LULC Classes Research Articles

The Nexus between Land Use/Cover changes and Land Surface Temperature: Remote sensing based Two-Decadal Analysis

The use of Remote Sensing (RS) data is crucial for promptly detecting and monitoring changes in both short and long term, providing real time information on Land Use/Cover (LULC), Land Surface Temperature (LST), and Enhanced Vegetation Index (EVI), adapting spatio-temporal variations. The primary focus of this study is to assess the effect of LULC changes on LST in Tashk-Bakhtegan and Maharloo (TBM) lakes basin, Iran, within 2001, 2011, and 2021, using MODIS data. Specifically, five main LULC classes involving: water body, rangeland, cropland, urban area, and bareland were identified. Beside accuracy and transition of LULC maps using User Accuracy (UA), Producer Accuracy (PA), and Kappa Coefficient (KC), the analysis included changes in LULC, Enhanced Vegetation Index (EVI), and LST, as well as the relationship among them when vegetation cover was at its peak. Moreover, a one-way analysis of variance (ANOVA) test was performed to group these variables using Duncan's test. The results showed that the accuracy of LULC maps were more than 84% for all the years. Furthermore, the conversion of croplands to rangelands showed the most significant changes, with a total of 1311.38 km2 during 2001–2021. Average EVI remained almost stable across the total area, whereas average LST generally increased by 0.65 °C. Barelands consistently exhibited the highest temperatures in all the years, followed by urban areas. While no significant changes were observed in the EVI averages, significant changes were observed in the LST across all LULC classes in different years. The results also indicated a consistent negative correlation between LST and EVI, stronger in croplands than rangelands, with Spearman's correlation coefficient of −0.714, −0.674, and −0.623 over the total area in 2001, 2011, and 2021, respectively. The findings are crucial for land planners to comprehend the effects of LULC changes on LST to adopt appropriate strategies in the TBM lakes basin.

Geo-environmental monitoring of coastal and land resources for Coatzacoalcos coastal region

Coatzacoalcos is one of the notable Mexico coastal areas regarding location, activities, and resources. The purpose of the current study is to use geographic techniques to assess the spatial and temporal changes in Land Use/Cover (LULC), Land Surface Temperature (LST), and Shoreline as a result of anthropogenic and natural factors. Four calibrated multi-temporal Landsat images, dated 1993, 2003, 2013, and 2023, were combined and processed to accomplish this goal. The six primary LULC classes forest, built-up land, mining, water bodies, and agriculture were mapped using RF Machine learning algorithms. The last period (2003–2023) shows the highest rate of built-up land expansion, while the years 1993–2013 show the lowest rate of agricultural land reduction. Forest cover in the southeast decreased steadily from 339.25, 308.49, 287.43, and 211.83 km2 in 1993, 2003, 2013, and 2023. There is a slight difference in mean LST between built-up areas that recorded 26, 29, 32, and 33 °C in 1993, 2003, 2013, and 2023, respectively. The following is an order of LULC from lowest to highest LST during 2023 water body 12 °C, Forest 19 °C, Agriculture Land 22 °C, Mining 26 °C, Barren Land 34 °C and Built-up Land 33 °C. In Zone B near Coatzacoalcos showed particularly high accretion as a result of building artificial structures. Maximum rates of accretion and erosion were recorded at 2.0 m/year and −9.63 m/year, respectively. Fortunately, Zone C has minimal urban development and is home to forests and agricultural areas, so the potential negative effects are reduced. This study aids in understanding spatial and temporal LULC changes, LST variations, and shoreline dynamics, crucial for the sustainable management of Coatzacoalcos coastal resources in Mexico.

LULC changes in the region of the proposed Pwalugu hydropower project using GIS and remote sensing technique

<p>Proper understanding of LULC changes is considered an indispensable element for modeling. It is also central for planning and management activities as well as understanding the earth as a system. This study examined LULC changes in the region of the proposed Pwalugu hydropower project using remote sensing (RS) and geographic information systems (GIS) techniques. Data from the United States Geological Survey's Landsat satellite, specifically the Landsat Thematic Mapper (TM), the Enhanced Thematic Mapper (ETM), and the Operational Land Imager (OLI), were used. The Landsat 5 thematic mapper (TM) sensor data was processed for the year 1990; the Landsat 7 SLC data was processed for the year 2000; and the 2020 data was collected from Operation Land Image (OLI). Landsat images were extracted based on the years 1990, 2000, and 2020, which were used to develop three land cover maps. The region of the proposed Pwalugu hydropower project was divided into the following five primary LULC classes: settlements and barren lands; croplands; water bodies; grassland; and other areas. Within the three periods (1990–2000, 2000–2020, and 1990–2020), grassland has increased from 9%, 20%, and 40%, respectively. On the other hand, the change in the remaining four (4) classes varied. The findings suggest that population growth, changes in climate, and deforestation during this thirty-year period have been responsible for the variations in the LULC classes. The variations in the LULC changes could have a significant influence on the hydrological processes in the form of evapotranspiration, interception, and infiltration. This study will therefore assist in establishing patterns and will enable Ghana's resource managers to forecast realistic change scenarios that would be helpful for the management of the proposed Pwalugu hydropower project.</p>

Satellite imagery-based tropical cyclone impact assessment on LULC and vegetation: a case study of cyclone Biparjoy.

Cyclones pose significant threats to coastal regions, triggering widespread ecological and hydrological changes. This study presents an impact assessment of cyclone Biparjoy, which originated in the Arabian Sea and made landfall on the Gujarat coast of India on June 16, 2023. The research encompasses flood delineation and vegetation impact assessment in the Kachchh and Devbhoomi Dwarka districts of Gujarat, India. Sentinel-1A (VV polarized) imagery is used to precisely map the extent of inundation caused by cyclone Biparjoy. The total flooded area for Kachchh and Devbhoomi Dwarka was calculated to be 6556.73 km2 and 104.49 km2, respectively. The most affected LULC class in Kachchh is found to be bare ground (38.95%) and rangeland (38.94%) which is the major part of the Northeastern Rann region. In Dwarka, most waterlogging has been seen in the cropland (33.04%). The classification of the water and non-water pixels for the pre- and post-images is validated using the ROC curve. The accuracy was 93.2% and 89.5% for pre- and post-images classifications, respectively. Furthermore, vegetation impact was investigated to estimate the cyclone's ecological consequences. Alterations in vegetation density and overall health were estimated by calculating Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from both pre- and post-cyclone Landsat-8 OLI images. The cyclone-induced damage is further assessed for the mangrove trees in Kori Creek. This work contributes to understanding the ecological repercussions of such extreme weather events.

Three decades of land use and land cover changes in an East African lake and their implications for the conservation of bird communities

AbstractLand use and land cover (LULC) dynamics using GIS and remote sensing techniques measure extent and spatial structure, which are critical for monitoring biomes. A digital LULC detection technique with multi‐temporal satellite imagery analysis was employed at four timepoints from 1987 to 2017 and performed using supervised classifications method in ERDAS imagine 2015. For image classification, the five major LULC types included forests, built‐up land, agricultural land, water bodies and grasslands. LULC of grasslands, forests and water bodies reduced, while LULC of agricultural and built‐up lands increased. Avifaunal surveys were conducted at 11 sites across the LULC classes to compare species richness and abundance. The decrement of forests and water bodies would result in habitat loss, affecting endemic and threatened avifauna, as forests showed highest avifaunal species diversity (H′ = 3.80). Despite the decrement, grasslands demonstrated higher avifaunal species richness (107) and abundance (2767). Species richness decreased significantly from grasslands to water bodies and agricultural land (p < 0.05), and from forests to agricultural and built‐up land (p < 0.05), and water bodies (p < 0.01). Avifaunal abundance decreased significantly from grasslands to all other LULC classes. Regular LULC change assessment is integral to avifaunal habitat and biodiversity conservation efforts, particularly at Lake Tana.

Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine

Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat island (SUHI) phenomena. This research focuses on the nexus between LULC alterations and variations in LST and air temperature (Tair), with a specific emphasis on the intensified SUHI effect in Kharkiv, Ukraine. Employing an integrated approach, this study analyzes time-series data from Landsat and MODIS satellites, alongside Tair climate records, utilizing machine learning techniques and linear regression analysis. Key findings indicate a statistically significant upward trend in Tair and LST during the summer months from 1984 to 2023, with a notable positive correlation between Tair and LST across both datasets. MODIS data exhibit a stronger correlation (R2 = 0.879) compared to Landsat (R2 = 0.663). The application of a supervised classification through Random Forest algorithms and vegetation indices on LULC data reveals significant alterations: a 70.3% increase in urban land and a decrement in vegetative cover comprising a 15.5% reduction in dense vegetation and a 62.9% decrease in sparse vegetation. Change detection analysis elucidates a 24.6% conversion of sparse vegetation into urban land, underscoring a pronounced trajectory towards urbanization. Temporal and seasonal LST variations across different LULC classes were analyzed using kernel density estimation (KDE) and boxplot analysis. Urban areas and sparse vegetation had the smallest average LST fluctuations, at 2.09 °C and 2.16 °C, respectively, but recorded the most extreme LST values. Water and dense vegetation classes exhibited slightly larger fluctuations of 2.30 °C and 2.24 °C, with the bare land class showing the highest fluctuation 2.46 °C, but fewer extremes. Quantitative analysis with the application of Kolmogorov-Smirnov tests across various LULC classes substantiated the normality of LST distributions p > 0.05 for both monthly and annual datasets. Conversely, the Shapiro-Wilk test validated the normal distribution hypothesis exclusively for monthly data, indicating deviations from normality in the annual data. Thresholded LST classifies urban and bare lands as the warmest classes at 39.51 °C and 38.20 °C, respectively, and classifies water at 35.96 °C, dense vegetation at 35.52 °C, and sparse vegetation 37.71 °C as the coldest, which is a trend that is consistent annually and monthly. The analysis of SUHI effects demonstrates an increasing trend in UHI intensity, with statistical trends indicating a growth in average SUHI values over time. This comprehensive study underscores the critical role of remote sensing in understanding and addressing the impacts of climate change and urbanization on local and global climates, emphasizing the need for sustainable urban planning and green infrastructure to mitigate UHI effects.

Analysis of Land Use and Land Cover Change Detection for Indore District of Malwa Plateau Region Using Supervised Machine Learning

The main aim of this work is to find out LULC classes for the Indore region. For the last seven years, Indore has been crowned the cleanest city in India according to the latest ranking of 2023. In the Indian state of Madhya Pradesh, the city and district of Indore are situated on the southern edge of the Malwa plateau. The municipalities of the Indore district majorly cover Manpur, Depalpur, Goutampura, Mhow, Hatod, Betma, Rau and Sanwer, with an area of approximately 3900 square kilometers. In contrast, Indore covers a land area of approximately 530 square kilometres. It is in charge of the nation’s slow but steady industrialization and population growth found in the last three decades. The city was much smaller and had a far smaller population thirty years ago. However, to evaluate the changes and decide on Indore city's future planning, Indore has taken over responsibility for the city's economic activities in recent years. New appropriate patterns should be recommended based on local factors like topography characteristics. The satellite image with multiple spectrums was used for the investigation. ArcGIS is based on pixel-by-pixel supervised classification using the maximum likelihood approach of Landsat satellite images from 2003 and 2023. The ground area, agriculture, and urbanization are linked to the LULC features. The various categories of classified land use and land cover features include the area of built-up regions, water bodies, cropland, forest, and barren land taken to predict the broad changes. Remotely sensed Landsat 5 images of 2003 and 8 images of 2023 were utilized to identify changes to accomplish this goal. This work clarifies the comparison of LULC classes for the Indore region.

Retrieval of high-resolution aerosol optical depth (AOD) using Landsat 8 imageries over different LULC classes over a city along Indo-Gangetic Plain, India.

Aerosol optical depth (AOD) serves as a crucial indicator for assessing regional air quality. To address regional and urban pollution issues, there is a requirement for high-resolution AOD products, as the existing data is of very coarse resolution. To address this issue, we retrieved high-resolution AOD over Kanpur (26.4499°N, 80.3319°E), located in the Indo-Gangetic Plain (IGP) region using Landsat 8 imageries and implemented the algorithm SEMARA, which combines SARA (Simplified Aerosol Retrieval Algorithm) and SREM (Simplified and Robust Surface Reflectance Estimation). Our approach leveraged the green band of the Landsat 8, resulting in an impressive spatial resolution of 30m of AOD and rigorously validated with available AERONET observations. The retrieved AOD is in good agreement with high correlation coefficients (r) of 0.997, a low root mean squared error of 0.035, and root mean bias of - 4.91%. We evaluated the retrieved AOD with downscaled MODIS (MCD19A2) AOD products across various land classes for cropped and harvested period of agriculture cycle over the study region. It is noticed that over the built-up region of Kanpur, the SEMARA algorithm exhibits a stronger correlation with the MODIS AOD product compared to vegetation, barren areas and water bodies. The SEMARA approach proved to be more effective for AOD retrieval over the barren and built-up land categories for harvested period compared with the cropping period. This study offers a first comparative examination of SEMARA-retrieved high-resolution AOD and MODIS AOD product over a station of IGP.

Multi-decade land cover/land use dynamics and future predictions for Zambia: 2000–2030

Human LULCC is the many driver of environmental changes. Accurate and up-to-date current and predicted information on LULCC is important in land use planning and natural resource management; however, in Zambia, detailed information on LULCC is insufficient. Therefore, this study assessed the dynamics of LULC change (2000–2020) and future projections (2020–2030) for Zambia. The ESA CCI land cover maps, which have been developed from Sentinel-2 images were used in this study. This dataset has a grid spatial resolution of 300 m for the 2000, 2010 and 2020. The 31 ESA CCI Classification were reclassified into ten (10) local Classifications using the r.class module in QGIS 2.18.14. The 2000 and 2010 LULC maps were used to simulate the 2020 LULC scenario using Artificial Neural Network (Multi-layer Perception) algorithms in Modules for Land Use Change Evaluation (MOLUSCE) plugin in QGIS 2.18.14. The 2010 and 2020 maps were used to predict the 2030 LULC classes. The reference 2020 and predicted 2020 LULC maps were used to validate the model. Predicted against observed 2020 LULC map, Kappa (loc) statistic was 0.9869. The 2020 LULC patterns was successfully simulated using ANN-MLP with accuracy level of 95%. LULC classes were predicted for 2030 using the 2010–2020 calibration period. The predicted 2030 LULC types shows an increase in built-up (71.44%) and decrease in cropland (0.73%) with reference to 2020 LULC map. Dense forest (0.19%), grassland (0.85%) and bare land (1.37%) will reduce from 2020–2030. However, seasonally flooded, sparse forest, shrub land, wetland and water body will increase marginally. The largest LULC change is from forest into other LULC types. The insights from this study show that ANN-MLP can be used to predict LULCC, and that the generated information can be employed in land use planning and National Adaptation Plans at regional and national scale.

Analyzing land use and land cover change in the Pra River Basin: A multi-tool approach for informed decision-making

Quantifying land use and land cover change (LULCC) is essential for addressing societal challenges and conserving ecosystems. This study focuses on analyzing the dynamics of five LULC classifications at a 30 m spatial resolution using satellite imagery from Landsat, coupled with various tools (Google Earth Engine, ArcMap, QGIS, and IDRISI SELVA) and indicators such as the Bare Soil Index (BSI). Historic time series data (2007, 2015, and 2023) were utilized to assess land change and predict future scenarios (2030 and 2063) using (CA) Markov models of the land change modeler (LCM). With a kappa coefficient and overall accuracy of 95 %, and 93 % respectively, our analysis reveals that LUC has significantly impacted nearly a third of the Pra River Basin (PRB) land area in two decades (2007–2023) after the launch of Agenda 2063 (The Africa We Want), with projected future impacts doubling under a business-as-usual (BAU) scenario. Geographical variations in LULC change processes are evident, with farmland abandonment, deforestation, and agricultural expansion observed predominantly in the northwestern region (Upper Pra, Anum, and Birim) of the basin. A 25 % increment of cultivated/farmland from 2007 to 2015 and a further 8 % in 2023. The northeast region (Upper and Lower Offin, Twifo Praso) faces deforestation and encroachment along riverbanks, along with built-up expansion due to mining activities. The northern region (Oda and Upper Pra) experiences exponential built-up and bare land increment. Accelerated phases of LULC change are attributed to factors such as mining (illegal and small-scale), agricultural production, migration, and population increment highlighting the influence of economic activities on land use dynamics. The concept gives a hybrid approach of using multi-tools in offering critical awareness of the state of the river system and encouraging the need for informed decision-making towards the anthropogenic impacts on the basin through effective management of LULC classes.

Spatio-Temporal Analysis of Land Use/Land Cover Change and the Implications on Sustainable Development Goals in the Vea Catchment of Ghana

Globally, land use and land cover change (LULCC) is recognized as posing a substantial environmental challenge with lasting and severe consequences. This study quantified LULCC within the Vea catchment area spanning 24 years, from 1998 to 2022. Landsat TM imagery from 1998, ETM+ imagery from 2006, and Landsat 8 OLI imagery from 2014 to 2022 were used. A supervised classification algorithm was employed to classify the LULC classes. The findings reveal a notable transformation in the Vea catchment area from 1998 to 2022, primarily by converting grassland to cropland. Agricultural activities emerged as a significant contributor to the observed LULCC trend. Notably, cropland expanded from 10.9% to 51.98% between 1998 and 2022, while grassland and mixed vegetation/forest areas decreased from 54.8% to 18.14% and 31.7% to 22.73%, respectively. These results, it is argued, underscore the potential implications for achieving the targets set by the UN's Sustainable Development Goals.

The multiscale nexus among land use-land cover changes and water quality in the Suquía River Basin, a semi-arid region of Argentina

Agricultural intensification and urban sprawl have led to significant alterations in riverscapes, and one of the critical consequences is the deterioration of water quality with significant implications for public health. Therefore, the objectives of this study were the assessment of the water quality of the Suquía River, the assessment of LULC change at different spatial scales, and the analysis of the potential seasonal correlation among LULC change and Water Quality Index (WQI). The Sample Sites (SS) 1 and 2 before Cordoba city had the highest WQI values while from SS3 the WQI decreased, with the lowest WQI close to the wastewater treatment plant (SS7) after Cordoba city. From SS8 in a agricultural context, the WQI increases but does not reach the original values. In light of analysis carried out, the correlation between water quality variables and the different LULC classes at the local and regional scales demonstrated that WQI is negatively affected by agricultural and urban activities, while natural classes impacted positively. The spatialization of the results can help strongly in assessing and managing the diffusion of point and non-point pollution along the riverscape. The knowledge gained from this research can play a crucial role in water resources management, which supports the provision of river ecosystem services essential for the well-being of local populations.

High-resolution land use/cover forecasts for Switzerland in the 21st century

We present forecasts of land-use/land-cover (LULC) change for Switzerland for three time-steps in the 21st century under the representative concentration pathways 4.5 and 8.5, and at 100-m spatial and 14-class thematic resolution. We modelled the spatial suitability for each LULC class with a neural network (NN) using > 200 predictors and accounting for climate and policy changes. We improved model performance by using a data augmentation algorithm that synthetically increased the number of cells of underrepresented classes, resulting in an overall quantity disagreement of 0.053 and allocation disagreement of 0.15, which indicate good prediction accuracy. These class-specific spatial suitability maps outputted by the NN were then merged in a single LULC map per time-step using the CLUE-S algorithm, accounting for LULC demand for the future and a set of LULC transition rules. As the first LULC forecast for Switzerland at a thematic resolution comparable to available LULC maps for the past, this product lends itself to applications in land-use planning, resource management, ecological and hydraulic modelling, habitat restoration and conservation.

Spatial and temporal disturbances in the zone of mining of ferruginous quartzites of the Kostomuksha cluster, NW Russia

The research presents new methods for analyzing spatial and temporal disturbances, focusing on landscape ecology. The study covers the period from the beginning of the development of the field to the present stage. The Land Use and Land Сover (LULC) change analysis is based on processing of Landsat satellite imagery. The method uses landscape metrics, SDI calculation, and mapping to describe the mechanisms of transformation and self-regulation of the system. Landscape metrics include three groups: areas and edges, shape metrics, and diversity metrics. An increase in landscape fragmentation over time is shown. All LULC classes have been changed. The area of the “mining" class has been increased in 7.7% (from 0 to 55.5 thousand km2). The increase in the area of quarries occurs evenly at the expense of all other land use classes. The data obtained confirm several stages of anthropogenic impact. An approach has been developed for mapping disturbances and assessing the impact on geosystemsIt was revealed that in the cell size range from 1500 to 2500 m, the landscape parameters are the most stable. The calculation of the spatial disturbance index is based on principal component analysis data. Factors were identified that indicate external load and the adaptation and self-regulation of the geosystem. Three degrees of disturbance of the territory are defined: low, medium, and high. In 2006, the maximum level of spatial disturbance was found, with the area of heavily disturbed areas being 68%. The results of this study indicate the importance of taking the necessary measures to reduce environmental risks.

Characterization of landuse and landcover dynamics and their impact on runoff generation patterns in dam catchments of Northern Ghana

Landuse alteration is a critical factor that exerts influence over hydrological mechanisms and the oversight of water resources across the globe. Northern Ghana is characterized by erratic rainfall, leading to the intense construction of small dams and dugouts. This study investigated historical landuse/cover (LULC) changes and physical factors associated with surface runoff generation within eight (8) sub-catchments namely; Guno, Gbahali, Sambu, Duago, Denugo, Kwisini, Sandu, and Nyeko, with areas of 29.5, 110, 38.5, 106.04, 67.81, 2.59, 10.56 and 120.65 km2, respectively, located in four (4) regions of northern Ghana. For LULC change detection, historical satellite imagery and geospatial data were used with the aid of remote sensing and applying supervised maximum likelihood classification to Landsat images of 1995, 2005, 2015, and 2023. Runoff factors and depths were identified based on LULC classes, hydrologic soil groups, and precipitation conditions prevailing in the areas, based on the curve number (CN) method. With regards to LULC, the results from the overall analysis gave an insight into the rampant conversion, where grassland/mix vegetation witnessed significant declines, with decreases ranging from 150 to 395.25% in different catchments. Agricultural and arable lands emerged as the dominant landuse category, indicating a remarkable expansion of 11.61–51.17 km2, from 1995 to 2023 respectively, followed by built-up areas. While grassland decreased in all catchments over the same period, in some places, open tree (savannah woodland) increased, which confirmed the implementation of the afforestation policy. The CN values offered essential insights into the hydrological characteristics of each catchment. In addition to the impact of LULC and watershed characteristics on runoff, the observed variations in runoff patterns can be attributed to interannual variability in total annual precipitation which is critical driver of runoff. The findings hold significant importance for developing effective strategies and integrated watershed management policies to contribute to better water resource management and inform sustainable landuse planning in the face of changing environmental conditions.

Spatiotemporal analysis and identifying the driving forces of land use change in the Abay district (Karagandy Region, Kazakhstan)

Land use and cover change (LUCC) affects the nature of human activities in a particular area. Therefore, the manifestation of the driving forces of these changes plays a decisive role. This paper analyses the LULC dynamics of the Abay district of Karagandy oblast from 2016 to 2023. The study’s main objective is to find the driving forces of land use based on the integrated assessment of spatio-temporal data (STD) and socio-economic, climatic and environmental indicators (SECEI). Classification of Sentinel- 2 images into LULC classes is carried out using the Random Forest (RF) algorithm on the Google Earth Engine (GEE) platform. The driving factors were assessed using principal component analysis (PCA) and linear regression (LR). The results obtained can be used to guide the development planning of the territory.

A spatio-temporal analysis of the magnitude and trend of land use/land cover changes in Gilgel Gibe Catchment, Southwest Ethiopia

Analyzing alterations in land use/land cover is crucial for water Scientists, planners, and decision-makers in watershed management. This examination enables the development of effective solutions to mitigate the adverse impacts resulting from such changes. The focus of this research was analyzing alterations in land use/land cover within the Gilgel Gibe Catchment in 1991 – 2021. LULC data of 1991-2021 were derived from multispectral Landsat images. Data were also gathered using field observations and key informant interview. Data of LULC classes (1991–2021) were generated utilizing supervised classification with maximum likelihood algorithm of ENVI 5.1 and ArcGIS 10.5. Change detection analysis and accuracy assessment were done where accuracy levels all the study periods were > 85 %, and the overall Kappa statistics of the periods were > 0.89. Built-up area and cultivated land of the catchment are increasing with increasing magnitude of change; whereas, while forest cover and grazing land of the catchment are shrinking with declining magnitudes of change, shrubland covers and water body are declining with increasing magnitude of change in the catchment. The net increase in degraded land is a reflection of the increasing degradation of natural resources in the catchment. Swift escalation of population and the subsequent raising demand for farmland and forest and shrub (e.g. fuel-wood and construction) products, decline yield, unemployment and lack of alternative income source, and open access and limited conservation of resources are the principal factors for the dramatic shrinkages of grazing, forest, water body and shrubland resources. Thus, concerned bodies should take rehabilitation measures to restore degraded lands, improve production and yield of farmland by increasing improved farm-inputs and irrigation, and create employment and alternative income sources for the youth, women and the poor so as to ensure sustainable rural livelihoods and to curb the impacts on forest, shrubland and other resources.

Bidirectional Recurrent Imputation and Abundance Estimation of LULC Classes With MODIS Multispectral Time-Series and Geo-Topographic and Climatic Data

Bidirectional Recurrent Imputation and Abundance Estimation of LULC Classes With MODIS Multispectral Time-Series and Geo-Topographic and Climatic Data

CA-Markov chain for simulation and prediction of LULC and assessing the status of water pollution in Manzala Lake after recent development

The Egyptian government recently undertook a large-scale national dredging project to improve the properties and productivity of the northern lakes. Manzala Lake has a distinctive ecological system that offers locals a variety of ecological services. This study was occurred using an integrating approach of remote sensing and Markov modeling to determine the land use/cover (LULC) change of Manzala Lake’s components. The physiochemical characteristics of the water were also measured. Six LULC classes namely; water, crops, vegetation, urban areas, bare lands, and, sabkha were detected in and around the lake in three different years (1998, 2010, and 2022). The LULC transition was studied and predicted using the CA-Markov model. The area of surface water, flooded vegetation, sabkha, and urban areas were increased by the year of 2022. The bare lands were declined more comparing with the past periods. The new developments in Manzala Lake made more changes in the LULC of lake ecosystems. Also, it is an indication to exploitation the bare areas around lakes into new urban areas. The obtained results were necessary for the revised management plan. It is essential for managing the lakes’ land resources, and vital to carefully consider the development strategies to determine how these projects will influence on the environment and biodiversity. In addition, to what extent this lake productivity will improve and help in the recovery of Egypt’s national income.

Soil CO2 emissions under different land-use managements in Mato Grosso do Sul, Brazil

Understanding the variability of soil CO2 emission across several land use and cover (LULC) classes and biomes and its relationship with climate variables is important to drive strategies that contribute to meeting local and international demands for sustainable development and low carbon agriculture. The hypothesis of this research is that soil CO2 emission in situ (FCO2) is variable between LULCs across different biomes and that there may be an association between soil CO2 flux and environmental variables such as temperature and soil moisture. This study evaluated FCO2, measured by a portable EGM-5 CO2 gas analyzer, CO2Flux model (obtained by remote sensing approach), soil moisture (SM), soil temperature (ST) and relationship between these variables in different LULC classes. We identified LULCs can contribute to carbon neutralization actions over the Cerrado, Atlantic Forest and Pantanal biomes located in State of Mato Grosso do Sul (MS), Brazil. Four LULC classes were evaluated in each biome: agriculture (soybean cultivation), pasture, eucalyptus plantation, and native vegetation. A principal component analysis (PCA) was performed to verify the relationship between biomes and LULC classes with the variables evaluated, and a Pearson correlation plot was created to assess the relationship between the variables evaluated. The lowest FCO2 values were found in eucalyptus and soybean crops, regardless of biome. Our findings reveal the existence of soil CO2 flux variability between the different LULCs and biomes. Pasture in Pantanal and Atlantic Forest biomes exhibited the highest FCO2 values. Eucalyptus cultivation and native forest showed negative CO2Flux values, regardless of biome. Lower FCO2 values were also observed for soybean cultivation. Such findings reinforce that native vegetation function as carbon sinks and that, therefore, their conservation is vital for the mitigation of CO2 emissions. However, soybean and eucalyptus farming can be strategic for low carbon agriculture in MS and carbon neutralization projects by simultaneously contribute to economic and sustainable development of the regions covered by the biomes evaluated here.