarlung Zangbo River Basin due to recent climate change, which has accelerated the expansion of glacial lakes. We performed a systematic survey of glacial lakes in the Yarlung Zangbo River Basin using the Google Earth Engine platform. This allowed us to map the distribution and monitor the evolution of glacial lakes over seven time periods from 1990 to 2020, using more than 140 Landsat Thematic Mapper and Operational Land Imager images at a 30-m resolution. The results show that there were 2088 glacial lakes in 2020, with a total area of 245.87 km2, mainly distributed at elevations of 3,800–5,800 m above sea level. From 1990 to 2020, the Yarlung Zangbo River Basin glacial lakes expanded by approximately 1.81 km2·a−1. A total of 153 glacial lakes in the Yarlung Zangbo River Basin were assessed. The assessment shows that 153 glacial lakes were divided into very high, high, medium, low, and very low level. The number of glacial lakes in each level is 23, 22, 32, 32, and 44, respectively. The very high and high level glacial lakes were mainly found in the Yi’ong Zangbo River Basin, Parlung Zangbo River Basin, the western of the Yarlung Zangbo River Basin and Nianchu River Basin. These glacial lakes deserve further attention and monitoring.

Analysis of Land Use and Land Cover Changes in Urban Areas Using Remote Sensing: Case of Blantyre City

Blantyre City has experienced a wide range of changes in land use and land cover (LULC). This study used Remote Sensing (RS) to detect and quantify LULC changes that occurred in the city throughout a twenty-year study period, using Landsat 7 Enhanced Thematic Mapper (ETM+) images from 1999 and 2010 and Landsat 8 Operational Land Imager (OLI) images from 2019. A supervised classification method using an Artificial Neural Network (ANN) was used to classify and map LULC types. The kappa coefficient and the overall accuracy were used to ascertain the classification accuracy. Using the classified images, a postclassification comparison approach was used to detect LULC changes between 1999 and 2019. The study revealed that built-up land and agricultural land increased in their respective areas by 28.54 km2 (194.81%) and 35.80 km2 (27.16%) with corresponding annual change rates of 1.43 km·year−1 and 1.79 km·year−1. The area of bare land, forest land, herbaceous land, and waterbody, respectively, decreased by 0.05%, 90.52%, 71.67%, and 6.90%. The LULC changes in the study area were attributed to urbanization, population growth, social-economic growth, and climate change. The findings of this study provide information on the changes in LULC and driving factors, which Blantyre City authorities can utilize to develop sustainable development plans.

Soft Data in Hydrologic Modeling: Prediction of Ecologically Relevant Flows with Alternate Land Use/Land Cover Data

Watershed-scale hydrological models have become important tools to understand, assess, and predict the impacts of natural and anthropogenic-driven activities on water resources. However, model predictions are associated with uncertainties stemming from sources such as model input data. As an important input to most watershed models, land use/cover (LULC) data can affect hydrological predictions and influence the interpretation of modeling results. In addition, it has been shown that the use of soft data will further ensure the quality of modeling results to be closer to watershed behavior. In this study, the ecologically relevant flows (ERFs) are the primary soft data to be considered as a part of the modeling processes. This study aims to evaluate the impacts of LULC input data on the hydrological responses of the rapidly urbanizing Upper Cahaba River watershed (UCRW) located in Alabama, USA. Two sources of LULC data, i.e., National Land Cover Database (NLCD) and Digitized Landsat 5 Thematic Mapper (TM) images, were used as input in the Soil and Water Assessment Tool (SWAT) model for the years 1992 and 2011 using meteorological data from 1988 to 2013. The model was calibrated at the watershed outlet against daily streamflow from 1988 to 1993 using the 1992 LULC data and validated for the 2008–2013 period using the 2011 LULC datasets. The results show that the models achieved similar performances with both LULC datasets during the calibration and validation periods according to commonly used statistical rating metrics such as Nash Sutcliffe efficiency coefficient (NSE), coefficient of determination (R2), and model percent bias (PBIAS). However, LULC input information had substantial impacts on simulated ERFs such as mean monthly streamflow, maximum and minimum flows of different durations, and low flow regimes. This study demonstrates that watershed models based on different sources of LULC and applied under different LULC temporal conditions can achieve equally good performances in predicting streamflow. However, substantial differences might exist in predicted hydrological regimes and ERF metrics depending on the sources of LULC data and the LULC year considered. Our results reveal that LULC data can significantly impact the simulated flow regimes of the UCRW with underlaying influences on the predicted biotic and abiotic structures of aquatic and riparian habitats.

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).

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.