Landsat Satellite Images Research Articles

Mapping African wetlands for 2020 using multiple spectral, geo-ecological features and Google Earth Engine

Wetlands provide numerous ecosystem services essential for biodiversity conservation, climate change mitigation, and human well-being. Nevertheless, there are still no complete and uniform wetland maps providing information on the location, distribution, size, and changing status of wetlands in Africa. In this study, we used 130,000 Landsat satellite images and 270,000 sampling points evenly distributed across Africa to classify wetlands with a random forest classifier based on the Google Earth Engine (GEE) cloud platform. This resulted in the first systematic and comprehensive map of existing wetlands in Africa that quantified the area and distribution of these wetlands at a spatial resolution of 30 m, with an overall wetland producer accuracy of 90.22 % and user accuracy of 95.74 %. To address the seasonal variation in African wetlands, we used a dynamic threshold segmentation method based on the Otsu method, which can extract the extent of wetlands under different periods more accurately based on Landsat time series and African phenology data. Our results show that the total African existing wetlands extent from 2019 to 2021 (here in 2020), was approximately 1,341,500 km2, excluding shallow marine waters, comprises 3.98 % of Africa's total land area. Of these wetlands, inland wetlands account for 81 %, and coastal wetlands account for 19 %. The wetlands in Africa were symmetrically distributed around the equator and decreased with increasing latitude, accounting for 97 % of the total wetland area being within the 20°N and 20°S latitudes. African marshes were highly dynamic, expanding by 92.22 % from the dry to wet season. Our study demonstrates the substantial potential of large data samples, multiple wetland classification features (spectral-temporal, topographic, and hydrological features), and random forest classification in GEE for large-scale wetland mapping. The resulting wetland maps provide new baseline data for large-scale monitoring of seasonal dynamics and long-term trends in African wetlands.

ОСОБЕННОСТИ ИЗМЕНЕНИЯ РАСТИТЕЛЬНОГО ПОКРОВА ПСКОВСКОГО ОЗЕРА В УСЛОВИЯХ ДИНАМИКИ УРОВЕННОГО РЕЖИМА

The results of the analysis of literature data since 1949 and original long-term (2007–2019) studies of the higher aquatic vegetation of Lake Pskov are presented. Pskov Lake is a shallow water body with heightened trophic level and annual fluctuations of water level. Currently, the flora of the Lake includes 117 species. Since first studies, certain changes have occurred in the flora composition of the lake. We have not found 10 species, 3 of which were included in the Red Book of the Pskov region Data. 16 new species have appeared, among which 4 are listed in the Red Book of the Pskov Region and 2 in the Red Book of Estonia. 64 associations in the composition of vegetation were noted. The main cenose-formers are Phragmites australis (Cav.) Trin. et Steud., Potamogeton perfoliatus L., Schoenoplectus lacustris (L.) Palla., which play leading role in the Lake overgrowth in all years of research. The decrease in the water level in Pskov Lake leads to an increase of associations and number of species in them. With an increase in the water level in the lake, the aboveground biomass of the southern reed, the main edificator among helophytes, decreases. Communities of helophytes play the main role in the overgrowth of the shores of Pskov Lake. According to the analysis of Landsat satellite images (1988–2017), the average value of the lake area occupied by helophytes was 4.14%. According to our data for the period from 2007 to 2019, the proportion of hydrophytes amounted to ~6.3% of the water area. The total overgrowth of Lake Pskov has clearly increased: in 1966–1970, the overgrowth area was 5% of the water area, in 1988–1989 – 7.9%, in 2005 – about 12%. Currently (2007–2019), macrophytes occupy ~10.4% of the lake area.

Associations of greenness, public parks, and blue space with cardiovascular and respiratory disease hospitalization in the US Medicare cohort

Background and Aim: Natural environments have been linked to decreased risk of cardiovascular disease (CVD) and respiratory disease (RSD) mortality. However, few cohort studies have looked at associations of natural environments with CVD or RSD hospitalization. Our aim was to evaluate these associations in a cohort of U.S. Medicare beneficiaries (~63 million individuals). Methods: Our open cohort included all fee-for-service Medicare beneficiaries (2000-2016), aged ≥65, living in the contiguous U.S. We assessed zip code-level average greenness (Normalized Difference Vegetation Index, NDVI), public park cover based on the United States Geological Survey Protected Areas Database, and percent blue space cover based on Landsat satellite images. Cox-equivalent Poisson models were used to estimate associations of the exposures with first CVD and RSD hospitalization in the full cohort and among those living in urban zip codes (≥1000 persons/mile2). Results: NDVI was weakly negatively correlated with percent public park cover (Spearman ρ = -0.23) and not correlated with percent blue space (Spearman ρ = 0.00). After adjustment for potential confounders, an IQR (0.27) increase in NDVI was negatively associated with CVD (HR: 0.97, 95%CI: 0.96, 0.97), but not with RSD hospitalization (HR: 0.99, 95%CI: 0.98, 1.00). In urban zip codes, an IQR increase in NDVI was positively associated with RSD hospitalization (HR: 1.02, 95%CI: 1.00, 1.03). Percent public park cover was not associated with CVD or RSD hospitalization in the full or urban population. In stratified analyses, percent public park cover was negatively associated with CVD and RSD hospitalization for Medicaid eligible individuals and individuals living in low socioeconomic status neighborhoods in the urban population. We observed no associations of percent blue space cover with CVD or RSD hospitalization. Conclusions: This study suggests that natural environments may benefit cardiorespiratory health; however, benefits may be limited to certain contexts and certain health outcomes.

Sakarya Nehri’nin İnhisar (Bilecik) Çevresindeki 6 Yıllık Arazi Kullanımı Değişiminin Uzaktan Algılama Tabanında İncelenmesi

İnhisar (Bilecik) is located in the center-east of the study area, which is bounded by Söğüt (Bilecik) in the southwest and Yenipazar (Bilecik) in the northeast. Sakarya River, located in the center of the study area, provides continuity in the East-West direction for approximately 40 km and is fed by drains from both the north and the south. Marble enterprises with economic value are widely observed in the region. In addition, it is very important for the natural environment to protect areas such as İnhisar waterfall and Harmankaya Canyon Nature Park in the region from harm that may be caused by human activities. The main purpose of the study is to examine the areas with natural structures that are important for the environment on a regional scale with data provided by earth observation satellites and to define the time-dependent changes of these areas. Landsat-8 satellite images, a 12.5m-resolution digital elevation model and geology map constitute the main data of the study. The results of the change analysis conducted on the Landsat images classified with the random forest algorithm are discussed on the geology and topography maps. When the 6-year change analysis findings are interpreted, it is suggested that the mining activities, which intensified especially around Gölpazarı, should not approach the Yenipazar-İnhisar line in the east.

The urban vegetation and allergic rhinitis in Montreal, Canada

Background: Urban vegetation offers many benefits to population health and wellbeing. In addition to these benefits, trees also emit pollen that can cause allergies. Objective: This study aimed to assess the association between urban vegetation and allergic rhinitis in the Montreal area. Methods: Data on spring symptoms (April, May, June) of allergic rhinitis were retrieved from the 2008 and 2014-2015 Quebec Population Health Surveys (EQSP) and population-weighted. 2015 Landsat-8 satellite images and LiDAR (Light Detection and ranging) point cloud were used to calculate, respectively, the Normalized Difference Vegetation Index (NDVI) and the average crown volume of trees. The values of these indices were classified into population quintiles. Associations between vegetation parameters and the prevalence of symptoms of allergic rhinitis were assessed with logistic regression models within buffer zones of 100, 250, 500, and 1000 meters around the respondent’s six-digit residential postal codes, adjusting for sex, age and categories of income. Results: The combined sample of the two surveys totaled 6,901 respondents. The estimated prevalence of spring symptoms of allergic rhinitis in the population was 11.3% (CI95%: 10.4 – 12.2). The NDVI and average crown volume were not associated with symptoms of allergic rhinitis for any of the buffer zones. For a buffer zone of 250 meters, the adjusted odds ratio for an increase from the lowest to the highest NDVI value (Q1 and Q5) was 1.029 (CI95%: 0.764 – 1.385). The adjusted odds ratio for an increase from the lowest to the highest average crown volume value was 1.167 (CI95%: 0.878 - 1.552). Conclusion: The results suggest that vegetation density does not have an impact on the prevalence of spring symptoms of allergic rhinitis in residents of the Montreal urban area. More detailed assessments on the associations with vegetation types are needed. Keywords: Urban vegetation, allergic rhinitis, Remote Sensing, NDVI

Detecting land use and land cover change for a 28-year period using multi-temporal Landsat satellite images in the Jukskei River catchment, Gauteng, South Africa

The Jukskei River catchment is one of the urban catchments in the central part of Gauteng province covering a large part of City of Johannesburg Metropolitan Municipality and small part of Tshwane and Ekurhuleni Metropolitan Municipalities that have witnessed tremendous land use/land cover (LULC) change over time. Jukskei River catchment is one of the fastest growing catchments in terms of population and change in LULC over time. Therefore, it is very important to detect the nature and extent of these changes in order to identify the direction and future expansion of LULC within the catchment area. To accomplish that, multi-temporal satellite remotely sensed data acquired from Landsat-5 Thematic Mapper (TM) 1987, Landsat-5 Thematic Mapper (TM) 2001 and Landsat-8 Operational Land imager (OLI) 2015 were used to detect LULC change in Jukskei River catchment area. The Jukskei River catchment was classified into four major LULC classes including: Built-up area, bare surface, sparse vegetation and intact vegetation. The analysis of the results revealed that for the past 28 years (i.e., 1987-2015), built-up and bare surface areas have increased by 56.2% (42713.1 ha) and 8,2% (6225.1 ha) while intact and sparse vegetation have decreased by 9.8% (7455.0 ha) and 25.8% (19659.6 ha), respectively. The overall accuracies for 1987, 2001, and 2015, were 85.9%, 87.5%, and 92.5% respectively, with Kappa Index of Agreement (KIA) of 81.3%, 83.3%, and 90% which indicates the accuracy of classified images with the reference images. The results revealed by this study can be used for decision-making activities and policy development regarding land use management within the catchment.

Multi-decadal land cover change analysis over Sundarbans Mangrove Forest of Bangladesh: A GIS and remote sensing based approach

Both natural processes and anthropogenic actions alter land use and land cover but the impact of anthropogenic actions has dramatically increased since the last century in many regions of the world. This study attempts to investigate the land cover change of the Sundarbans and surrounding areas of Bangladesh in the last forty years by analyzing Landsat images. Sundarbans, having the richest and exceptional biodiversity, is the largest remaining tract of mangrove forest on earth facing multidimensional threats of degradation. Time series Landsat satellite images collected for five years are 1980 (MSS), 1990 (TM), 2000 (TM), 2010 (TM) and 2020 (OLI). By analyzing, comparing and visualizing the classified images using ArcMap 10.5 software the historical trend of land cover change of the study area is exhibited. The result revealed that a large amount of non-vegetated areas located outside of Sundarbans mangrove forest converted into water bodies due to shrimp cultivation. During this long time, the Sundarbans mangrove forest area remained unchanged except for little change occurred by soil erosion along the river bank and coastline. Kappa coefficient revealed the accuracy of the land cover map with a coefficient value of 0.88, 0.92, 0.90, 0.93 and 0.92 for 1980, 1990, 2000, 2010 and 2020 images respectively. This research will support policymakers and the forest department to improve management policy. Also, influence researchers to conduct case-specific micro-scale research in the region. • Land cover change of Sundarbans Mangrove forest and surrounding area was analysed using GIS and RS techniques. • Excessive shrimp cultivation altered land cover outside of Sundarbans. • Sundarbans Mangrove forest remained unchanged over the last 40 years except for little change occurred by soil erosion. • Cyclone, sea level rise and decreased river water flow affecting the coastal island and the river bank. • Kappa coefficient value of 0.88, 0.92, 0.90, 0.93 and 0.92 found for 1980, 1990, 2000, 2010 and 2020 images respectively.

Monitoring desertification in the Euphrates region south Iraq using Remote Sensing Techniques

Although Euphrates regions are very important agricultural areas, the land fertility declined due to drought episodes of desertification. Sand dunes developed into Euphrates areas and salinity increased, Landsat satellite images for years 1972, 1984, 1990, 2002, 2016 where used to study such changes and match for all records to identify negative phenomena. Separated layers and its spatial area for each year showed expansion of desertification (29%) during the period 1972 – 2002 as a result of wind come from two major sand resources areas on the other hand, period 2002 – 2016 witnessed a low of rate about 7% desertification due to the invasion of the Ahwar (swamps) by water that partly cover sand areas, salinity increased up to 21% on river banks and places of shallow groundwater with high evaporation, Gravel covering is a highly recommended to prevent soil erosion and dune development, about salinity must establish an active drainage channels for all area.

Assessing the performance of machine learning algorithms in Google Earth Engine for land use and land cover analysis: A case study of Muğla province, Türkiye

Regions with high tourism density are very sensitive to human activities. Ensuring sustainability by preserving the cultural characteristics and natural structure of these regions is of critical importance in order to transfer these assets to the future world heritage. Detecting and mapping changes in land use and land cover (LULC) using innovative methods within short time intervals are of great importance for both monitoring the regional change and making administrative planning by taking necessary measures in a timely manner. In this context, this study focuses on the creation of a 4-class LULC map of Muğla province over the Google Earth Engine (GEE) platform by utilizing three different machine learning algorithms, namely, Support Vector Machines (SVM), Random Forest (RF), and Classification and Regression Tree (CART), and on comparison of their accuracy assessments. For improved classification accuracy, as well with the Sentinel-2 and Landsat-8 satellite images, the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) are also derived and used in classification of the major land use classes, which are ‘built-up area & barren land’, ‘dense vegetation’, ‘water surface’, and ‘shrub, grassland & sparse vegetation’. Experimental results show that the most relevant algorithm is RF with 0.97 overall accuracy and 0.96 Kappa value, followed by SVM and CART algorithms, respectively. These results indicate that the RF classifier outperforms both SVM and CART classifiers in terms of accuracy. Moreover, based on the results of the RF classifier, 19% (2,429 km2) of the study region is classified as built-up area & barren land, 48% (6,135 km2) as dense vegetation, 2% (301 km2) as water surface and 30% (3,832 km2) as shrub, grassland & sparse vegetation class.

Determination of terrestrial EUNIS habitat types of Mount Ganos (Işıklar), Tekirdağ, Türkiye

In this study, it is aimed to determine the terrestrial European Union Nature Information System (EUNIS) habitat types of Mount Ganos (Işıklar) and its surroundings. Field studies were carried out from April to October 2021. Reference areas were determined for Maximum Likelihood (ML) classification during the field studies. To increase the accuracy and obtain the highest possible level of EUNIS habitat types, we used both reference areas observed in the field studies and processed land cover and habitat maps. These are; Landsat Satellite Images classified with ML, Corine Land Cover, and European ecosystem maps. Regarding both biodiversity and social activities, Mount Ganos is among the most significant natural areas in the Tekirdağ district. The northern slopes of the mountain have a rainier and more humid climate than the southern slopes which Mediterranean climate is dominant. The presence of various climate types and the remarkable altitude variations also contribute to the habitat diversity of the Mount Ganos. Many natural areas have been degraded due to anthropogenic effects such as mineral extraction, agricultural, tourism, and urbanization activities in the Mount Ganos region until today. In this study, a total of 9 ecosystems and 29 habitat types were determined for Mount Ganos according to the EUNIS classification. 21 of them were identified at level 3 and 8 of them ranged between 2 and 6 levels. The intensive unmixed crops (I1.1) are the most-covered EUNIS habitat type with 16173.16 hectares. This is followed by low and medium altitude hay meadows (E2.2, 9350.63 ha), Meso- and eutrophic Quercus, Carpinus, Fraxinus, Acer, Tilia, Ulmus, and related woodland (G1.A, 7548.73 ha) and Pseudomaquis (F5.3, 5926.65 ha). With this study, a portrait of the habitat destruction created by humans has also been drawn. The results of this study can be used by decision-makers to conserve the remaining natural habitats on Mount Ganos.

Associations of parks, greenness, and blue space with cardiovascular and respiratory disease hospitalization in the US Medicare cohort

Natural environments have been linked to decreased risk of cardiovascular disease (CVD) and respiratory disease (RSD) mortality. However, few cohort studies have looked at associations of natural environments with CVD or RSD hospitalization. The aim of this study was to evaluate these associations in a cohort of U.S. Medicare beneficiaries (∼63 million individuals). Our open cohort included all fee-for-service Medicare beneficiaries (2000–2016), aged ≥65, living in the contiguous U.S. We assessed zip code-level park cover based on the United States Geological Survey Protected Areas Database, average greenness (Normalized Difference Vegetation Index, NDVI), and percent blue space cover based on Landsat satellite images. Cox-equivalent Poisson models were used to estimate associations of the exposures with first CVD and RSD hospitalization in the full cohort and among those living in urban zip codes (≥1000 persons/mile2). NDVI was weakly negatively correlated with percent park cover (Spearman ρ = −0.23) and not correlated with percent blue space (Spearman ρ = 0.00). After adjustment for potential confounders, percent park cover was not associated with CVD or RSD hospitalization in the full or urban population. An IQR (0.27) increase in NDVI was negatively associated with CVD (HR: 0.97, 95%CI: 0.96, 0.97), but not with RSD hospitalization (HR: 0.99, 95%CI: 0.98, 1.00). In urban zip codes, an IQR increase in NDVI was positively associated with RSD hospitalization (HR: 1.02, 95%CI: 1.00, 1.03). In stratified analyses, percent park cover was negatively associated with CVD and RSD hospitalization for Medicaid eligible individuals and individuals living in low socioeconomic status neighborhoods in the urban population. We observed no associations of percent blue space cover with CVD or RSD hospitalization. This study suggests that natural environments may benefit cardiorespiratory health; however, benefits may be limited to certain contexts and certain health outcomes.

Impact of mining-induced deforestation on soil surface temperature and carbon stocks: A case study using remote sensing in the Amazon rainforest

The Amazon rainforest is one of the most important ecosystems on the planet; however, the environmental pressure created by anthropic activities require monitoring of this critical biome. In this study, we assessed the evolution of deforestation in an open mine pit and its impact on surface environment (i.e., temperature and carbon stocks) using remote sensing techniques. The study was carried out on an area of 11,283.3 ha in the municipality of Marabá, Pará State, Brazil, where the “Salobo” copper mine is located. A temporal analysis was conducted, using Landsat satellite images (2005–2020). Subsequently, the Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI) of the deforested area were determined. Mining-induced deforestation has expanded from 0.9 ha in 2005 to 2214 ha in 2020 with an increase in surface temperature of 10 °C in the period. The temperature difference between the pit and the adjacent forest ranged from 30 to 40 °C over the 15 years, while the temperature at the forest edges rose by 4 °C. The correlation coefficient between exposed soil temperatures and mining deforestation was 0.66. CO2 emissions, increasing from 0.005 Tg CO2 in 2005 to 1.82 Tg CO2 in 2020 due to mining deforestation. The findings demonstrate the significant environmental impact of deforestation, which may also have an influence on local climate. The results can provide scientific support for public policies aimed at mitigating the issue.

Understanding the Linkage between Urban Growth and Land Surface Temperature—A Case Study of Bangalore City, India

Planning for a sustainable future involves understanding the past and present problems associated with urban centers. Rapid urbanization has caused significant adverse impacts on the environment and natural resources. In cities, one such impact is the unsettling urban growth, resulting in the urban heat island (UHI) effect, which causes considerable positive feedback in the climate system. It can be assessed by investigating the relationships between urban Land Use/Land Cover (LULC) changes and changes in land surface temperature. This study links the urban transformations in Bangalore, India, between 2001 and 2021, with the city’s changing average land surface temperatures. LULC classification was performed on Landsat satellite images for the years 2001, 2011, and 2021, using the support vector machine (SVM) classification algorithm. LULC change analysis revealed an increase in the built-up area coinciding with a decreasing trend of water bodies, vegetation, and the area under the others (wasteland/open land/barren land) category. The results show that built-up increased from 462.49 km2 to 867.73 km2, vegetation decreased from 799.4 km2 to 485.72 km2, and waterbody declined from 34.28 km2 to 24.69 km2 in 20 years. The impact of urbanization was evident in Bangalore’s land temperature changes between 2001 and 2021, showing the average temperature increased by 0.34 °C per year between the highest UHI events, contrary to 0.14 °C per year in non-urbanized areas. It is hoped that the results of this study can help the urban planners of Bangalore city identify critical areas where improvement in urban dwelling could be planned sustainably according to the global smart cities concept, an offshoot concept of the Sustainable Development Goal (SDG)-11.

Giant Aufeis—Unknown Glaciation in North-Eastern Eurasia According to Landsat Images 2013–2019

Based on the analysis of Landsat satellite images over the period of 2013–2019, the number (6683) and total area (4529 km2) of giant aufeis fields (area ≥ 0.1 km2) were estimated for the territory of North-Eastern Eurasia. The contribution of aufeis runoff to river streamflow in different seasons was calculated for 58 hydrological gauges (area 523–526,000 km2). The contribution of aufeis and glaciers to water balance is compared. The aufeis resources vary from 0.4 to 4.25 km3 (or 3.7–11 mm) for individual basins of large rivers. They are at least 10.6 km3 in total or 5 mm of water depth on average for the study area. Aufeis annual runoff varies from 0.3 to 29 mm (0.1–22%, average 3.8%), with the share in winter runoff amount about 6–712% (average 112%) and the spring freshet 0.2–43% (average 7.1%). In general, the aufeis runoff exceeds the glacial runoff. The dynamics of aufeis formation are directly related to winter runoff, whose changes are observed in different parts of the cryosphere. The presented results are relevant for studying the impact of climate change on the hydrological cycle and its components in the permafrost regions of the Northern Hemisphere.

Analysis Long-Term and Spatial Changes of Forest Cover in Typical Karst Areas of China

In recent decades, China has exhibited the fastest and most remarkable social-economic development in the world. As a result of such development, the forest cover of the country has undergone radical changes. This paper aims to develop a method for analyzing long-term and spatial changes in forest cover based on historical maps and remote sensing images. Moreover, we will focus on the reduction or restoration of forests distributed at different altitudes, slopes, soils, and lithologic types in different periods, to reveal the problems that should be paid attention to in forest restoration in karst areas. A typical county of China was selected as the study area. A historical military operation map was considered the principal source of basic data. These data were then combined with Landsat satellite images to conduct quantitative analysis on changes in the spatial area and location of forest cover with a long time series. The findings are as follows: in terms of time series, the forest area in the study area showed a trend of decreasing at first and then increasing, with the year 1986 as the turning point. In terms of spatial patterns, a considerable difference is observed in regions with changes in forest cover under different historical periods. Changes are obvious in limestone areas, rock soil areas, and areas with an elevation of 2000–2500 m and a slope gradient of 6°–15°. Spatial–temporal changes in forest cover reflect the effects of the war, national policy, and economic development to some extent. All these results indicate that, despite its limitations, a historical map is a valuable document for studying an ecological environment.

Soil Moisture Mapping with Moisture-Related Indices, OPTRAM, and an Integrated Random Forest-OPTRAM Algorithm from Landsat 8 Images

Remote sensing tools have been extensively used for large-scale soil moisture (SM) mapping in recent years, using Landsat satellite images. Rainfall, soil clay percentage, and the standardized precipitation index play key roles in determining the moisture content of crop fields. The objective of this study was to (i) calculate and determine the effectiveness of moisture-related indices in predicting surface SM, (ii) predict surface SM from satellite images using the Optical Trapezoid Model (OPTRAM), and (iii) evaluate if the OPTRAM predictions can be improved by incorporating weather station, soil, and crop data with a random forest algorithm. The ENVI® platform was used to create moisture-related indices maps, and the Google Earth Engine (GEE) was used to prepare OPTRAM maps. The results showed a very weak relationship between the moisture-related indices and surface SM content where r2 and slopes were ˂0.10 and ˂0.20, respectively. OPTRAM SM, when compared with in situ surface moisture, showed weak relationship with regression values ˂0.2. Surface SM was then predicted using random forest regression using OPTRAM moisture values, rainfall, and the standardized precipitation index (SPI), and percent clay showed high goodness of fit (r2 = 0.69) and low root mean square error (RMSE = 0.053 m3 m−3).

A spatio-temporal assessment and prediction of Ahmedabad’s urban growth between 1990–2030

Analyzing long term urban growth trends can provide valuable insights into a city’s future growth. This study employs LANDSAT satellite images from 1990, 2000, 2010 and 2019 to perform a spatiotemporal assessment and predict Ahmedabad’s urban growth. Land Use Land Change (LULC) maps developed using the Maximum Likelihood classifier produce four principal classes: Built-up, Vegetation, Water body, and “Others”. In between 1990–2019, the total built-up area expanded by 130%, 132 km2 in 1990 to 305 km2 in 2019. Rapid population growth is the chief contributor towards urban growth as the city added 3.9 km2 of additional built-up area to accommodate every 100,000 new residents. Further, a Multi-Layer Perceptron — Markov Chain model (MLP-MC) predicts Ahmedabad’s urban expansion by 2030. Compared to 2019, the MLP-MC model predicts a 25% and 19% increase in Ahmed-abad’s total urban area and population by 2030. Unaltered, these trends shall generate many socio-economic and environmental problems. Thus, future urban development policies must balance further development and environmental damage.

Coastal and landuse changes of Burullus Lake, Egypt: A comparison using Landsat and Sentinel-2 satellite images

Monitoring water bodies such as lakes is expensive, but remote sensing made it cost-effective. The extensive anthropogenic activities negatively impact the water lakes everywhere. Moreover, the Egyptian coastal lakes are not an exception. This paper investigates changes in land use/land cover and detects coastal changes of Buullus Lake, ranked to be the second-largest Egyptian northern Mediterranean coastal lake, using Remote Sensing and Geographic Information Systems (GIS) techniques. Multitemporal Landsat satellite images from 1984 to 2019 have been collected and processed where Landsat and Sentinel-2A images for 2019 have been used for comparison purposes. In addition field visits have been conducted to verify the obtained results. The maximum likelihood supervised classification approach was shown to be more accurate in monitoring changes in the Lake's water bodies. Classes of the supervised classification included eight classes; seawater, lake water, floating vegetation, sand, urban, agricultural areas, fish farms, and drainage canals. Analysis of the satellite data indicated that: between 1984 and 2019, the lake's water bodies and floating vegetation area were reduced by 16 % and 52 %, respectively. While agricultural lands increased by (648 km2). In addition, there has been a significant growth in the area of fish farms (290 km2). The resulted updated spatiotemporal maps and extracted water features assist decision-makers in taking the necessary steps to stop or minimize anthropogenic activities. These steps will help to restore or maintain the lake to host the government planned activities to achieve the Sustainable Development Goals (SDGs) on the lake level.

Use of cellular automata-based artificial neural networks for detection and prediction of land use changes in North-Western Dhaka City.

The purpose of this study was to analyze the trend of change in land use land cover (LULC) and land surface temperature (LST) in Mirpur and its surrounding area over the last 30years using Landsat satellite images and remote sensing indices, and to develop relationships between LULC types and LST, as well as to analyze their impact on local warming. Using this analyzed data, a further projection of LULC and LST change over the next two decades was made. From 1989 to 2019, 5-year intervals of Landsat 4-5 TM and Landsat 8 OLI images were utilized to track the relationship between LULC changes and LST. The modeled LST was validated with MODIS-derived LST within the study area. Cellular automata-based artificial neural network (CA-ANN) algorithm was used to model the LULC and LST maps for the year 2039. The Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) were analyzed to determine their link with LST. The relation between LST and LULC types indicates that built-up area raises LST by substituting non-evaporating surfaces for natural vegetation. The average surface temperature was increasing steadily for the last 30years. For the year 2019, it was determined that roughly 86% of total land area has been converted to built-up area and that 89% of land area had an LST greater than 28°C. According to the study, if the current trend continues, 72% of the Mirpur area is predicted to see temperatures near 32°C in 2039. Additionally, LST had a significant positive association with NDBI and a negative correlation with NDVI. The overall accuracy of LULC was greater than 90%, with a kappa coefficient of 0.83. The study may assist urban planners and environmental engineers in comprehending and recommending effective policy measures and plans to mitigate the consequences of LULC.

Land use change and its effect on selected soil properties in the northwest highlands of Ethiopia

Evidence on land use/land cover (LULC) change and its effect on soil properties are important for sustainable land management interventions. Hence, this study was conducted to analyze LULC change over a period of 31 years and to evaluate the effects of land use on soil properties in the Ganzer watershed, northwest Ethiopia. Landsat satellite images (1988, 2002 and 2019) were used as a source of information image analysis and LULC classification were done using ERDAS imagine 2010 software. About 24 composite soil samples were collected from four land use types (natural forest, plantation forest, cultivated and grazing lands) at two soil depths (0–20 and 20–40 cm) to determine soil properties. Standard soil analytical methods were used in carrying out soil analysis. The result revealed that the study site has undergone extensive land use changes where cultivated and grazing lands declined by 5.4% and 22.6%, respectively. However, the settlement and forest lands increased by 7.9% and 20%, respectively (1988–2019). The soil physicochemical properties differed significantly (p < 0.05) across the land use types and with soil depth. Higher contents of clay, pH, organic carbon (OC), total nitrogen (TN), available phosphorus (AP), exchangeable bases (Ca, Mg, K, Na) and CEC were recorded in the natural forest than in the other land use types. Similarly, pH, clay, BD and exchangeable bases increased with an increase in soil depth across all land use types. Generally, LULC change in the study area showed a significant increase in settlement and forest lands due to population pressure and expansion of eucalyptus plantation forests. These inappropriate land use changes have a negative effect on soil properties. Therefore, an appropriate and effective intervention in the land use systems should be implemented to amend soil properties.