Land Use And Land Cover Research Articles

Spatial Distribution of Burned Areas from 1986 to 2023 Using Cloud Computing: A Case Study in Amazonas (Peru)

Wildfire represents a significant threat to ecosystems and communities in the Department of Amazonas, Peru, causing losses in biodiversity and land degradation and affecting socioeconomic security. The objective of this study was to analyze the spatial and temporal distribution of burned areas (BAs) from 1986 to 2023 to identify recurrence patterns and their impact on different types of land use and land cover (LULC). Landsat 5, 7, and 8 satellite images, processed by Google Earth Engine (GEE) using a decision tree approach, were used to map and quantify the affected areas. The results showed that the BAs were mainly concentrated in the provinces of Utcubamba, Luya, and Rodríguez de Mendoza, with a total of 1208.85 km2 burned in 38 years. The most affected land covers were pasture/grassland (38.25%), natural cover (forest, dry forest, and shrubland) (29.55%) and agricultural areas (14.74%). Fires were most frequent between June and November, with the highest peaks in September and August. This study provides crucial evidence for the implementation of sustainable management strategies, fire prevention, and restoration of degraded areas, contributing to the protection and resilience of Amazonian ecosystems against future wildfire threats.

Land-Use Impacts on Soil Erosion: Geochemical Insights from an Urban Drinking Catchment, South-Central Chile

We investigate the influence of land use and land cover (LU/LC) changes on soil erosion and chemical weathering processes within the Nonguén watershed in the Coastal Cordillera of south-central Chile. The watershed is divided into three sub-basins, each characterized by distinct LU/LC patterns: native forest and exotic plantations. A comprehensive geochemical analysis, including trace elements and lithium (Li) isotopes, was conducted on river water and suspended sediment samples collected from streams within these sub-basins to assess how land management practices, particularly plantation activities, influence the geochemical composition of river systems. Our results show that sub-basins dominated by exotic plantations exhibit significantly higher concentrations of major and trace elements in suspended sediments compared to sub-basins dominated by native forests. The elevated trace element concentrations are primarily attributed to increased physical erosion due to forestry activities such as clear-cutting and soil disturbance, which enhance sediment mobilization. Notably, concentrations of elements such as Fe, Al, and As in plantation-dominated sub-basins are raised to ten times higher than in native-dominated sub-basins. In contrast, sub-basins with native forest cover exhibit lower levels of sediment transport and trace element mobilization, suggesting that native vegetation exerts a stabilizing effect that mitigates soil erosion. Despite the substantial differences in sediment transport and element concentrations, Li isotopic data (δ7Li) show minimal fractionation across the different LU/LC types. This indicates that land use changes impact the chemical weathering processes less compared to physical erosion. The isotopic signatures suggest that physical erosion, rather than chemical weathering, is the dominant process influencing trace element distribution in plantation-dominated areas. The study provides critical insights into how forestry practices, specifically the expansion of exotic plantations, accelerate soil degradation and affect the geochemical composition of river systems. The increased sediment loads, and trace element concentrations observed in plantation-dominated sub-basins, raise concerns about the long-term sustainability of forest management practices, particularly regarding their impacts on water quality in urban catchment areas. These results are of significant relevance for environmental management and policy, as they underscore the need for more investigation and sustainable land use strategies to minimize soil erosion and preserve water resources in regions undergoing rapid LU/LC changes.

Enhancing Cover Management Factor Classification Through Imbalanced Data Resolution

This study addresses the persistent challenge of class imbalance in land use and land cover (LULC) classification within the Shihmen Reservoir watershed in Taiwan, where LULC is used to map the Cover Management factor (C-factor). The dominance of forests in the LULC categories leads to an imbalanced dataset, resulting in poor prediction performance for minority classes when using machine learning techniques. To overcome this limitation, we applied the Synthetic Minority Over-sampling Technique (SMOTE) and the 90-model SMOTE-variants package in Python to balance the dataset. Due to the multi-class nature of the data and memory constraints, 42 models were successfully used to create a balanced dataset, which was then integrated with a Random Forest algorithm for C-factor classification. The results show a marked improvement in model accuracy across most SMOTE variants, with the Selected Synthetic Minority Over-sampling Technique (Selected_SMOTE) emerging as the best-performing method, achieving an overall accuracy of 0.9524 and a sensitivity of 0.6892. Importantly, the previously observed issue of poor minority class prediction was resolved using the balanced dataset. This study provides a robust solution to the class imbalance issue in C-factor classification, demonstrating the effectiveness of SMOTE variants and the Random Forest algorithm in improving model performance and addressing imbalanced class distributions. The success of Selected_SMOTE underscores the potential of balanced datasets in enhancing machine learning outcomes, particularly in datasets dominated by a majority class. Additionally, by addressing imbalance in LULC classification, this research contributes to Sustainable Development Goal 15, which focuses on the protection, restoration, and sustainable use of terrestrial ecosystems.

Growth Simulation of Agriculturally Dominant Cities by Incorporating Multiple Drivers into a CA-based patch-generating Land Use Simulation Model: A Case Study in Siracusa, Italy

Abstract. Understanding the historical trajectory of land use and land cover (LULC) and predicting future alterations is essential to advancing the SDGs. Current researches on LULC in agriculture-dominated cities primarily focuses on the spatiotemporal pattern analysis of carbon storage changes, with relatively little attention given to LULC simulation studies. Moreover, most studies employing cellular automata (CA) models concentrate on urban centers, overlooking the specific sustainable development of agricultural cities. This paper selects Siracusa, Italy, as a case study, incorporating multiple drivers and constructing four development scenarios. The patch-generating land use simulation (PLUS) model is employed to predict the LULC for Siracusa by 2030 and to examine potential transformations under each scenario. Our findings show that 60% of the land in Siracusa is designated as cropland, predominantly situated in flat regions. Our simulation results reveal that urbanization and demographic growth are the main factors driving cropland conversion to urban areas. Different development scenarios exhibit significant variations in future land use structures; the cropland protection scenario emphasizes the stability and sustainability of agricultural development, whereas the economic development scenario highlights the substantial impact of urban and industrial expansion on agricultural land. These insights are instrumental for land use planning and policy-making in Siracusa and other agriculture-centric cities, providing a framework to guide urbanization while promoting agricultural sustainability.

Assessing Chile's national expansion of urban and artificial coverages between 2000 and 2022

Abstract. This study investigates the national expansion of urban and artificial coverages in Chile between 2000 and 2022 using remote sensing techniques. The research employed Google Earth Engine and Landsat imagery to classify land use and land cover (LULC) changes. By analyzing annual cloud-free composites and employing random forest algorithms, the study produced detailed LULC maps, focusing on the 'infrastructure' class, which includes urban areas and major roads. Results indicate a significant 33% increase in infrastructure coverage, growing from approximately 380,000 hectares in 2000 to 510,000 hectares in 2022. This expansion was predominantly observed in seven regions, which accounted for 68% of the total infrastructure area and 83% of the national increase. A notable correlation (0.6) between urban growth and population growth was identified, suggesting that urban expansion is largely driven by demographic changes. However, regions with higher population growth exhibited more vertical urban development. The study highlights the complex interplay between urbanization, population dynamics, and regional planning, emphasizing the need for sustainable development strategies to manage urban growth effectively. Validation efforts showed high classification accuracies, with infrastructure class accuracies averaging 82% for the User and 87% for the Producer across the evaluated years.

Thermal Footprint of the Urbanization Process: Analyzing the Heat Effects of the Urbanization Index (UI) on the Local Climate Zone (LCZ) and Land Surface Temperature (LST) over Two Decades in Seville

Urbanization is a multifaceted process characterized by changes in urban areas through various means, such as sprawl, ribbon development, or infill and compact growth. This phenomenon changes the pattern of the local climate zone (LCZ) and significantly affects the climate, vegetation dynamics, energy consumption, water resources, and public health. This study aims to discern the impacts of changes in urban growth on the LCZ and land surface temperature (LST) over a two-decade period. A comprehensive methodology that integrates statistical analysis, data visualization, machine learning, and advanced techniques, such as remote sensing technology and geospatial analysis systems, is employed. ENVI, GEE, and GIS tools are utilized to collect, process, and monitor satellite data and imagery of temporal and spatial variations in intensive or diffuse urbanization processes from 2003 to 2023 to analyze and simulate land use and land cover (LULC) changes, urbanization index (UI), LCZ patterns, and LST changes over the years and to make overlapping maps of changes to recognize the relation between LULC, LCZ, and LST. This study focuses on Seville’s urban area, which has experienced rapid urbanization and a significant increase in average temperature during the last few decades. The findings of this study will provide actionable recommendations into the interplay between urban growth and climate and highlight the pivotal role of urban growth in shaping resilience and vulnerable areas based on microclimate changes. Urban planners can leverage these insights to predict alternatives for the future development of urban areas and define practical climate mitigation strategies.

The Implications of Plantation Forest-Driven Land Use/Land Cover Changes for Ecosystem Service Values in the Northwestern Highlands of Ethiopia

In the northwestern Highlands of Ethiopia, a region characterized by diverse ecosystems, significant land use and land cover (LULC) changes have occurred due to a combination of environmental fragility and human pressures. The implications of these changes for ecosystem service values remain underexplored. This study quantifies the impact of LULC changes, with an emphasis on the expansion of plantation forests, on ecosystem service values in monetary terms to promote sustainable land management practices. Using Landsat images and the Random Forest algorithm in R, LULC patterns from 1985 to 2020 were analyzed, with the ecosystem service values estimated using locally adapted coefficients. The Random Forest classification demonstrated a high accuracy, with values of 0.97, 0.98, 0.96, and 0.97 for the LULC maps of 1985, 2000, 2015, and 2020, respectively. Croplands consistently dominated the landscape, accounting for 53.66% of the area in 1985, peaking at 67.35% in 2000, and then declining to 52.86% by 2020. Grasslands, initially the second-largest category, significantly decreased, while wetlands diminished from 14.38% in 1985 to 1.87% by 2020. Conversely, plantation forests, particularly Acacia decurrens, expanded from 0.4% of the area in 2000 to 28.13% by 2020, becoming the second-largest land cover type. The total ecosystem service value in the district declined from USD 219.52 million in 1985 to USD 39.23 million in 2020, primarily due to wetland degradation. However, plantation forests contributed USD 17.37 million in 2020, highlighting their significant role in restoring ecosystem services, particularly in erosion control, soil formation, nutrient recycling, climate regulation, and habitat provision. This study underscores the need for sustainable land management practices, including wetland restoration and sustainable plantation forestry, to enhance ecosystem services and ensure long-term ecological and economic sustainability.

Modeling future projections of land use and land cover in the Kakataibo territory

Abstract. The Amazon region is undergoing a dynamic and extensive transformation from forested areas to human-altered landscapes, significantly impacting forests and indigenous territories. This research aims to create future land use and land cover (LULC) scenarios by examining past trends (2014–2022) and predicting future patterns for 2030, focusing on the Kakataibo territory in central Peru. Using the Dinamica EGO modelling platform, we projected LULC changes based on historical data and various predictive factors. Our findings reveal notable forest loss between 2014 and 2022, with this trend likely continuing through 2030. Within indigenous territories, deforestation was less severe, with a reduction of 30,247.4 hectares compared to the more significant loss in non-indigenous areas. This underscores the vital role these territories play in mitigating forest loss. However, the rapid expansion of agriculture and infrastructure within these regions signals an urgent need for stronger legal protections to halt further deforestation. This study highlights the critical importance of indigenous territories in forest conservation and offers valuable insights for future land management and conservation strategies in the Amazon.

Spatiotemporal dynamics of LULC change and implications for sustainable urban growth in Saudi Arabia

This study investigates the spatiotemporal dynamics of land use and land cover (LULC) change in Saudi cities between 2013 and 2023. Using Landsat satellite imagery and a Maximum Likelihood Classifier, LULC changes are analyzed in nine cities to understand the impact of Saudi Arabia’s rapid urbanization following the oil crisis. The objectives are: (1) to evaluate the LULC changes in nine cities between 2013 and 2023; (2) to explore the spatiotemporal patterns of LULC changes in this period; and (3) to interpret the urban expansion that occurs in Saudi Arabia in the light of the kingdom sustainable development plan. The results identifiy four primary LULC change models: Urbanization, Vegetation Recovery, Land Degradation, and Ecological Transition Models. The Urbanization and Land Degradation Models determine Saudi Arabia’s urban growth trend, posing significant challenges to sustainable development. Most cities experienced substantial increases in impermeable surfaces and developed lands, while concurrently losing high terrain and barren areas. However, the small cities showed lower urbanization and land degradation rates than major cities, showing an opportunity to sustainably grow by adopting sustainable planning approaches. The study’s findings show that more targeted and comprehensive policies are needed to address the challenges of urbanization and land degradation, mitigate environmental impacts, and ensure long-term resilience. It underscores the importance of considering Ecological Urbanism in urban development plans.

Scale-specific effects of urban landscape pattern on the COVID-19 epidemic in Hangzhou, China

ContextUnderstanding the scale-specific effects of different landscape variables on the COVID-19 epidemics is critical for developing the precise and effective prevention and control strategies within urban areas.ObjectiveBased on the landscape epidemiology framework, we analyzed the scale-specific effects of urban landscape pattern on COVID-19 epidemics in Hangzhou, China.MethodsWe collected COVID-19 cases in Hangzhou from 2020‒2022 and combined the datasets of land use and land cover (LULC) and social gathering point (SGP) to quantify the urban landscape pattern. Optimal general linear model with stepwise regression was applied to explore the dominant landscape factors driving the COVID-19 transmission in the city. Furthermore, multi-scale geographically weighted regression illustrated the spatial heterogeneity and scale specificity of these landscape variables’ effects to COVID-19 epidemic.ResultsEight landscape variables of LULC and SGP patterns were identified which explained 68.5% of the variance in spatial risk of COVID-19. Different optimal bandwidths across these variables in MGWR indicated their scale-specific effects. LSI of green space enhanced the spatial risk across the entire region. The effects of landscape contagion, the number of water bodies, LSI of cropland and built-up areas, and the density of commercial houses were detected to vary between urban and suburban areas. The effects of LSI of water bodies and the density of shopping malls were found to vary among different districts.ConclusionsIn this study, we firstly discriminated the scale-specific effects of different landscape variables on the COVID-19 epidemic in the urban region. These findings can help to optimize the differentiated zoning prevention and control strategies for COVID-19 in cities and guide policy-making and urban planning at a multi-scale hierarchical perspective to improve public health and urban sustainability.

Understanding the relationship between land use/land cover changes and air quality: A GIS-based fuzzy inference system approach.

Air pollution is a global issue that demands urgent attention due to its detrimental effects on human health and the environment. Land Use and Land Cover (LULC) change is an essential factor that significantly impacts ambient air quality through alterations in emission sources, vegetation cover, natural processes, and urban design. This study investigates the spatio-temporal variation of key air pollutants resulting from urban LULC changes in the Delhi region. Findings reveal a notable increase in pollutant concentrations, particularly particulate matter, in 2019 (PM10: 318.65 ± 45.80 µg/m3) and 2023 (PM10: 383.70 ± 61.49 µg/m3), compared to 2008 (PM10: 246.76 ± 30.66). LULC change analysis demonstrates a rise in built-up areas 24.59%(2008 to 2019), 33.62% (2008 to 2023) and a decline in vegetation cover 27.49% (2008 to 2019),32.37% (2008 to 2023). Correlation analysis indicates a positive correlation between PM10 and urban indices (+ 0.63) and a negative correlation between PM10 and vegetation indices (- 0.61), highlighting the impact of LULC on air quality deterioration. Subsequently, a fuzzy inference system model integrates LULC information to develop an air quality index (AQI). Incorporating LULC changes in AQI assessment offers a realistic approach to address the complexity arising from combined air pollutant effects, surpassing conventional AQI calculation methods. The findings underscore the significance of understanding the impact of Land Use and Land Cover (LULC) change on ambient air quality in formulating effective air quality management programs and policies. Integrating this knowledge into policymaking is crucial for the successful abatement of air pollution in urbanized areas.

Evolving road networks and urban landscape transformation in the Himalayan foothills, India.

The amalgamation of Himalayan and Indo-Burmese biodiversity has made the state of Manipur, India, a unique ecosystem. In addition, the region is a strategic place for the country to establish an economic corridor to Southeast Asia. In recent times, the region has witnessed tremendous infrastructural/road development. Subsequently, forest fragmentation related to urbanization and road expansion has emerged in the Himalayan foothills. The development of roads brought rapid changes in land use and land cover (LULC) and thus subsequent environmental degradation. The current study attempts to understand how the development of road networks has impacted the natural cover in Manipur, India. A spatio-temporal analysis was performed to assess the relationship between the development of road networks and LULC changes using the Landsat satellite images over a decade (2012-2022). The results showed significant changes in the area coverage of LULC categories such as agricultural land, built-up areas, forest, and water bodies with the increase in road density. To have a holistic view, the study area was segregated into three functional zones based on their urban land use pattern, i.e., urban center, peri-urban, and urban peripheral fringes. Urban sprawl in the urban center has led to the rapid conversion of forested lands into built-up areas and agricultural lands in the peri-urban and urban peripheral fringes, respectively. The decline of forest areas to urbanization in peri-urban and urban peripheral fringes calls for conservation and restoration initiatives. The study also emphasizes how different stakeholders can be involved and empowered to strengthen public-private partnerships for conservation and restoration in such sensitive ecosystems. Urban planners and developers should be critical in making informed decisions through understanding ecological concerns in tandem with infrastructural development.

A Study on the Cooling Impact of the River Yamuna and its Surrounding LULC: A Case Study of New Delhi, India

Abstract. The unplanned growth of urban cities has adversely affected the thermal conditions. The Land Use and Land Cover (LULC) present in urban areas significantly impacts the thermal conditions of the surrounding environment. This study investigated the cooling effect of river in dense urban environments. It is conducted on the Yamuna river that flows through New Delhi, the capital city of India. The River Cooling Intensity (RCI) and River Cooling Range (RCR) assessment has been done for the summer season of May 2023 with the help of Landsat 8 images. The RCI ranged from 3.58°C to 1.16°C while the RCR varied from 1320 m to 360 m. It is observed that the river cooling effect is higher in the built-up class while the vegetation class weakens the effect of river cooling intensity and range. Zonal mean temperature varies from 32.02°C to 34.82°C according to the LULC distribution. This study provides valuable insights into the relationship between river cooling and temperature regulation according to the LULC classes. It also establishes the presence of an urban cool island inside the city region due to the river, as the mean temperature of the river surrounding (33.83°C) was lower than that of the city (35.73°C).

Detection of spatial and temporal variation characteristics of vegetation cover in the Lower Mekong region and the influencing factors

As the main component of terrestrial ecosystem, vegetation plays a very important role in regional ecosystem environmental change, global carbon cycle and climate regulation. The Lower Mekong region (LMR) is at the core of Southeast Asia, its vegetation changes will affect the regional ecosystem and climate. Five countries of LMR were selected as the study area, based on MODIS (Moderate-Resolution Imaging Spectroradiometer) NDVI(Normal Difference Vegetation Index) data from 2000 to 2022, using the Sen’s slope estimator, Mann–Kendall trend test and geographic detector to study the spatial and temporal variation trends and driving forces of vegetation coverage. The results showed that:(1) From 2000 to 2022,the vegetation coverage in the LMR showed an overall fluctuating upward trend, the annual average Fractional Vegetation Cover(FVC) value was 0.70, mainly with high vegetation coverage and relatively high vegetation coverage. Vegetation distribution had obviously spatial heterogeneity, and the vegetation of Myanmar, Laos and Vietnam was significantly larger than Thailand and Cambodia.(2) The variation trend analysis of Sen_MK showed that the proportion of improved and degraded vegetation coverage areas in the LMR were 56.33% and 37.55% respectively. The vegetation improvement area was much larger than the vegetation degradation area during 2000–2022. According to the variation trend analysis of different countries, the vegetation coverage improvement area in Vietnam, Myanmar and Thailand were larger than the degraded , the overall vegetation coverage variation trend were good. However, in Laos and Cambodia, the degraded areas were larger than the improved, the overall variation trends of coverage were not good.(3) The results of geographic detector showed that the Land Use and Land Cover(LULC) had the greatest influence on vegetation coverage in the study area.The influencing factors of vegetation coverage were different in the LMR. For Vietnam, Thailand and Laos,elevation and slope factors were second only to LULC, for Myanmar and Cambodia, the influence of precipitation factor was second only to LULC. The results provide scientific data support for understanding the ecological environment status and future changes in the research area.

Significant negative impact of human activities on carbon storage in the Yellow River Delta over the past three decades

With the increasing intensification of human activities, significant changes in land use and land cover (LULC) have posed a severe threat to the carbon storage capacity of wetland ecosystems. A deep understanding of this impact is crucial for protecting regional ecosystems and promoting sustainable development. This study utilized the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and the human activity intensity (HAI) index to conduct detailed grid analysis and global analysis of carbon storage through creating fishnet system and explored the complex relationship between carbon storage and HAI in the Yellow River Delta (YRD), China. The results indicated that over the past 30 years, natural wetlands such as meadow wetlands and salt marshes in the study area had undergone significant degradation due to escalating human activities, while artificial wetlands and non-wetland areas expanded. Concurrently, the total regional carbon storage had declined by 2.08 Tg, representing a significant drop of 8.22 % in the YRD from 1990 to 2020. Among them, dry land, as the primary land type, served as the most crucial carbon pool. Additionally, the human activity intensity of land surface (HAILS) increased significantly, with a growth rate of 37.27 %. HAI mapping revealed a continuous expansion of areas with high HAI. In contrast, the Yellow River Delta National Nature Reserve (YRDNNR) maintained relatively low HAI. Correlation analysis further showed the significant negative correlation (p < 0.01) between carbon storage and HAI, with r values of grid analysis ranging from −0.1395 to −0.0344, while that for global analysis was −0.9643, respectively. This reflected the spatial heterogeneity and agglomeration effects of data analysis across different scales. This study provides valuable insights for achieving the “dual carbon” goals and supporting the conservation and management of wetland ecosystems.

Optimum Combination of Spectral Variables for Crop Mapping in Heterogeneous Landscapes based on Sentinel-2 Time Series and Machine Learning

Abstract. This article aimed to determine a workflow for more efficient large-scale crop mapping using a time series of images from the Sentinel-2 Satellite, statistical methods of attribute selection, and machine learning. The proposed methodology explores the best possible combination of spectral variables related to vegetation (16 vegetation indices in the RGB, NIR, SWIR, and Red Edge regions) to characterize different spectro-temporal profiles of Land Use and Land Cover (LULC) in spatially heterogeneous landscapes. First, we applied a data dimensionality reduction analysis using the PCA (Principal Component Analysis) method. Subsequently, the variables that showed the highest statistical correlation between each other were used in the spectro-temporal classification process, using the Random Forest, TempCNN, and LightTAE algorithms, following three different strategies: C1 (ALL), C2 (BE + IV (Red Edge)) and C3 (BE + IV (without Red Edge)), where ALL – All variables; BE – Spectral Bands; IV – Vegetation Indices. Given the results found, the C2 classification scenario (with bands B3, B4, B5, B6, B7, B8, and B8A and the NDRE1, RESI, and MSR indexes) demonstrated the best LULC classification accuracy at the crop pattern level, compared to the other scenarios, with average values of 0.91, 0.88, 0.91, 0.89, and 0.89 (Global Accuracy, Producer Accuracy, User Accuracy, Kappa index, and F1-Score, respectively, for the TempCNN model), the which emphasized the importance of both qualitative and quantitative variability of sampling data and variables based on the Red Edge region for improving LULC classification processes in large-scale heterogeneous landscapes.

Assessing Land Use and Cover Changes arising from the 2022 water crisis in Southeast China: A comparative analysis of Remote Sensing Imagery classifications and Machine Learning algorithms

Abstract. The water crisis in the southeast region of China in 2022, caused by one of the worst heatwaves on record, was characterized by severe shortages of water resources, leading to challenges for local communities, agriculture, and industry. To analyze changes in land use and land cover (LULC) in the Jialing River region, Chongqing, China, we compared Remote Sensing (RS) imagery classifications before and after the intense heat waves of 2022. We evaluated the performance of two machine learning algorithms, KDTree KNN and Random Forest (RF), in LULC classifications. The classifications were carried out based on the RS images from the OLI/Landsat 8 system, NDWI index, and SRTM data. The model performances were similar, the classification accuracy showed that the RF algorithm was superior to KDTree KNN. The RF LULC classification and area calculation corroborate with the visual analysis, reaffirming the superiority of RF, which shows a decrease in water surface area, unlike DKTree KNN.

Economic Valuation of Ecosystem Services of Coastal Area of Kinondoni District, Tanzania

This study investigates the often overlooked economic values of ecosystem services in the coastal area of Kinondoni District, Tanzania, in the context of spatial and temporal changes in land use and land cover (LULC). The primary objectives were to assess how ecosystem services and functions have been affected by LULC changes over a 30-year period, from 1993 to 2023. The study employs a benefit transfer method, integrating local and global estimations of ecosystem service value (ESV) with field survey, remote sensing and GIS techniques. The findings reveal that the annual changes in ESV during the study period are estimated at US$ 0.024 million and US$ 0.034 million using local and global coefficients, respectively. Over the three decades, there has been a significant annual loss of US$ 0.72 million locally and US$ 2.11 million globally, primarily due to the degradation of mangrove forests and bushland. The decline in ecosystem functions is largely driven by reductions in regulating services, which account for 54.1% of the total decrease in local valuations and 31.6% in global valuations. Supporting services also experienced substantial declines, with reductions of 39.7% in local valuations and 55.8% in global valuations. The study underscores the urgent need to review and enhance management and conservation strategies to ensure the sustainability of the coastal ecosystems in Kinondoni District, Tanzania.

A dynamical system to model land use interactions by using population dynamics: application on vulnerable grasslands

We introduce a deterministic set of equations, the Lotka–Volterra (LV) generalized equations, to explain or predict the temporal dynamics and competition between land use and land cover (LULC) classes. We evaluated this new approach in two regions of Uruguay where human activities have reduced natural grasslands. We infer the interaction coefficients from the LV model through the pairwise maximum entropy method using the spatial covariance matrices obtained from annual remote-sensing LULC maps. The model performed well in predicting short-term temporal changes, with errors similar to those obtained using traditional Markov chains. This model introduces a novel avenue for assisting LULC modelling in improving the temporal dynamics.

Modeling carbon dynamics from a heterogeneous watershed in the mid-Atlantic USA: A distributed-calibration and independent verification (DCIV) approach

The terrestrial ecosystem plays a vital role in regulating regional and global carbon budgets. Ecosystem models are extensively employed to estimate carbon fluxes across different spatial scales. However, there remains a need to reduce the uncertainties associated with model parameterization and input data. To address these limitations, we assessed a distributed-calibration and independent-verification (DCIV) approach that uses (1) remotely sensed net primary production (NPP) and evapotranspiration (ET) data from the Moderate Resolution Imaging Spectroradiometer (MODIS), (2) multi-site eddy covariance net ecosystem exchange (NEE) data; and (3) field sampling of soil organic carbon (SOC) and aboveground biomass (ABG) data to improve the overall predictability of carbon fluxes for the different land use and land cover (LULC) types at a watershed scale. The DCIV approach was applied to an advanced version of the Soil and Water Assessment Tool (SWAT)-Carbon (or SWAT-C), equipped with Century-based SOC algorithms to simulate carbon dynamics for watersheds with heterogeneous vegetation. The objective of the modeling effort was to assess carbon stocks and fluxes under different land management scenarios for a 3000-acre experimental farm and forest preserve in the northeastern United States. Our study showed that a large SOC stock of at least 100 tons ha−1 is stored under mixed forest, deciduous, shrubland, and floodplain (grass). Our study also showed that converting floodplain (grass) to deciduous forest has the potential to increase CO2 uptake (-NEE) by an order of three magnitude and ABG by 77 %, leading to an increased SOC stock of 23 % after twenty years. Similarly, we found that converting ungrazed grassland to grazed pasture leads to a non-statistically decreasing trend of SOC, especially in the 0–30 cm soil layer. Thus, the methodology used in this study can be applied to improve carbon dynamic prediction from a heterogeneous watershed at a regional scale.