Land Cover Change Mapping Research Articles

The application of remote sensing and geographic information system techniques for the analysis of land use and land cover changes in Wayanad district of Kerala

Mapping Land Use and Land cover Changes (LULC) and detecting changes using remote sensing and Geographic Information System (GIS) techniques is a cost-effective way of gaining a good understanding of the land cover alteration processes caused by land use change and their effects. This study assessed the transformation of the Wayanad district landscape over a period of 23 years. LANDSAT satellite images (of 30 m resolution) encompassing the area at three epochs were classified into nine classes (coffee dominated mixed crop, built-up, evergreen forest, deciduous forest, grassland, mixed crop with built-up, paddy, tea plantation, and waterbody) using the maximum likelihood algorithm, resulting in classes for each land use. The results showed that over the past 23 years, coffee-dominated mixed crops have increased by 0.84% in 2014 and 11.84% in 2022 compared to 1999; deciduous forest area decreased 3.6% and increased 0.6% in 2014 and 2022, respectively. Tea plantations increased by 0.9% in 2014 and by 0.49% in 2022, which decreased by 0.41% compared to 2014. Built-up has increased by 6.42% and 6.02% in 2014 and 2022 respectively, and slightly decreased by 0.4% compared to 2014. Evergreen forest areas increased in 2014 by 6.68% and 2.28% in 2022 and decreased by 4.44% compared to the previous time period. Grass land areas have decreased by 6.25% and 5.33%, respectively, and mixed crops with built-up areas has decreased by 0.74% and remained the same in the last two epochs, while paddy and waterbodies have decreased by 3.4%, 15.79%, and 0.07 and 0.19%, respectively, in 2014 and 2022 of the total geographical area Keywords: Remote sensing, GIS techniques, LULC, forest cover

Analysis of Land Use and Land Cover Changes and Trends in Fantalle Range Land, East Shewa Zone, Oromia Regional State, Ethiopia

This study was conducted in Fantalle Range lands in East Shewa zone of, Oromia Region, Ethiopia, to assess land use land cover changes, trends, drivers and their socioeconomics. Household surveys were conducted through simple random sampling to collect qualitative data. Qualitative data are used to investigate the causes and effects of land use and land cover changes. SPSS software (version 20) was used for data analysis, and descriptive research methods were adopted. Additionally, map processing was done using ERDAS Imagine (version 9.1) and ArcGIS (version 10.1). The land use land cover classification activity was started by obtaining Landsat images of 1972, 1990, 2000 and 2020 at different intervals from the Earth Explorer (USGS) from the Landsat 4, Landsat 5, Landsat 7 and Landsat 8, respectively. Land use land cover change (LULCC) maps are generated based on year classification. Range land, agricultural land, woody vegetation, bare land and settlement are the five main LULCC categories generated from satellite data. The findings show that in the presence of LULCC, agricultural land, settlements and bare land expand significantly, while range land and woodland show a decreasing trend. The classification results of the 1972 image show that rangeland/grazing land accounts for the largest proportion of the land in this area, accounting for 31.6%. In addition, due to various factors, the number of livestock owned in pastoral areas is also decreasing. The main cause of changes in livestock types is drought, which can cause different impacts, such as feed and water shortages and health problems. Therefore, intervention in land use manipulation is needed to maintain ecosystems and natural resources. Furthermore, rangeland policies should be developed to maintain pastoral and pastoral systems.

Using Radiometric and Categorical Change to Create High-Accuracy Maps of Historical Land Cover Change in Watersheds of the Great Lakes Basin

Great Lakes Basin landscapes are undergoing rapid land cover and land use (LCLU) change. The goal for this study was to identify changes in land cover occurring in the Great Lakes Basin over three time periods to provide insights into historical land cover changes occurring on a bi-national watershed scale. To quantify potential impacts of anthropogenic changes on important yet vulnerable Great Lakes Wetland ecosystems, the historical changes in land cover over time are assessed via remote sensing. The goal is to better understand legacy effects on current conditions, including wetland gain and loss and the impacts of upland ecosystems on wetland health and water quality. Three key time periods with respect to Great Lakes water level changes and coastal wetland plant invasions were mapped using Landsat-derived land cover maps: 1985, 1995, and 2010. To address change between the three time periods of interest, we incorporate both radiometric and categorical change analysis and open-source tools available for assessing time series data including LandTrendr and TimeSync. Results include maps of annual land cover transition from 1985 to 1995 and 1995 to 2010 basin-wide and by ecoregion and an assessment of the magnitude and direction of change by land cover type. Basin-wide validated change results show approximately 776,854 ha of land changed from c.1980–1995 and approximately 998,400 ha of land changed from c.1995–2010. Both time periods displayed large net decreases in both deciduous forest and agricultural land and net increases in suburban cover. Change by ecoregion is reviewed in this study with many of the change types in central plains showing change in and out of agriculture and suburban land covers, the mixed wood plain ecoregion consisted of a mixture of agricultural, suburban, and forestry changes, and all top five change types in the mixed wood shield consisted of various stages of the forestry cycle for both time periods. In comparison with previous LCLU change studies, overall change products showed similar trends. The discussion reviews why, while most changes had accuracies better than 84%, accuracies found for change from urban to other classes and from other classes to agriculture were lower due to unique aspects of change in these classes which are not relevant for most change analyses applications. The study found a consistent loss in the deciduous forest area for much of the time studied, which is shown to influence the aquatic nitrogen implicated in the expansion of the invasive plant Phragmites australis in the Great Lakes Basin. This underscores the importance of LCLU maps, which allow for the quantification of historical land change in the watersheds of the Great Lakes where invasive species are expanding.

Spatio-temporal assessment of soil salinization utilizing remote sensing derivatives, and prediction modeling: Implications for sustainable development

This study aims to investigate the combined use of multi-sensor datasets (Landsat 4–5 &8 OLI satellite imagery, spatial resolution = 30 m) coupled with field studies to evaluate spatio-temporal dynamics of soil salinization along the coastal belt in West Bengal, India. This study assesses soil salinization by mapping the salinity and electrical conductivity of saturation extract (ECe) and utilizing spectral signatures for estimating soil salinity. The SI change (%) was analyzed (2021–1995), categorizing increases in salinity levels into 5%, 10%, and 50% changes possibly due to salt encrustation on the soil layers. The land use land cover (LULC) change map (2021–1995) demonstrates that the study area is continuously evolving in terms of urbanization. Moreover, in the study area, soil salinity ranges from 0.03 ppt to 3.87 ppt, and ECe varies from 0.35 dSm−1 to 52.85 dSm−1. Additionally, vulnerable saline soil locations were further identified. Classification of soil salinity based on ECe reveals that 26% of samples fall into the non-saline category, while the rest belong to the saline category. The Spectral signatures of the soil samples (n = 19) acquired from FieldSpec hand spectrometer show significant absorption features around 1400, 1900, and 2250 nm and indicate salt minerals. The results of reflectance spectroscopy were cross-validated using X-ray fluorescence and scanning electron microscopy. This study also employed partial least square regression (PLSR) approach to predict ECe (r2 = 0.79, RMSE = 3.29) and salinity parameters (r2 = 0.75, RMSE = 0.51), suggesting PLSR applicability in monitoring salt-affected soils globally. This study’s conclusion emphasizes that remote sensing data and multivariate analysis can be crucial tools for mapping spatial variations and predicting soil salinity. It has also been concluded that saline groundwater used for irrigation and aqua-cultural activities exacerbates soil salinization. The studywill help policymakers/farmers identify the salt degradation problem more effectively and adopt immediate mitigation measures.

Land cover change analysis in Majalengka Regency using the pan-sharpening method and random forest machine learning algorithm

The ever-increasing population has accelerated the need for housing and supporting facilities. Further, this growing number of residences and life support facilities resulted in changes in land cover. For instance, the construction of the West Java International Airport and the increase in the population resulted in land cover changes in Majalengka Regency, Indonesia. This study aims to analyze changes in land cover during 2014, 2018, and 2022 in the Majalengka district using Landsat 8 satellite imagery. We used the pan-sharpening research method, while for the closure classification, we used machine-learning random forest algorithms on the google earth engine platform. The land cover classification classes adopted in this study were natural vegetation, cultivated vegetation, open land, built-up land, and bodies of water. The obtained land cover classification results suggested overall accuracy values of 0.96, 0.94, and 0.93 in 2014, 2018, and 2022 respectively, with kappa index values of 0.950, 0.925, and 0.91667. The results indicate a trend of changes in the land cover in the Majalengka district. From 2014-2022, the trend of increasing land cover area was observed in open land and built-up land, while the decreasing land cover area was found in natural vegetation and cultivated vegetation areas. Using both the pan-sharpening method and the machine learning random forest algorithm, we established images with a more detailed appearance with an outstanding kappa accuracy value (above 0.85). Therefore, the developed algorithm can be used in land cover change mapping analysis.

Trends of Land Cover Change and Conservation Challenges of Grevy’s Zebra (Equus grevyi, Oustalet 1882) in Hallaydeghe Asebot Proposed National Park, Southeast Ethiopia

Large parts of biodiversity in protected areas (PAs) and their ecosystem services have been deteriorated due to land cover change over time. To reverse these effects, analysis of land cover change is essential. One of the protected areas, Hallaydeghe Asebot Proposed National Park (HAPNP), was assumed its land cover change and was undergone from one habitat type to others during the last three decades. As a result, wildlife species particularly Grevy’s zebra (Equus grevyi) in HAPNP are facing conservation challenges. Here, we aimed to examine the land cover changes of HAPNP for the period of 1990–2021 and assess Grevy’s zebra conservation challenges so as to improve the protection of the park and conservation of the species. Landsat images for the years 1990, 2006, and 2021 were generated to analyze land cover change of the study area. In addition, ground control points and information from elder and knowledgeable informants were collected to substantiate the result. Household survey, key informant interviews, and field observation checklist were utilized to collect data on conservation challenges. To generate land cover change maps, the satellite image for each period was classified using supervised classification. Content analysis was used to analyze conservation challenges of the species. The findings of this study revealed that 12.2% of grassland cover lost from 1990 to 2021 (i.e., an estimated annual average loss 0.4%). The remaining grassland cover in the HAPNP was aggregated in a small land area. On the other hand, the size of bushland increased by 26.4% during the same year (i.e., an estimated annual average gain 0.9%). These findings have implications for conservation and management of grazers. Major drivers for conservation challenges of Grevy’s zebra were habitat degradation, unintegrated development, and poor law enforcement practices. Grassland restoration program and species conservation challenges’ intervention strategy should be designed and implemented to safeguard the natural habitat and the species that occur in the HAPNP.

Accuracy Assessment of Land Use/Land Cover Indices for Al-Rusafa in Baghdad Governorate by Remote Sensing Technology and GIS

The management and planning of natural and artificial resources depend on accurately monitoring land cover changes. Land cover change mapping and monitoring used to require expensive field surveys. Remote sensing is cheaper and more practical for mapping land use and cover changes. The Tigris River divides the Iraqi capital, Baghdad, into two parts: Karkh and Rusafa. Al-Rusafa was selected as a study area for current research, which has had rapid population and urban growth in recent decades. The current research applies the support vector machine technique to supervised LU/LC maps’ classification into barren regions, water bodies, vegetation cover and built-up regions. Spectral indicators were calculated: Enhanced Vegetation Index, Modified Normalized Difference Water Index, Normalized Built-Up Area Index, Dry Bareness Index in addition to calculating the accuracy assessment and Kappa coefficient. Using the Landsat 9 satellite image, ArcGIS 10.8 and Envi5.3 software were used to analyze and evaluate the results and field points observed by GPS devices. The results showed that the SVM classification algorithm accurately revealed the categories of LU/LC, where the classification accuracy reached 95%, and that the arid lands covered most of the study area 848.864 km2 and water bodies 76.747 km2, the vegetation and the built-up regions 466.459 km2 and 439.077 km2, respectively. The spectral indices showed slightly different areas of barren lands (DBSI 752.589 km2, 93% accuracy), vegetation (EVI 423.651 km2, 96% accuracy), and water bodies (MNDWI 73.187 km2, 98% accuracy) and built-up areas (NBAI 501,731 km2, 90%accuracy). The Support Vector Machine method outperforms other classification methods, and the spectral indicators employed in this work are useful and dependable for extracting each LU/LC category. In conclusion, Landsat 9 satellite data can reliably and swiftly detect ground cover.

Forest Tales? Unravelling Divergent Land Use and Land Cover Change (LULCC) Maps and State Narratives in Vietnam’s Northern Uplands

The Vietnamese state has advocated for the sedentarization and market integration of upland northern farmers over the past thirty years, leading to both agrarian and forest transitions. This article presents a comprehensive land use and land cover change (LULCC) analysis of two adjacent upland borderland districts, Phong Thổ and Bát Xát, in northern Vietnam, spanning two neighboring inland provinces, Lai Châu and Lào Cai. These districts are primarily home to ethnic minority farmers who are encouraged by Vietnamese state officials to not only protect forests but to also transition toward cash crop cultivation from less intensive semi-subsistence agriculture. Our LULCC maps, covering the period from 1990 to 2020, revealed a reduction in the speed by which closed-canopy forests were disappearing. During interviews, state officials were confident that this was due to a range of state policies and state-sponsored initiatives, including the promotion of tree crops and payments for forest environmental services. Our own fieldwork in the region suggests other factors are also supporting this decline in deforestation rates, rooted in ethnic minority farmer livelihood decision making. Some state officials were also able to point to factors hindering a more positive result regarding forest cover, including population pressure and new infrastructure. Interestingly, despite our positive findings on Land use and land cover change (LULCC) related to forest cover, one-third of state officials, upon reviewing our LULCC maps, firmly maintained that errors had occurred. Some even proposed that there was an actual rise in forest cover. Our study shows that these discrepancies raise compelling questions about officials’ political motivations and ongoing pressures to uphold the central state’s reforestation and agrarian transition discourses.

THE EFFECT OF GLOBAL CLIMATE CHANGE ON RENEWABLE ENERGY POTENTIAL: SOLAR ENERGY AND ITS RELATIONSHIP WITH LAND COVER: THE CASE OF VAN / TURKIYE

Climate change has led to many problems related to both environment and livelihood. Increasing the use of renewable energy is one of a solution tool to prevent climate change and adapt to its effects. The aim of this study was, to probe the possible effects of climate change on solar energy potential and its relationship with the land cover in Van. VYYU SPP’s data on power generation was used to find which method of mapping was more accurate in predicting the solar energy potential in Van. According to the analysis of climate parameters, they were interpreted in regard to the effects of climate change and solar energy potential. Additionally, CORINE land cover change maps have been discussed and compared with the solar energy potential approach for the province of Van. Further, the solar energy potential of land cover classes has been presented and an assessment too and propositions have been made accordingly.

Land cover change analysis of buffer areas in New Capital City of Nusantara, Indonesia: A cellular automata approach on satellite imageries data

The proposed plan to move Indonesia's capital city to the New Capital City of Nusantara in East Kalimantan Province undoubtedly requires careful efforts to ensure food supply for the population. Population migration to the new capital may pose a food security challenge. To address this fundamental issue, one of the most crucial approaches is to establish buffer areas that can support the food needs of the new capital. The currently existing official Area Sampling Frame survey conducted by the government to assess food vulnerability faced several limitations, including weather conditions, field terrain variations, and high cost. In this study, we propose the utilization of remote sensing satellite imagery data in buffer areas to analyze changes and predict future land cover, which can provide valuable data for assessing food availability. We investigate the integration of a Cellular Automata method with the two most popular analytical methods of classical Logistic Regression and data-driven Artificial Neural Networks, known as CA-LR and CA-ANN, to identify and map land cover changes in the new capital buffer zones. Our findings reveal that both combined methods, CA-LR and CA-ANN, yield fairly promising results, with correctness and kappa statistic values exceeding 80%. Prediction results indicate that buffer areas are predominantly covered by trees, while built-up areas are still limited. The flooded vegetation cover, including rice fields, is predicted to decrease by 2024. This should be a matter of concern for stakeholders, considering the construction of the new capital city is still ongoing and the number of migrants is expected to keep rising.

A global land cover training dataset from 1984 to 2020

State-of-the-art cloud computing platforms such as Google Earth Engine (GEE) enable regional-to-global land cover and land cover change mapping with machine learning algorithms. However, collection of high-quality training data, which is necessary for accurate land cover mapping, remains costly and labor-intensive. To address this need, we created a global database of nearly 2 million training units spanning the period from 1984 to 2020 for seven primary and nine secondary land cover classes. Our training data collection approach leveraged GEE and machine learning algorithms to ensure data quality and biogeographic representation. We sampled the spectral-temporal feature space from Landsat imagery to efficiently allocate training data across global ecoregions and incorporated publicly available and collaborator-provided datasets to our database. To reflect the underlying regional class distribution and post-disturbance landscapes, we strategically augmented the database. We used a machine learning-based cross-validation procedure to remove potentially mis-labeled training units. Our training database is relevant for a wide array of studies such as land cover change, agriculture, forestry, hydrology, urban development, among many others.

Effect of Urbanization Through Land Coverage Classification

AbstractLand cover changes from rural or natural habitats to impervious surfaces that support various human activities are called urbanization. The evaluation of policy alternatives for future expansion and the promotion of sustainable urban development therefore place great emphasis on the mapping, analysis, and monitoring of land cover changes in metropolitan environments. By assessing changes in relevant environmental indicators at a range of scales, from local to regional, this study seeks to determine the rate of urbanization and analyze its impact on the environment in and around two key cities in Tamilnadu. Chennai and Madurai are the urban areas under investigation. The results are based on classifications of medium to high spatial resolution optical satellite images from 1995 to 2020. Various classification approaches with combinations of spectral, shape, and texture input information were combined to achieve high classification accuracy. Based on the categories, environmental indicators were created and used to calculate the impact of urbanization on the environment. The findings show that each research area experienced varying degrees of urban expansion and environmental impact. Urban areas grew tenfold between 1995 and 2015, according to a study comparing Chennai and Madurai. To evaluate the spatiotemporal dynamics of urbanization and its environmental impact in various metropolitan regions, this research shows the value of combining urban and environmental indicators with remote sensing data.

MAXIMUM LIKELIHOOD ALGORITHM DETECTS COASTAL WETLAND CHANGES IN TWO CONTRASTING COASTAL WETLANDS IN LOUISIANA

Abstract. Louisiana coastal wetlands contain about 37 percent of the estuarine herbaceous marshes in the conterminous United States. However, the combined effect of sea level rise and other anthropogenic factors have altered land use land cover over the last few years. This is true for two wetlands in coastal Louisiana, Barataria bay and Wax Lake delta. Barataria Bay, Louisiana, USA has experienced significant land loss. Updated information on the dynamics of change in these wetlands is limited and poorly documented. This information is necessary to develop strategies that will contribute to reversing and halting degradation. Thus, this study employed the Maximum Likelihood classifier on Landsat satellite imagery to assess land use and land cover changes in Barataria Bay and Wax Lake Delta, southeastern Louisiana, USA. The analysis revealed notable alterations in the land cover patterns over the study period. In Barataria Bay, there was a decrease in salt marsh areas with a corresponding increase in open water and Built-up area. In contrast, Wax Lake Delta demonstrated substantial land/wetland growth, with significant expansion of vegetation cover. The Maximum Likelihood classifier demonstrated high accuracy in classifying the land cover types, with an overall accuracy of 86% for Barataria Bay and 92% for Wax Lake Delta. These results highlight the effectiveness of the classifier in accurately identifying and mapping land cover changes in coastal environments. The findings contribute valuable insights for understanding the dynamics of coastal ecosystems and can inform decision-making processes for coastal management and conservation efforts.

Spatial Analysis of Intra-Annual Reed Ecosystem Dynamics at Lake Neusiedl Using RGB Drone Imagery and Deep Learning

The reed belt of Lake Neusiedl, covering half the size of the lake, is subject to massive changes due to the strong decline of the water level over the last several years, especially in 2021. In this study, we investigated the spatial and temporal variations within a long-term ecosystem research (LTER) site in a reed ecosystem at Lake Neusiedl in Austria under intense drought conditions. Spatio-temporal data sets from May to November 2021 were produced to analyze and detect changes in the wetland ecosystem over a single vegetation period. High-resolution orthomosaics processed from RGB imagery taken with an unmanned aerial vehicle (UAV) served as the basis for land cover classification and phenological analysis. An image annotation workflow was developed, and deep learning techniques using semantic image segmentation were applied to map land cover changes. The trained models delivered highly favorable results in terms of the assessed performance metrics. When considering the region between their minima and maxima, the water surface area decreased by 26.9%, the sediment area increased by 23.1%, and the vegetation area increased successively by 10.1% over the investigation period. Phenocam data for lateral phenological monitoring of the vegetation development of Phragmites australis was directly compared with phenological analysis from aerial imagery. This study reveals the enormous dynamics of the reed ecosystem of Lake Neusiedl, and additionally confirms the importance of remote sensing via drone and the strengths of deep learning for wetland classification.

Land Tenure of Forest Fringe Communities and Their Impact on Deforestation: A Case Study in Soeharto Forest Park

Soeharto Forest Park is a forest area located in East Kalimantan, Indonesia. Soeharto Forest Park has an area of about 61,840 hectares and is one of the critical forest areas in Indonesia. Soeharto Forest Park primarily serves as a conservation area and nature preservation. It contains high biodiversity, including various species of flora and fauna that are protected. The research methods involve field surveys, structured interviews, and secondary data analysis. Data was collected to understand community land tenure, including tenure rights, land use, and agricultural practices. In addition, deforestation impacts are identified through mapping land cover change and vegetation density analysis. It is just that over time, the connection of the surrounding community with Soeharto Forest Park is very high. Many people use the Forest Park area to become open land. This lasted from 1957 until 2022. The total land area of Soeharto Forest Park that has been opened is 335,994 m2 or 33,6 hectares. Many communities on the outskirts of Soeharto Forest Park that cleared land and used it as settlements, plantations, and rice fields from 1957 to 2022 highlights the deforestation in land use around the area over a long period. These changes reflect social, economic, and policy dynamics that affect local communities and the surrounding environment.

Long-term mapping of land use and cover changes using Landsat images on the Google Earth Engine Cloud Platform in bay area - A case study of Hangzhou Bay, China

Large-scale, long-term series, and high-precision land use and cover change (LUCC) mapping is the basic support for territorial spatial planning and sustainable development in the Bay Area. In response to the sustainable development agenda, for characteristics of high landscape fragmentation, strong surface heterogeneity and frequent land use type conversion in the Bay Area, this study developed a random forest (RF) algorithm that considers spectral bands, remote sensing indices and components of a principal component analysis, and the mapping and monitoring of LUCC in Hangzhou Bay from 1985 to 2020 based on Google Earth Engine (GEE) and Digital Shoreline Analysis System (DSAS) were carried out. The results are as follows. (1) The overall accuracy (OA) and kappa coefficient were 92.83% and 0.91, respectively. (2) During the study period, the areas of the construction land, water area, and bare land increased, while the areas of the wood land, cultivated fields, and tidal flats decreased. (3) During the study period, the total area of the tidal flats decreased from 181.65 km2 to 161.50 km2, with an average annual decrease of 0.58 km2, and the tidal flats were primarily concentrated on the south shore of Hangzhou Bay. (4) During the study period, the transfer of cultivated fields to construction land was the most significant (2268.05 km2). (5) During the study period, the length of the coastline decreased from 383.73 km to 362.80 km, with an average annual decrease of 0.60 km. According to the DSAS statistics, the net shoreline movement (NSM) of the coastline on the north shore of Hangzhou Bay was 773.58 m, the end point rate (EPR) and the linear regression rate (LRR) were 22.10 m/a and 27.00 m/a, respectively. The NSM of the south shore was 4109.57 m, and the EPR and LRR were 117.42 m/a and 132.22 m/a, respectively. The proposed methods improve the accuracy of land use classification of the RF algorithm in the complex environment of the Bay Area, and it can provide technical support for natural resource survey and regional sustainable development in the Bay Area.

Agricultural expansion into forest reserves in Zambia: a remote sensing approach

Forest reserves play an important role in the provision of ecosystem goods and services; however, these reserves are facing increasing anthropogenic impacts primarily because of agricultural activities. In sub-Saharan Africa, studies on understanding the extent and impacts of agricultural expansion into forest reserves are generally lacking. Thus, this study aimed to assess the extent of agricultural expansion into forest reserves in Zambia on a national scale between 2000 and 2018 and explores the drivers. We used a remote sensing approach by employing Google Earth Engine (GEE) and Random Forests classifier to map land cover changes at five-time steps: 2000, 2005, 2010, 2015 and 2018. Landsat images were used to map six land cover classes (forest, cropland, wetland, grassland, settlement, and water body) determined by the Intergovernmental Panel on Climate Change (IPCC). A social survey was also undertaken to understand the drivers of agricultural expansion into forest reserves at two study sited located in eastern and north-western part of Zambia. Based on the land cover maps which were produced with high accuracies ranging from 82% to 94%, the results indicated a general decline of 10% in forest area and an increase of 25% in cropland. Almost 50% of the forest reserves in Zambia are experiencing some form of encroachment, and 10% of these are heavily encroached (>90% loss). Local communities indicated that population growth, loss of soil fertility, high market demand for crops, land tenure system, and lack of law enforcement are the major drivers of agricultural expansion into forest reserves. This study highlights the need for policy marker to consider trade-offs in agricultural expansion and conservation of forest reserves relation to population growth. Enhancing sustainable agricultural practices and strengthening the implementation of different legislative frameworks is key in addressing the challenges of conservation, while enhancing food security.

Grassland cover declined in Southern Africa but increased in other African subcontinents in early twenty-first century

The African continent has the most extensive grassland cover in the world, providing valuable ecosystem services. African grasslands, like other continental grasslands, are prone to various anthropogenic disturbances and climate, and require data-driven monitoring for efficient functioning and service delivery. Yet, knowledge of how the African grassland cover has changed in the past years is lacking, especially at the subcontinent level, due to lack of relevant long-term, Africa-wide observations and experiments. In this study, we used Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type (MCD12Q1) data spanning 2001 to 2017 to conduct land use land cover (LULC) change analyses and map grassland distribution in Africa. Specifically, we assessed the changes in grassland cover across and within African subcontinents over three periods (2001-2013, 2013-2017, and 2001-2017). We found that the African grassland cover was 16,777,765.5 km2, 16,999,468.25 km2, and 16,968,304.25 km2 in 2001, 2013, and 2017, respectively. There were net gain (1.32%) and net loss (- 0.19%) during 2001-2013 and 2013-2017 periods, respectively, and the annual rate of change during these periods were 0.11% and - 0.05%, respectively. Generally, the African grassland cover increased by 1.14% (0.07% per annum) over the entire study period (2001-2017) at the expense of forestland, cropland, and built-up areas. The East and West African grassland cover reduced by 0.07% (- 0.02% per annum) and 1.35% (- 0.34% per annum), respectively from 2013 to 2017 but increased in other periods. On the other hand, the grassland cover in North and Central Africa increased throughout the three periods while that of Southern Africa decreased over the three periods. Overall, the net gains in the grassland cover of other African subcontinents offset the loss in Southern Africa and promoted the overall gain across Africa. This study underscores the need for continuous monitoring of African grasslands and the causes of their changes for efficient delivery of ecosystem services.

Implications of land use/land cover dynamics on urban water quality: Case of Addis Ababa city, Ethiopia

Water resources are often at the center of urban development but, as the city expands, the environmental pressure on its water resources increases. Therefore, in this study, we looked into how various land uses and changes in land cover affect the water quality in Addis Ababa, Ethiopia. Land use and land cover change maps were generated from 1991 to 2021 at intervals of five years. On the basis of the weighted arithmetic water quality index approach, the water quality for the same years was likewise divided into five classes. The relationship between land use/land cover dynamics and water quality was then evaluated using correlations, multiple linear regressions, and principal component analysis. According to the computed water quality index, the water quality decreased from 65.34 in 1991 to 246.76 in 2021. The built-up area showed an increase of over 338%, whereas the amount of water decreased by over 61%. While barren land exhibited a negative correlation with nitrates, ammonia loadings, total alkalinity, and total hardness of the water, agriculture and built-up areas positively correlated with water quality parameters such as nutrient loading, turbidity, total alkalinity, and total hardness. A principal component analysis revealed that built up areas and changes in vegetated areas have the biggest impact on water quality. These findings suggest that land use and land cover modifications are involved in the deterioration of water quality around the city. This study will offer information that might help reduce the dangers to aquatic life in urbanized environments.

Evaluation of perennial reference evapotranspiration (ETo) over a typical dryland using satellite images: A case study from Uzbekistan

Evapotranspiration (ET) is one of the most significant compartments in the energy and water balance between the atmosphere and the Earth's surface. Global climate change has an impact on the water cycle and can result in an increase in ET from the land surface; nevertheless, an increase in ET can destabilize the macro- and micro-climate at the local and continental levels. In line with this, the purpose of this article is to learn more about the factors that contribute to the high and current reference ET (ETo) rate of Karakalpakstan (Uzbekistan). MODIS Terra images was used to analyze long-term land cover change. The Google Earth Engine code editor, Erdas Imagine 2020, and ArcMap 10.8 were used to map land cover change and the leaf area index (LAI). Though the Normalized Difference Vegetation Index and the Soil-Adjusted Vegetation Index were used because of their past performance as affirmative measurement techniques for vegetation monitoring. The Hargreaves-Samani equation was utilized to ETo in this study. Increased crop density in agriculture and the conversion of barren soil to grasslands to rehabilitate the seashore ecosystem of the Aral Sea influenced LAI significantly, according to multifactorial analyses of perennial air temperature, land cover change, solar irradiance, and ETo in a typical dryland of Uzbekistan. However, the extension of LAI in the agricultural area of Karakalpakstan substantially accelerated the ETo rate, which was a key contributor of an increase in crop water demand. We can see that measured solar radiation and ETo were consistent across time by looking at the nearly same quantities for both. As a result, we assume that global climatic changes have no effect in the uncertainty of solar radiation and ET rate.