Land Use And Land Cover Types Research Articles

Characteristics and drivers of the soil multifunctionality under different land use and land cover types in the drylands of China

The drylands of China cover approximately 6.6×106 km2 and are home to approximately 5.8×108 people, providing important ecosystem services for human survival and development. However, dryland ecosystems are extremely fragile and sensitive to external environmental changes. Land use and land cover (LULC) changes significantly impact soil structure and function, thus affecting the soil multifunctionality (SMF). However, the effect of LULC changes on the SMF in the drylands of China has rarely been reported. In this study, we investigated the characteristics of the SMF changes based on soil data in the 1980s from the National Tibetan Plateau Data Center. We explored the drivers of the SMF changes under different LULC types (including forest, grassland, shrubland, and desert) and used structural equation modeling to explore the main driver of the SMF changes. The results showed that the SMF under the four LULC types decreased in the following descending order: forest, grassland, shrubland, and desert. The main driver of the SMF changes under different LULC types was mean annual temperature (MAT). In addition to MAT, pH in forest, soil moisture (SM) and soil biodiversity index in grassland, SM in shrubland, and aridity index in desert are crucial factors for the SMF changes. Therefore, the SMF in the drylands of China is regulated mainly by MAT and pH, and comprehensive assessments of the SMF in drylands need to be performed regarding LULC changes. The results are beneficial for evaluating the SMF among different LULC types and predicting the SMF under global climate change.

Patterns of change, driving forces and future simulation of LULC in the Fuxian Lake Basin based on the IM-RF-Markov-PLUS framework

Studying land use and land cover (LULC) patterns, identifying driving forces, and simulating future scenarios are vital for grasping the complex connection between human actions and the environment. This helps in shaping sustainable land management strategies and preparing for the impacts of climate change. However, there is a necessity for a comprehensive system modeling framework that can accurately capture spatial and temporal changes in LULC, analyze driving mechanisms, and provide an integrated analysis of future simulations. In this paper, a comprehensive IM-RF-Markov-PLUS analysis framework is developed, focusing on the Fuxian Lake Basin (FLB) as a study area. The study aims to achieve accurate prediction of LULC by combining the microscopic LULC change trend and the contribution of macroscopic driving forces. The results show that: (1) The IM-RF-Markov-PLUS framework can explore the change patterns and driving mechanisms of LULC in the FLB, and accurately predict the LULC in the FLB. Compared with the PLUS model, its accuracy is improved by 2 %. (2) IM analysis reveals that LULC transformation in the FLB is both general and specific. Although the area of grassland, buildings, roads and structures converted to desert and bare land is minimal, it shows relatively tendentious and specific change characteristics. (3) Different land types are significantly affected by driving factors, with the expansion of LULCs is constrained by major factors. The distance to the lake has the most significant impact on the distribution of garden land, while the primary road has the greatest impact on the distribution of forestland. (4) Under different scenarios, the spatial heterogeneity of LULC patterns is obvious. In 2035, under baseline and economic development scenarios, cultivated land will decrease, while other LULC types will increase. Under the cultivated land protection scenario, cultivated land is protected, with an increase of 5.85 %. Under the ecological protection scenario, there is an increase in ecological land. The largest increase is in forestland, which increases by a total of 3.46 %. The ecological protection scenario presents a viable approach for ensuring the sustainable development of the FLB. The results of this paper may serve as a reliable foundation for implementing LULC strategies in the FLB and offer guidance for crafting sustainable development regulations at the regional level.

Assessment of urbanization impacts on soil erosion and groundwater recharge in Enugu, Southeastern Nigeria

The study of Land Use and Land Cover (LULC) in Enugu, southeastern Nigeria, is crucial for understanding its effects on erosion and groundwater recharge. Different LULC types, coupled with varying slope gradients and soil types, create a complex interaction that influences soil stability and water infiltration in the region. This research aims to assess the distribution of LULC types, slope gradients, and soil types in Enugu, and to evaluate their impacts on erosion rates and groundwater recharge potential. Data for LULC, slope gradients, and soil types were collected and analyzed to determine their spatial distribution and characteristics. The study employed GIS techniques to map these variables and assess their interactions. Erosion potential and groundwater recharge capacity were evaluated based on the characteristics of the LULC types, slopes, and soils. The analysis revealed that trees cover the largest area (3709.74 km²), playing a significant role in stabilizing the soil and reducing erosion through their extensive root systems and canopy cover. Rangeland (2631.81 km²) also contributes to soil stability, although less effectively than forested areas. Crops (192.40 km²) have mixed impacts on erosion depending on agricultural practices. Built areas (1162.69 km²) present challenges due to impervious surfaces, which increase surface runoff and reduce groundwater recharge. Slope gradients were found to correlate with erosion processes and groundwater dynamics. Gentle slopes (0 - 1.81 degrees) cover 1870.15 km² and facilitate infiltration, enhancing groundwater recharge. Moderate slopes (1.81 - 4.06 degrees), covering 3275.46 km², are more prone to erosion, while steeper slopes (4.06 - 11.73 degrees) covering 2176.54 km² experience accelerated runoff and increased erosion rates. The steepest slopes (11.73 - 44.06 degrees) are the most erosion-prone areas, requiring significant intervention. The soil analysis showed that Dystric Nitosols (4052.80 km²) with the lowest K-factor (0.0178) are least prone to erosion and have high infiltration capacity, making them beneficial for groundwater recharge. Plinthic Acrisols (2732.53 km²) and Ferric Acrisols (102.36 km²) exhibit moderate erosion susceptibility. Dystric Fluvisols (793.92 km²) with the highest K-factor (0.0223) are highly erosion-prone. Gleysols (20.69 km²) have low to moderate erosion susceptibility. The interplay between LULC types, slope gradients, and soil types significantly influences erosion and groundwater recharge in Enugu. The study highlights the need for targeted land management practices, such as afforestation, contour farming, terracing, and the use of cover crops to mitigate erosion and enhance groundwater recharge. This research provides a comprehensive analysis of the relationships between LULC, slope gradients, and soil types in Enugu, offering valuable insights for developing effective land management strategies to address erosion and groundwater recharge challenges.

Modeling of land use and land cover changes using google earth engine and machine learning approach: implications for landscape management

A precise and up-to-date Land Use and Land Cover (LULC) valuation serves as the fundamental basis for efficient land management. Google Earth Engine (GEE), with its numerous machine learning algorithms, is now the most advanced open-source global platform for rapid and accurate LULC classification. Thus, this study explores the dynamics of the LULC changes between 1993 and 2023 using Landsat imagery and the machine learning algorithms in the Google Earth Engine (GEE) platform. Focus group discussion and key informant interviews were also used to get further data regarding LULC dynamics. Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Trees (CART) were demonstrated for LULC classification. Six LULC types (agricultural land, grazingland, shrubland, built-up area, forest and bareland) were identified and mapped for 1993, 2003, 2013, and 2023. The overall accuracy and kappa coefficient demonstrated that the RF using images comprising auxiliary variables (spectral indices and topographic data) performed better than SVM and CART. Despite being the most common type of LULC, agricultural land shows a trend of shrinking during the study period. The built-up area and bareland exhibits a trend of progressive expansion. The amount of forest and shrubland has decreased over the last 20 years, whereas grazinglands have exhibited expanding trends. Population growth, agricultural land expansion, fuelwood collection, charcoal production, built-up areas expansion, illegal settlement and intervention are among causes of LULC shifts. This study provides reliable information about the patterns of LULC in the Robit watershed, which can be used to develop frameworks for watershed management and sustainability.

Seasonal-scale intercomparison of SMAP and fused SMOS-SMAP soil moisture products

Two L-band passive microwave satellite sensors, onboard the Soil Moisture and Ocean Salinity (SMOS) launched in 2009 and Soil Moisture Active Passive (SMAP) launched in 2015, are specifically designed for surface soil moisture (SM) monitoring. The first global continuous fused L-band satellite SM product based on SMOS and SMAP observations (SMOS-SMAP-INRAE-BORDEAUX, the so-called Fused-IB) was recently released to the public. Currently, the performance of Fused-IB has only been evaluated collectively over the entire data records in the study period, without specific evaluation for individual seasons. To fill this gap, this study intercompared the Fused-IB and the enhanced SMAP-L3 version 6 (SMAP-E) SM products against in situ SM data from the International Soil Moisture Network (ISMN) from 2016 to 2020 regarding the whole period and different seasons. We aim to investigate the performance of these two products at different time scales and to explore the potential eco-hydrological factors (i.e., precipitation and vegetation) driving their seasonal variations. Results show that both SM products are in good agreement with the in situ measurements, demonstrating high median correlation (R) and low ubRMSD (median R = 0.70 and ubRMSD = 0.058 m3/m3 for Fused-IB vs. R = 0.68 and ubRMSD = 0.059 m3/m3 for SMAP-E) during 2016–2020. For most land use and land cover (LULC) types, Fused-IB outperformed SMAP-E with higher accuracy and lower errors, particularly in forests, partly due to the advantage of the robust SMAP-IB (SMAP-INRAE-BORDEAUX) algorithm used to generate Fused-IB in forests, which avoids the pronounced saturation effects of vegetation optical depth caused by relying on optical information. Besides, both products had superior performances across most LULC types in summer (JJA) and autumn (SON), yet increased uncertainties were observed in forests, grasslands, and croplands during spring (MAM) and winter (DJF). These uncertainties could be mainly attributed to the effects of vegetation growth in forests, grasslands and croplands, and the interception of water from rainfall events in grasslands. The results of this study can serve as a reference for algorithm developers to enhance the accuracy of SM and thus promote hydro-meteorological applications that benefit from L-band radiometer soil moisture products.

Changing Pattern and Drivers of Land Use and Land Cover in Bagmati Province of Nepal

Land use and land cover (LULC) represents the physical component of the Earth's environment, and often changes as a result of natural and anthropogenic activities. This study carried out changing patterns and orientations of LULC types of Bagmati province of Nepal by using satellite images between 1996 and 2016. The study applied a classification scheme through support vector machines (SVM) for the period from 1996 to 2006, and 2006 to 2016. Our study explored that built-up area expansion was the major change witnessed which increased from 85.25 km2 in 1996 and sharply increased and labeled 250.06 km2 in 2016. Similarly, the forest land of the province increased by 329.02 km2 between 1996 and 2016. The status of cultivated land was found slightly decreasing trend, which was noted at 6113.50 km2 in 1996 and labeled at 5725.82 km2 in 2016. The shrubland of the province declined by 272.03 km2 from 1851.67 km2 in 1996 to 1579.64 km2 in 2016. However, the status of snow/ ice cover found at fluctuated form, which was noted 774.27 km2 in 1996, 1297.43 km2 in 2006, and 828.92 km2 in 2016. The increasing population trend is the main driver of the high rate of built-up area expansion and contraction of the cultivated land. Community forestry programs highly supported the expansion of forest cover. The proper implementation and improvement of the existing policy are the major concerns for sustainable LULC management in the province.

Assessment of land use and land cover change detection and prediction using deep learning techniques for the southwestern coastal region, Goa, India.

Understanding the connections between human activities and the natural environment depends heavily on information about land use and land cover (LULC) in the form of accurate LULC maps. Environmental monitoring using deep learning (DL) is rapidly growing to preserve a sustainable environment in the long term. For establishing effective policies, regulations, and implementation, DL can be a valuable tool for assessing environmental conditions and natural resources that will positively impact the ecosystem. This paper presents the assessment of land use and land cover change detection (LULCCD) and prediction using DL techniques for the southwestern coastal region, Goa, also known as the tourist destination of India. It consists of three components: (i) change detection (CD), (ii) quantification of LULC changes, and (iii) prediction. A new CD assessment framework, Spatio-Temporal Encoder-Decoder Self Attention Network (STEDSAN), is proposed for the LULCCD process. A dual branch encoder-decoder network is constructed using strided convolution with downsampling for the encoder and transpose convolution with upsampling for the decoder to assess the bitemporal images spatially. The self-attention (SA) mechanism captures the complex global spatial-temporal (ST) interactions between individual pixels over space-time to produce more distinct features. Each branch accepts the LULC map of 2 years as one of its inputs to determine binary and multiclass changes among the bitemporal images. The STEDSAN model determines the patterns, trends, and conversion from one LULC type to another for the assessment period from 2005 to 2018. The binary change maps were also compared with the existing state of the art (SOTA) CD methods, with STEDSAN having an overall accuracy of 94.93%. The prediction was made using an recurrent neural network (RNN) known as long short term memory network (LSTM) for the year 2025. Experiments were conducted to determine area-wise changes in several LULC classes, such as built-up (BU), crops (kharif crop (KC), rabi crop (RC), zaid crop (ZC), double/triple (D/T C)), current fallow (CF), plantation (PL), forests (evergreen forest (EF), deciduous forest (DF), degraded/scurb forest (D/SF) ), littoral swamp (LS), grassland (GL), wasteland (WL), waterbodies max (Wmx), and waterbodies min (Wmn). As per the analysis, over the period of 13 years, there has been a net increase in the amount of BU (1.25%), RC (1.17%), and D/TC( 2.42%) and a net decrease in DF (3.29%) and WL(1.44%) being the most dominant classes being changed. These findings will offer a thorough description of identifying trends in coastal areas that may incorporate methodological hints for future studies. This study will also promote handling the spatial and temporal complexity of remotely sensed data employed in categorizing the coastal LULC of a heterogeneous landscape.

Change in Bhutan’s Land Use and Land Cover from 1930 - 2020

The land is a fundamental component of Bhutan's geographical makeup, characterized by natural features and human activities. Bhutan's land can be divided into land use and land cover (LULC), where human activities contribute to land use, and natural elements define land cover. This paper analyzes Bhutan's LULC patterns, emphasizing the evolving trends in major LULC types, strengths and challenges of sustainable land use management, and envisioning its future trajectory. In recent years, Bhutan's land-use pattern has displayed a predominant forest cover, encompassing a significant portion of the total land area. Similarly, the built-up area in Bhutan is gradually increasing, reflecting the country's ongoing urbanization and development activities. A long-term analysis reveals dynamic shifts in agricultural land in Bhutan. While there has been a historical expansion of agricultural areas, recent trends may suggest a potential slowdown or decline, influenced by factors such as urban growth and changing economic landscapes. Like community forestry practices in Bhutan, successful conservation efforts may also influence land-use changes. In the eastern Himalayas, Bhutan faces challenges related to snow/glacial cover impacted by climate change. Increasing temperatures in the region contribute to alterations in snow/glacial patterns, necessitating a focus on environmental conservation and sustainable practices to preserve these vital resources. The land tenure system and land use policies in Bhutan have evolved and been shaped by socioeconomic and political dynamics. Bhutan needs to adapt and formulate effective policies to address contemporary challenges and promote sustainable LULC management. Implementing specific LULC zones, as outlined in Bhutan's land use policies, is crucial for ensuring sustainable land management practices.

Identifying land use land cover dynamics using machine learning method and GIS approach in Karaivetti, Tamil Nadu

<p>An important analytical tool for tracking, mapping, and quantifying changes in land use and land cover (LULC) across time serves as the use of machine learning techniques. The environment and human activities both have the potential to change how land is used and covered. Classifying LULC types at different spatial scales has been effectively achieved by models like classification and regression trees (CART), support vector machines (SVM), extreme gradient boosting (XGBoost), and random forests (RF). To prepare images from Landsat before sending and analysis for an aspect of our research, we employed the Google Earth Engine. High-resolution imagery from Google Earth images were used to assess each kind of method and field data collection. Utilizing Geographic Information System (GIS) techniques, LULC fluctuations between 2015 and 2020 were assessed. According to our results, XGBoost, SVM, and CART models proved superior by the RF model regarding categorization precision. Considering the data, we collected between 2015 and 2020, from 11.57 hectares (1.74%) in 2015 to 184.19 hectares (27.65%) in 2020, the barren land experienced the greatest variation, that made an immense effect. Utilizing the support of satellite imagery from the Karaivetti Wetland, our work combines novel GIS techniques and machine learning strategies to LULC monitoring. The created land cover maps provide a vital benchmark that will be useful to authorities in formulating policies, managing for sustainability, and keeping track of degradation.</p>

Sustainable flood hazard mapping with GLOF: A Google Earth Engine approach

This study aims to evaluate the efficacy of Google Earth Engine (GEE) in mapping floods and their aftermath, focusing on the recent event caused by cloud burst rainfall and glacial lake outburst flood (GLOF) of Lhonak glacier lake in the Teesta River basin, North Sikkim. The objective is to utilize GEE, coupled with Sentinel-1 Synthetic Aperture Radar (SAR) data and Landsat 9 imagery, for precise remote sensing analysis, flood mapping, and Land Use and Land Cover (LULC) classification. The study employs a comprehensive methodology within the GEE platform, involving the acquisition and preprocessing of Sentinel-1 SAR data to create pre- and post-flood images. The difference between these images is calculated to generate flood maps at five-day intervals, providing a temporal evolution of the flood extent. Additionally, LULC mapping is conducted using Landsat 9 data, contributing to an understanding of pre-flood landscape characteristics. The results and discussion reveal significant impacts on various LULC types, with barren rocks, dense and medium forests, settlements, and agricultural lands experiencing notable effects. This research not only enhances our understanding of GLOFs but also serves as a critical tool for informing disaster management strategies, emphasizing the importance of accurate hazard assessment and the need for holistic approaches to mitigate the cascading effects of such events.

Transfer-Ensemble Learning: A Novel Approach for Mapping Urban Land Use/Cover of the Indian Metropolitans

Land use and land cover (LULC) classification plays a significant role in the analysis of climate change, evidence-based policies, and urban and regional planning. For example, updated and detailed information on land use in urban areas is highly needed to monitor and evaluate urban development plans. Machine learning (ML) algorithms, and particularly ensemble ML models support transferability and efficiency in mapping land uses. Generalization, model consistency, and efficiency are essential requirements for implementing such algorithms. The transfer-ensemble learning approach is increasingly used due to its efficiency. However, it is rarely investigated for mapping complex urban LULC in Global South cities, such as India. The main objective of this study is to assess the performance of machine and ensemble-transfer learning algorithms to map the LULC of two metropolitan cities of India using Landsat 5 TM, 2011, and DMSP-OLS nightlight, 2013. This study used classical ML algorithms, such as Support Vector Machine-Radial Basis Function (SVM-RBF), SVM-Linear, and Random Forest (RF). A total of 480 samples were collected to classify six LULC types. The samples were split into training and validation sets with a 65:35 ratio for the training, parameter tuning, and validation of the ML algorithms. The result shows that RF has the highest accuracy (94.43%) of individual models, as compared to SVM-RBF (85.07%) and SVM-Linear (91.99%). Overall, the ensemble model-4 produces the highest accuracy (94.84%) compared to other ensemble models for the Kolkata metropolitan area. In transfer learning, the pre-trained ensemble model-4 achieved the highest accuracy (80.75%) compared to other pre-trained ensemble models for Delhi. This study provides innovative guidelines for selecting a robust ML algorithm to map urban LULC at the metropolitan scale to support urban sustainability.

Temporal and spatial variations of carbon storage and carbon sink improvement strategy at the district and county level based on PLUS-InVEST model: Taking Yanqing District as an example

Under the background of the carbon peaking and carbon neutrality goals, the evolution of the spatiotemporal pattern of carbon storage has recently emerged as a research hotspot. The change in land use and land cover (LULC) is the primary driver of carbon storage changes. Understanding the spatiotemporal variations of LULC and carbon storage at the small scale of district and county level and proposing strategies to improve carbon sink, will contribute to the ecological conservation, restoration and sustainable development of districts or counties. With Yanqing District in Beijing as an example, we calculated carbon storage from 1990 to 2020 based on the InVEST model and used the PLUS model to predict LULC type changes under three scenarios (natural growth, ecological conservation and economic development) from 2020 to 2050. We further predicted the carbon storage and proposed mea-sures to improve carbon sink. The results showed that the key LULC change in Yanqing between 1990 and 2020 were the conversion of 88.9% of grassland to forest, 50.1% of farmland to forest, and 39.5% of cropland to impervious surface. The total carbon storage showed an upward trend, with an increase of 3.34×106 Mg. The spatial distribution of carbon storage presented "high in the northeast, low in the southwest, and high in the mountainous areas, low in the riverine areas." The increase in forest and the decrease in grassland were the main reasons for the increase and decrease in carbon storage, respectively. Between 2020 and 2050, the ecological restoration efforts under the ecological protection scenario increased, and the probability of other LULCs transforming into forest increased, resulting in a 5.8% increase in carbon storage, which had the highest increase and carbon storage under the three scenarios. High-value carbon storage areas were concentrated in the northeast, northwest, and south of Yanqing District, basically corresponding to the mountainous regions of Yanqing with high forest coverage, and the low-value areas generally corresponded to the plains with high development intensity and low forest coverage. We could implement comprehensive ecological protection and restoration measures, including forest and grassland ecosystem protection, water environment ecological restoration, farmland ecological restoration, to promote sustainable development in Yanqing District and to achieve the "dual carbon" goal.

Impact of land use changes on the land surface thermal environment in Nanchang, Jiangxi province, China

Throughout human history, human activities have resulted in land use and land cover changes (LUCC) and can have a direct impact on the land surface thermal environment (LSTE). In the existing studies, the holistic nature of changes in land use and land cover (LULC) has been neglected in favor of focusing on the interactions between different LULC types and the land surface thermal environment. This study used ArcGIS Pro 3.0, ENVI 5.3, and SPSS software to construct a contribution index model and stepwise regression equation to investigate the relationship between LULC type, structure, and pattern changes and LSTE changes in Nanchang City, Jiangxi Province, China, from 1990 to 2020. The findings revealed that 1) rapid urbanization has led to a surge in the area of built-up land and a decrease in the area of arable land in Nanchang; between 1990 and 2020, the area of built-up land in Nanchang increased by 433.29 km2, while the area of arable land decreased by 291.99 km2; 2) The land surface temperature (LST) was divided into five classes according to the equal spacing method, and the areas with the highest and lowest temperature classes were the high temperature zone and the low temperature zone, respectively. Over the past 30 years, the LSTE in Nanchang has gradually deteriorated, with the area of the low temperature zone shrinking by 554.2 km2 and the area of other classes appearing to increase significantly; 3) the contribution index and stepwise regression equation demonstrate that the primary reasons for the worsening of the LSTE are an increase in the scale of cultivated land and construction land. It was found that rationalization of urban LULC type, structure, and pattern can effectively reduce land surface temperature.

GIS based method for mapping actual LULC by combining seasonal LULCs

One of the most significant applications of remote sensing data is to prepare land use and land cover (LULC) maps. LULC maps are always affected by seasonality and a single LULC map of a particular month is prepared to represent a year in most of the research, especially in change detection research. This does not represent the real view of the landscape because the seasonal variation of different LULC types is always overlooked. Considering the issue, the current method aims to solve the problem by incorporating seasonal LULC using the raster overlay method to remove the seasonality effect on LULC classification. To apply this method, a minimum of two seasonal LULC maps is required for a single study year. The map needs to overlay and then reclassify according to the stable and rotational LULC pattern of the study area. This method will replicate the actual LULC pattern of a study area from satellite images. Summary of the method is as follows:•LULC of each season was classified using image classification technique.•LULC of each seasons are coded and combined using overlay technique.•Combined map is reclassified to prepare the actual LULC map.

Evaluating the impact of land use and land cover changes on forest ecosystem service values using landsat dataset in the Atwima Nwabiagya North, Ghana

This study investigated land use and land cover (LULC) changes and its impact on forest ecosystem service values for 20 years in the Atwima Nwabaiagya North District using Landsat images of 2002, 2012 and 2022. Supervised classification with Maximum Likelihood Algorithm was used to classify the Landsat images. Five LULC types (high-dense forest, low-dense forest, water, bare-ground, and Built-up area) were successfully classified, with overall accuracies of 99.0 % and Kappa coefficients of 0.99. The result of the study showed a reduction of high-dense forest to 23.87 %, low-dense forest to 26.53 %, and water areas as 1.16 % whereas built-up (21.44 %) and bare-ground (27 %) experienced an expansion in their land areas. Related literatures and ecological assets value table with adjusted price value were used to evaluate ecosystem service values in response to LULC changes. The study discovered that ecosystem service value for high and low-dense forests have declined from USD 22.68 million and USD 8.75 million to USD 14.56 million and USD 5.2 million respectively. The overall total ecosystem service value declined by USD 33.73 million in 2002 to USD 21.91 million in 2022. It was revealed that the most notable feature to changes in forest ecosystem service values was the expansion of built-up and bare-grounds. There is a need to curb the current drivers of LULC changes in the Atwima Nwabiagya North to stop further forest degradation for optimum delivery of forest ecosystem service values in the district. For land use planners and decision makers who need site-specific information on the effects of LULC alterations on values of forest ecosystem services, the study's findings are essential. This will make it easier to track past environmental changes and obtain quick, accurate results for use in decision-making.

Land use and land cover changes and their impact on ecosystem service values in the north-eastern highlands of Ethiopia.

The land use and land cover (LULC) changes driven by the growing demands of mankind have a considerable effect on ecosystem services and functions. The study was carried out in the north-eastern highlands of Ethiopia to (1) analyze the effect of LULC changes between 1984 and 2021 and (2) assess the spatiotemporal variations in ecosystem service values (ESVs) and elasticity in response to LULC changes. Using Landsat imageries from 1984 to 2021, the spatiotemporal changes in LULC were evaluated with supervised image classification using maximum likelihood algorithm in ArcGIS software. Six LULC types were subsequently categorized, with overall accuracy and Kappa coefficients above 87% and 0.87, respectively. The ESVs were then estimated based on the Benefit Value Transfer (BVT) approach employing modified conservative value coefficients. The findings revealed a significant increase in cultivated land (9759.1ha) and built-up area (10174.41ha) during the stipulated periods and a drop in other land use types. The forest loss gradually decreased from 4.1% in the second period (1991-2001) to 0.58% in the third (2001-2021), compared to the first of the 1.1% conversion rates. Similarly, the proportion of grassland and water bodies steadily reduced over the stipulated periods, by 1.15% and 2.3% per annum, respectively. The overall loss of ESVs in the study landscape was estimated to be 54.4 million US$ (67.3%), drastically decreasing from 80.3 million US$ in 1984 to 26.4 million US$ in 2021, driven by the declining area coverage of water bodies, grassland, and forestland. Regardless of the loss, the ecosystem functions of hydrological regulation (37.2, 35.0, 6.1, and 5.1 US$ ha-1yr-1), water supply (14.5, 13.6, 2.4, and 2 US$ ha-1yr-1), and food production (9.8, 10.0, 9.1, and 9.9 US$ ha-1yr-1) contributed the most to the total ESV of each year while disturbance regulation and cultural values were the least throughout the study periods. The coefficient of sensitivity (CS) analysis revealed that our estimates were relatively robust. The findings further showed that human-dominated land-uses at the expense of natural ecosystems are the primary drivers of LULC transitions and the ensuing loss of ecosystem services in the region. Thus, this calls for intensive work on more effective land use policies that encourage an integrated management approach, with a focus on safeguarding the sustainability of natural ecosystems.

Impact of land use and land cover change on land surface temperature over Lake Tana Basin

Isolation of climate and Land Use and Land Cover (LULC) changes and their compounded effect is complicated since the two influence each other through forcing-feedback cycles. This study investigates the changes in LULC and its contribution to the change in surface and air temperature in the Lake Tana Basin using data obtained from MODIS remote sensing satellites, ground observation and reanalysis model. Observation minus reanalysis (OMR) method and various analysis tools are used to estimate the impact of LULC changes on temperature. The analysis of LULC change indicates that an increase of Savanna and other type of forest cover by about 519 km2 from year 2001 to year 2020. Wetland increased by about 83.7 km2 during the said period. During the period 2001–2020, about 56.5% baren-land areas are being converted into wetland. The conversion of water bodies and baren land to wetland is likely due to the increase of water hyacinth on both the water body and nearby baren-land whereas the increase in Savanna is linked to an on-going eucalyptus tree plantation. The negative correlation between LST and NDVI (reaching a value of −0.60) is noticed over the Basin throughout the year on inter-annual time scale. An increase in vegetation cover such as Savanna and broad leaf forest coincides with the decreasing LST with an average rate of 0.08%/decade. Also, a reduction in temperature, derived from OMR, at an average cooling rate of about 0.87%/decade is observed in the study period. However, an increase in the LST change rate over built-up and grassland is about 0.03%/decade. Therefore, we conclude that the consistent associations of the increase in areal coverage of the some LULC types with lowest LSTs, 2m-temperature and temperature trend derived from NDVI-LST and LULC-LST associations as well as OMR temperature and LST are unambiguous signatures of the effect of LULC change on temperature over Lake Tana Basin, distinct from that arising due to climate change.

Impacts of Land Use and Land Cover Change on Vegetation Diversity of Tropical Highland in Ethiopia

Ethiopia has undergone a substantial shift in land use and land cover (LULC), which is home to the majority of the human and animal population. Land degradation has occurred in the Ethiopian highlands as a result of modifications in LULC caused by poor farming methods, high livestock population, and human pressures. Most researchers identified the many LULC drivers and their impact on floristic composition. All of these manifestations have the potential to have major consequences for land users and individuals whose livelihoods rely on the products of a healthy environment. This change in LULC type, combined with poor land management practices in Ethiopia, puts land in jeopardy of erosion, resulting in accelerated soil degradation. All LULC variables, such as the spread of various agricultural activities, the production of fuelwood and charcoal, cutting trees for construction resources, settlements, and revenue growth, are associated with population increase and resettlement. In Ethiopia, the lack of a relevant forest policy implementation on the ground is recognized as a cause pushing deforestation and other landscape changes. This review paper aimed to compile the effects of land use land cover changes on Ethiopian vegetation.

Assessment and prediction of carbon storage based on land use/land cover dynamics in the coastal area of Shandong Province

Changes in land use and land cover (LULC) promote regional carbon storage capacity or trigger carbon depletion, which in turn exhibited significant impact on global climate change. Understanding the impacts of LULC on changes of carbon storage in coastal areas plays a critical role in the conservation of regional ecosystems and sustainable socio-economic development. The present study acted the coastal area of Shandong Province as an example to analyze the relationship between LULC and carbon storage combined with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) and Patch-generating Land Use Simulation (PLUS) model. We further predicted the variation of carbon storage through the change of LULC types under three scenarios in 2030. Our results showed that cropland (which decreased by 9.41%) and built-up land (which increased by 7.66%) underwent the most significant changes over the past 20 years, while forest, grassland, wetland, water and bare land underwent less changes. As the dominant land type, cropland was also the most important carbon pool with medium carbon storage. Areas with high carbon storage were distributed in the mountains and hills, where the main land types were grassland and forest. In addition, wetland located in the Yellow River Delta also stores large amounts of carbon. Accordingly, areas with low carbon storage were widely distributed in built-up land of urban metropolitan regions. We pinpointed that the carbon storage in the coastal area of Shandong Province lost 47.96×106Mg due to the increasing of built-up land and the decreasing of cropland and forest, while ecological protection measures would effectively enhance regional carbon storage. Specifically, the regional carbon storage could be increased by 6.64×106Mg when the conversion of cropland, forest and grassland into built-up land was reduced by 20% and the conversion of wetland and water into built-up land was reduced by 30% (under the ecological priority scenario (EP)). We believe the present study could be a valid reference for administrators to develop policies in more reasonable planning of land use and urban development to achieve carbon peaking and carbon neutrality (“Dual Carbon” goals).

Drought-induced vulnerability and resilience of different land use types using time series of MODIS-based indices

Drought is a creeping and complex phenomenon that significantly impacts terrestrial ecosystem services by reducing the ecosystem's performance and upsetting the balance of the terrestrial carbon cycle. This study used Google Earth Engine (GEE) to quantify the effects of drought on the vulnerability and resilience of land use and land cover (LULC) types in the Karkheh basin, Iran's food basket and the third-largest basin in southwest Iran. Using MODIS data, we first extracted the vegetation health index (VHI) for drought monitoring during 2000–2020, and as a result, 2008 was designated as the drought year. Then, water use efficiency (WUE) was calculated for all LULC types during drought. In general, all LULC types experienced an increase in WUE, with the highest increase seen in the forest , which indicates the forest's high resistance to drought. To evaluate the ecosystems' resilience to drought, we computed the drought recovery period using the gross primary production (GPP). The results showed that recovery times varied among different LULC types, with the forest recovering in the shortest time, further indicating a higher resilience and greater importance for the absorption and stabilization of terrestrial carbon. Conversely, rainfed farming had the longest recovery time, indicating a higher vulnerability and, consequently, a greater need for the adoption of appropriate adaptive measures. The results of this study provide a better understanding of the response of different LULC types and their resilience to drought and can assist ecosystem conservation and LULC management.