Land Use Change Research Articles

Choosing reference land use for carbon and biodiversity footprints

Abstract Purpose Carbon and biodiversity footprints are increasingly calculated. However, little attention has been paid to the coherency of methodological choices and interpretation of the footprint results. This paper aims to clarify the coherency between the choice of reference land use and carbon and biodiversity footprints and discuss the challenges encountered. Methods First, we analyse features of reference land use options proposed in ISO 14067 (2018) i.e. business as usual, projected future, target, potential natural regeneration and historic baseline. Second, we discuss the connection between temporal scope and life cycle of land use. Third, we provide guidance on how to choose reference land use coherently. Fourth, we discuss specific issues related to carbon and biodiversity footprints and their relationship to reference land use. Finally, we provide conclusions and recommendations for further conceptual development. Results and discussion Choice of reference land use and determination of temporal scope of a study may significantly influence the assessment of carbon or biodiversity footprints. If the aim of a footprint study is to assess the effects of land use, reference land use should describe dynamically non-use of land. Thus, potential natural regeneration or continuation of natural state (if it is the starting point) of land is the coherent reference land use in this case. If the aim of a footprint study is to assess the effects of a decision to change land use or its management, reference land use should describe dynamically alternative use of land expected without the studied decision. Business as usual or projected future is a coherent reference land use in this case. Historic baseline or target reference land use may be useful in comparing the ecosystem value of the studied land use with a pre-set historic baseline or (policy) target. Conclusions We conclude that coherent choice of reference land use for carbon or biodiversity footprints depends on the goal and scope of a study. Beyond methodological coherency, issues related to the reliability and availability of appropriate data are relevant and vary between reference land use choices. However, difficulties in the assessment should not justify incoherent choices. We recommend that uncertainties are not overlooked but incorporated in the assessment and considered in the interpretation of the results. In addition, we recommend that key methodological choices, including the definition of the purpose of a study, chosen temporal scope and applied reference land use, should be clearly and transparently presented.

Habitat quality assessment on the Qinghai-Tibet plateau across vegetation ecoregions using InVEST and Geodetector models

IntroductionThe Qinghai-Tibet Plateau holds a significant position in the global ecosystem, with its unique high altitude and complex geographical features fostering rich biodiversity. The plateau’s high-altitude environment and distinctive climate system significantly influence the Asian monsoon and regional hydrological cycles. Its vast glaciers and snow reserves are crucial in regulating the Asian climate. While previous studies have analyzed the habitat quality of the Qinghai-Tibet Plateau, significant variations exist across different vegetation ecoregions, warranting further investigation into these variations and their influencing factors.MethodsThis study conducted a comprehensive assessment of habitat quality on the Qinghai-Tibet Plateau by analyzing the impacts of human activities, climate change, and grazing intensity. Using the InVEST Model and Geodetector Model, we evaluated habitat quality across the plateau’s six vegetation ecoregions from 2000 to 2020. The efficacy of ecological red-line policies in actual conservation efforts was also examined.ResultsThe results indicate that habitat quality among vegetation ecoregions is shaped by a complex interaction of geographical and human-induced factors, leading to notable spatiotemporal variations. From 2000 to 2020, the quality of habitats was significantly impacted by human activities, climate change, grazing intensity, and land-use changes. These effects were especially prominent during the period from 2005 to 2010.DiscussionThe changes in habitat quality on the Qinghai-Tibet Plateau are influenced by multiple driving factors, with significant differences in the drivers across various vegetation ecoregions. The ecological redline policy has played an important role in protecting the ecological environment in key areas, particularly in regions with high human intervention, where its effectiveness is more pronounced. In future ecological management, it is essential to strengthen conservation measures based on regional specificity. By comprehensively considering the impact of human activities and natural factors, developing more targeted management strategies is crucial for improving habitat quality.

Attribution of Vegetation Dynamics in the Yellow River Water Conservation Area Based on the Deep ConvLSTM Model

Climate change and human activities have significantly impacted the long-term growth of vegetation, thereby altering the ecosystem’s response mechanisms. The Yellow River Water Conservation Area (YRWCA) is a critical ecological functional zone in China. Since 1982, the vegetation in the YRWCA has changed significantly, and the primary drivers of vegetation which changed before and after 2000 were identified as climate change and human activities, respectively. However, the extent to which different drivers contribute to the vegetation dynamics of the YRWCA remains uncertain. In this study, we introduced a modified deep Convolutional Long Short-Term Memory (ConvLSTM) model to quantify the contributions of climate change and human activities to vegetation change while considering the spatiotemporal heterogeneity. We identified areas with minimal human activity before 2000 using the residual trend method, and used the regional data from these areas to train the model. Subsequently, we applied the trained deep ConvLSTM model to perform an attribution analysis after 2000. The results show that the deep ConvLSTM effectively captures the impacts of climate change on vegetation growth and outperforms the widely used Random Forest model (RF). Despite the fact that the input data of RF were optimized, ConvLSTM still distinctly outperformed RF, achieving R2, MAE, and RMSE values of 0.99, 0.013, and 0.018, respectively, compared to RF’s corresponding values of 0.94, 0.038, and 0.045. Since 2000, the regional normalized difference vegetation index (NDVI) has shown a broad increasing trend, particularly in dryland, primarily induced by human activities from 2006 to 2015. Furthermore, an analysis of changes in regional land use, particularly in drylands, revealed that the highest magnitude of conversion of farmland back to forest or grass was recorded from 2000 to 2005. However, the most significant contributions from human activities occurred from 2006 to 2015, indicating a time lag in vegetation recovery from these ecological programs. The attribution results provide valuable insights for the implementation of ecological programs, and the introduced deep ConvLSTM proves the suitability of deep learning models that capture spatiotemporal features in vegetation growth simulations, allowing for broader applications.

Impacts of land use and land cover change on the landscape pattern and ecosystem services in the Poyang Lake Basin, China

ContextDecades of intensifying human activities have led to drastic changes in land use and land cover (LULC) in the Poyang Lake Basin (PLB), resulting in significant changes in landscape pattern and ecosystem service value (ESV), thereby affecting regional sustainability.ObjectivesWe focused on understanding the impact of LULC changes on the landscape pattern and ESV of the PLB and used the Patch-generating Land Use Simulation model (PLUS) to predict LULC changes in 2050.MethodsWe evaluated landscape patterns using landscape metrics and calculated ESV using the ecosystem service equivalent factor method. The Pearson correlation coefficient was used to analyze the correlation between landscape patterns and ESV from 1990 to 2020. Then, we combined the PLUS model and the ecosystem service equivalent factor method to calculate the ESV under multiple scenarios from 2020 to 2050.ResultsFrom 1990 to 2020, the LULC of the PLB changed to varying degrees. The PLB has undergone a rapid process of landscape fragmentation, and the total ESV of the PLB has decreased. The total ESV was positively correlated with the CONTAG index and negatively correlated with the SHDI index. Between 2020 and 2050, the ESV of the PLB is projected to decrease under the NDS (nature development scenario) and EDS (economic development scenario) and increase under the EPS (ecological protection scenario).ConclusionsESV responded to changes in landscape pattern. We recommend that the PLB should increase patch connectivity. Additionally, future development in the PLB should prioritize ecological protection to prevent further declines in ESV.

Comparison of various models for multi-scenario simulation of land use/land cover to predict ecosystem service value: A case study of Harbin-Changchun Urban Agglomeration, China

Understanding the intricate changes in land use and land cover (LULC) transformations, as well as accurately quantifying the ecosystem services value (ESV), holds paramount importance in achieving sustainable development goals. However, previous studies have notably neglected conducting empirical evaluations across various LULC prediction models under identical conditions within the same geographical region. Additionally, the majority of relevant studies primarily concentrate on local scales. In this study, we used CA-Markov, Future Land Use Simulation (FLUS) and Patch-generating Land Use Simulation (PLUS) models to simulate the dynamics of LULC, respectively. Subsequently, we successfully projected the future LULC patterns and corresponding ESV within the Harbin-Changchun Urban Agglomeration (HCUA), China. During the period spanning from 2000 to 2020, a persistent reduction of 2067 km2 in farmland was witnessed within the HCUA, while an accompanying expansion of 4081 km2 in built-up land occurred concurrently. The ESV in HCUA experienced a fluctuating trend. There was an initial decline of 7.443 × 109 yuan during the first decade, which was subsequently followed by an increase of 4.615 × 109 yuan during the latter decade. PLUS, FLUS, and CA-Markov models can all simulate the LULC of HCUA, but in decreasing order of accuracy and ease of use, with OA values of 0.8987, 0.8944 and 0.8651, and Kappa values of 0.8217, 0.8150 and 0.7860, respectively. The PLUS model was chosen to predict the future LULC. Then four development scenarios were set. Our optimization results indicate that it is advisable to restrict the area of built-up land within 17000 km2 in 2030. Moreover, under the economic and ecological balance (EEB) scenario, both ESV and economic benefits are projected to increase compared to the business as usual (BAU) scenario. This study offers valuable insights and serves as a significant reference for future research endeavors focused on optimizing land use.

Impacts of land use and abiotic factors on fruit, seed, and leaf morphology of the desert date Balanites aegyptiaca in Benin: Implications for management

Preserving intraspecific phenotypic variation within socio-economically important but declining species is fundamental in the current context of rapid change in land use and environmental conditions. Balanites aegyptiaca (L.) Delile is a valuable seed oil tree species native to the arid and semi-arid regions of Africa. Notable morphological variations within the species are recognized by local communities, who often select trees with preferred traits for preservation in croplands. This study compared phenotypic variation in B. aegyptiaca based on sixteen phenotypic traits of fruits, seeds, and leaflets between trees in a strictly protected area and those in croplands. Furthermore, the relative role of land use, soil characteristics, bioclimatic variables, elevation, and slope in the observed variation was analysed. Student t-test, principal component analysis, redundancy analysis, and variance partitioning were used for data analysis in R software. Results showed that, after accounting for tree variation, significant differences were observed in only two traits: seed ratio, and leaflet dry weight, with mean values in cropland being 1.470 (higher), and 0.943 (lower) times that of wild trees, respectively. Furthermore, principal component analysis showed great overlap between ellipses of cropland and wild trees. The variance partitioning analysis showed that bioclimatic variables explained 18 % of the total variation followed by soil characteristics (11 %), and land use (9 %). These findings collectively suggest weak impacts of croplands and a lack of domestication syndrome in B. aegyptiaca in Benin, potentially due to persisting gene flow between wild and cropland trees, inadvertent species management, or low intensity of selection pressures. Future studies should explore organoleptic properties, physico-chemical characteristics of the pulp and seed oil and discern the extent to which observed differences are attributable to genetic factors and biotic factors (pest attacks, plant-plant interactions, etc.).

Geographical distribution of caudatoside and ptaquiloside in bracken ferns in Northern Europe

Bracken ferns (genus Pteridium) are among the most prevalent plants worldwide, with their distribution expanding due to their invasive nature. The environmental implications of their proliferation in areas affected by human activity, natural disasters, or land-use changes are concerning, primarily because of the carcinogenic illudane glycosides they produce. These compounds cause domestic and wildlife animal poisoning, as well as contamination of dairy products and drinking water. Several illudane glycosides are known, but usually only ptaquiloside (PTA) is monitored. This study investigates the spatial and temporal variations in illudane glycosides PTA, caudatoside (CAU) and ptesculentoside (PTE) across two phenotypes of Pteridium aquilinum (vars. aquilinum and latiusculum) over a broad geographic range spanning Denmark, Sweden, and Finland, encompassing 66 locations. We analysed different parts of the fern fronds (the tips and the lowest pinnae) using LC–MS and statistically explored the influence of phenotype, frond part, geographic location, sunlight exposure, and the surrounding ecosystem on glycoside content. Our findings reveal that PTA accounts for approximately two-thirds of the total illudane glycoside content, followed by CAU at nearly one-third, and a minor contribution from PTE. Glycoside levels were not influenced by phenotypic varieties or the studied environmental factors, but were significantly affected by geographic location. Specifically, CAU levels increased progressively towards the northeast, while PTA concentrations were highest in Denmark and markedly decreased in northeastern countries by over threefold, presumably due to climatic gradient. It has been further supported by temporal analysis in selected PTA-dominant regions indicating a reduction in PTA towards the end of the growing season, aligning its levels with those of CAU. Our study highlights that CAU concentrations in bracken ferns may equal or surpass PTA, contesting the prevailing view that PTA is the only notable illudane glycoside in Bracken. To provide unbiased assessment of the potential risks posed by P. aquilinum in the region, environmental and toxicological research should include measurements of not only PTA, but also CAU and, if possible, PTE.

Effects of expansion of protected areas and habitat transformation on spatiotemporal variation in human-wildlife conflict.

Land-use changes and the expansion of protected areas (PAs) have fostered increased interactions between humans and wildlife, resulting in an escalation of human-wildlife conflict (HWC) globally. However, HWC spatiotemporal pattern variation and its associations with PAs and land-use change remain poorly understood. To address this knowledge gap, we mapped and analyzed HWCs from 1990 to 2022 across China. We comprehensively mapped the spatiotemporal dynamics of HWCs in ArcGIS with data sets stratified by county, year, and species; assessed the impact of PAs through propensity score matching; and analyzed the effects of habitat transformation with linear mixed models. As PA increased from 0 to 20,000km2, the likelihood of HWCs initially increased (50%) before declining (20%). Conversely, as the distance from a PA grew, the likelihood of HWC gradually decreased (0 beyond 65km). There was a temporal lag between the establishment of a PA and the occurrence of HWC. Habitat loss catalyzed HWCs, whereas decreased levels of habitat fragmentation sometimes initially caused a temporary increase in HWCs. In general, the distribution of PAs greatly affected HWC occurrence, and habitat loss and fragmentation were critical drivers of HWCs, both of which exhibited time-lagged effects. HWC has become more challenging to address as conservation initiatives have led to significant recovery of the habitats and populations of wild animals. Further measures to address the HWCs are needed to ensure the preservation of animal welfare while fostering the mutually beneficial coexistence of humans and animal species. Finally, our study provides an important starting point for informing future HWC research and conservation planning on a global scale.

Urban green spaces with high connectivity and complex vegetation promote occupancy and richness of birds in a tropical megacity

Urban growth often leads to land-use changes that result in biodiversity loss and reduced human benefits. In urban zones, green areas facilitate physicochemical processes (such as carbon capture, reducing environmental temperature and noise pollution), offer multiple benefits to human beings (e.g., water filtration and purification), and support numerous vertebrate populations, including birds. In the tropics, the capacity of green spaces to maintain bird populations is regulated by characteristics of these areas (e.g., vegetation structure) and environmental seasonality. In order to generate ecological knowledge to help conserve bird diversity in large urban settlements, this study aimed to (1) identify the most influential variables on the distribution of bird species in green areas of a tropical megacity, and (2) assess how bird richness varies between the dry and rainy seasons. Across two dry and rainy seasons between 2021 and 2022, detection records of 108 bird species were obtained from 101 green areas. Air temperature and sampling time were the primary factors influencing bird detection. Bird occupancy and richness were higher in parks near other green areas in first dry and increased with tree richness during the rainy seasons. Floral abundance explained the occupancy and richness in the second dry season. In 2021, the highest richness was observed during the dry season, while in 2022, the highest richness was estimated during the rainy season. These findings highlight the importance of resource availability and spatial arrangement in urban green areas for bird diversity, offering insights for conservation and maintaining ecosystem benefits in urban environments.

Grassland albedo as a nature-based climate prospect: the role of growth form and grazing

Abstract Nature-based solutions for mitigating climate change focus largely on land management to reduce carbon emissions and enhance carbon sequestration. Tree planting, commonly advocated for carbon offset, threatens grassland biodiversity and may induce positive radiative forcing (warming) by lowering albedo. Before making decisions about land-use changes in grasslands, an understanding of the fine-scale albedo of grassy versus woody vegetation is needed. Existing satellite-based albedo products offer global coverage with temporally fine, but spatially coarse, resolution, whereas fine-scale in situ grassland albedo data are sparse. We examined the hypotheses that albedo varies seasonally between grass type patches, between shrub and grass patches, and with grazing at the patch scale. Using a tripod-mounted albedometer, we quantified albedo of seven distinct grassland patches in South Africa’s eastern Karoo during early and late dormancy and growing seasons. Patches included intensely-grazed grazing lawn (Cynodon dactylon), grazed and less-grazed red tussock grass (Themeda triandra), grazed and less-grazed white tussock grass (Eragrostis lehmanniana), shrub (Pentzia incana) encroached grass, and bare ground. Season influenced albedo in all patches and, additionally, we found strong differences for the same period between years due to varying rainfall and temperature patterns. For grass-dominated patches, albedo differences were most pronounced during early dormancy, likely due to an effect of grass inflorescences. Albedo of intensely-grazed grazing lawns was consistently higher than other patches, except during early dormancy when white tussock grass albedo was equally high. We found no albedo difference between grazed and less-grazed tussock patches of either red or white grass. Shrub-encroached patches exhibited consistently lower albedo than other patches. Our findings underscore the nuanced relationship between grassland patches and albedo, with shrub encroachment, proposed afforestation, and certain grasses possibly increasing warming potential through reduced albedo. As climate initiatives extend into grasslands, understanding these patterns is essential for climate change mitigation and grassland conservation.

A Descriptive Analysis of Human-Environment Interactions in the Suburban Area of Pune City

The Baner-Balewadi was a rural area that transformed into a residential suburb and is now metamorphosing into a commercial and educational hub of Pune city. This area is now a part of Pune Municipal Corporation and is occupied by various schools, universities, and IT companies. The decadal population growth rate of the Baner-Balewadi area is 275%; it is much higher than the Pune district’s and city’s growth rate, which makes it a fascinating area for this study. A cross-sectional survey was conducted in this area covering three major regions: Balewadi Highstreet, Baner-Balewadi road, and NICMAR University. The data for 15 parameters were gathered, a total of 38 survey samples were collected using stratified random sampling, and land use was mapped to understand the grassroots reality. Thereafter, the parameters were analysed using a descriptive statistical technique, and spider mapping was used for data presentation. The strengths and challenges regarding infrastructural services, transportation, socio-economic factors, environment, and land use were identified. The results show that the waste collection is strong, but there are drinking water issues in the area. The recent development is causing stress on the infrastructural services and unregulated land-use change leading to environmental degradation. The metro construction is causing a lot of noise and air pollution, and people will still prefer buses as a transportation medium in the future because of good connectivity. The study is concluded with area-wise parametric recommendations made after examining the advancement, challenges, and gaps identified at the grassroots from both stakeholders’ and experts’ perspectives.

Can regionalization enhance the performance of land-use change models in rapidly urbanizing areas?

The performance of Land Use Change (LUC) models is influenced by the regional spatial characteristics that trigger the changes. However, the literature on LUC models generally reports validation results for entire regions without considering subregions that differ significantly in their LUC drivers. This research explores how the LUC driving forces differ among subregions and whether regionalization can improve the performance of LUC models in areas undergoing rapid urbanization. We analyzed the Geomod, Cellular Automata-Markov, and Land Change Modeler models across rural and urbanized subregions on the western edge of Mexico City. Regionalization significantly enhanced the overall accuracy of the models and the concordance of spatial patterns with the reference data in rural regions but was of limited benefit in urbanized regions. This shows the need to consider regionalized modeling to improve the performance of LUC models when there are noticeable differences in LUC drivers between subregions. These findings will enhance the usefulness of LUC models for urban planning and land management policies, promoting more precise and effective decision-making.

Perturbation of soil organic carbon induced by land-use change from primary forest

Abstract The impact of land-use change (LUC) on soil organic carbon (SOC) has been a wide concern of land management policymakers because CO2 emissions induced by LUC have been the second largest carbon source worldwide. However, due to insufficient data quality and limited biome coverage, a global big picture of the impact of LUC on SOC is still not clear. This study conducted a meta-analysis on 288 independent observations sourced from 62 peer-reviewed papers to provide a global summary of the change in SOC after the conversion of primary forests into other land-use types. The conversion of primary forest to cropland resulted in the most severe SOC loss (-33.2%), followed by conversion into plantation forests (-22.3%) and secondary forests (-19.1%). Nonetheless, SOC increased by 9.1% after a conversion from primary forests into pasture. More SOC loss was found at sites with lower precipitation for primary forests converted to cropland and plantation forests. The SOC loss decreased consistently with increasing mean annual temperature (MAT) for all four types of LUC. Moreover, the loss of SOC tended to worsen over time when primary forests are converted to cropland or plantation forests. In contrast, SOC loss recovered over time following conversion to secondary forests. The gain of SOC gradually increased over time after conversion to pastures. To conclude, the changes in SOC are related not only to the land-use type but also to precipitation, temperature and turn years after LUC. Due to limited data, this study focuses on soil profiles within 30 cm depth, and future research should explore SOC dynamics induced by LUC at greater depths. Overall, cases of SOC loss of approximately 30% following deforestation were very common (except for conversion to pasture). Thus we suggest that the loss of SOC following LUC should be carefully considered and monitored in land management.

Improving the efficiency of aerial surveys for monitoring North American beaver population dynamics

Context The North American beaver (Castor canadensis) was extirpated from much of its range in the US in the 1800s due to fur trapping and change in land use. However, the species has recolonised much of its former range, including the US state of Ohio. Since 2013, the Ohio Division of Wildlife (ODOW) has monitored trends in beaver colony density via aerial surveys of 40 km × 40 km plots classified as low, medium, or high suitability based on the amount of wetland. Nonetheless, the current classification system may miss important correlates of beaver colony density. Aims Our study aimed to (1) identify predictors of beaver colony density (number of colonies inferred from aerial counts of lodges) across Ohio, and (2) develop a model-based classification system to improve the efficacy of monitoring efforts. Methods To predict beaver colony density in Ohio we used an aerial survey dataset of 54 plots (40 km × 40 km) collected by ODOW annually between 2013 and 2020, along with a suite of environmental, anthropogenic, and climate variables in a mixed effects modelling framework. Key results Beaver colony density was positively associated with wetland and reclaimed surface mine areas and inversely associated with the proportion of agricultural lands. There was a negative interaction between wetland and surface mines; in general, beaver colony density increased with wetland and surface mine area. However, in plots with wetland area >1000 ha, beaver colony density was weakly negatively associated with surface mine area. Using median and interquartile ranges of model-averaged predicted beaver colony density, we developed a new classification of low, moderate and high suitability for both the survey plots and the entire state of Ohio. We found that eastern Ohio had high suitability, while the central and western parts of the state had lower suitability for C. canadensis. Conclusions Our approach to identifying predictors for beaver colony density at broad spatial scales highlights the importance of reclaimed surface mines and wetlands for beaver populations, while the model-based habitat classification provides ODOW additional information for monitoring and beaver management decisions. Implications Improved C. canadensis monitoring at the landscape scale using habitat classifications that consider local conditions can both improve annual survey cost-effectiveness and facilitate the sustainable management of this recovering species.

Analysis of Spatiotemporal Predictions and Drivers of Carbon Storage in the Pearl River Delta Urban Agglomeration via the PLUS-InVEST-GeoDetector Model

Land use and land cover change (LUCC) significantly influences the dynamics of carbon storage in thin terrestrial ecosystems. Investigating the interplay between land use alterations and carbon sequestration is crucial for refining regional land use configurations, sustaining the regional carbon balance, and augmenting regional carbon storage. Using land use data from the Pearl River Delta Urban Agglomeration (PRDUA) from 2010 to 2020, this study employed PLUS-InVEST models to analyze the spatiotemporal dynamics of land use and carbon storage. Projections for the years 2030, 2040, and 2050 were performed under three distinct developmental scenarios, namely, natural development (ND), city priority development (CPD), and ecological protection development (EPD), to forecast changes in land use and carbon storage. The geographic detector model was leveraged to dissect the determinants of the spatial and temporal variability of carbon storage, offering pertinent recommendations. The results showed that (1) during 2010–2020, the carbon storage in the PRDUA showed a decreasing trend, with a total decrease of 9.52 × 106 Mg, and the spatial distribution of carbon density in the urban agglomeration was imbalanced and showed an overall trend in increasing from the center to the periphery. (2) Clear differences in carbon storage were observed among the three development scenarios of the PRDUA between 2030 and 2050. Only the EPD scenario achieved an increase in carbon storage of 1.10 × 106 Mg, and it was the scenario with the greatest potential for carbon sequestration. (3) Among the drivers of the evolution of spatial land use patterns, population, the normalized difference vegetation index (NDVI), and distance to the railway had the greatest influence on LUCC. (4) The annual average temperature, annual average rainfall, and GDP exerted a significant influence on the spatiotemporal dynamics of carbon storage in the PRDUA, and the interactions between the 15 drivers and changes in carbon storage predominantly manifested as nonlinear and double-factor enhancements. The results provide a theoretical basis for future spatial planning and achieving carbon neutrality in the PRDUA.

Dynamics System Modeling of Declining Agricultural Land Area Potentially Causing Jakarta Flood Disaster

The change or evolution of land use in the DKI Jakarta area has caused various environmental problems in the city that have an impact on disasters that occur from time to time. The conversion of agricultural land that has a relatively high ability to absorb water into built-up land has reduced the water absorption capacity because the built-up area is increasingly impermeable which is at risk of causing flood disasters. This research focuses on a simple dynamic system modeling approach to understand the cause-and-effect relationship of the decline in agricultural land area in the DKI Jakarta area. The method used in this research is a qualitative-based dynamic system, dynamic system is a method that can be used to form structures and predict behavior and feedback. Dynamic system modeling using Powersim Studi 10 Express software uses time specifications ranging from 2020 to 2070. The results of the dynamic system model of agricultural land decline can be said to be valid, where the dominant structure formed is Negative Feedback (Balancing Loop) with Exponential Decay behavior and Average Means Error (AME) test results obtained a value of 29.6%

PRODUKTIVITAS SERASAH MANGROVE DI DESA POPALO GORONTALO UTARA

Popalo Village is located in Anggrek Sub-district, one of the sub-districts administratively located in Gorontalo Utara Regency, and has a mangrove area of 51.08 hectares. Some conditions of mangrove areas in Popalo Village have changed land use into ponds by the community; this has led to concerns about a decrease in the function and role of mangrove areas as nutrient converters. This study was conducted to determine the productivity and decomposition rate of litter of each component, including leaves, twigs, fruits, and flowers in mangrove vegetation, and the percentage of mangrove litter decomposition. The research method was sampling using litter traps with observation time every 15, 30, 45, and 60 days. The analysis carried out was to calculate the productivity of mangrove leaf, twig, fruit, and flower litter—the decomposition rate and percentage of litter decomposition of each mangrove component. The results showed that litter productivity in each part of the mangrove plant, namely leaves, twigs, flowers, and fruit that became the research sample, experienced a downward trend during the 60 days of observation, while the results of the percentage of decomposition showed a fluctuating trend. This condition is caused by several environmental factors essential to decomposing litter. Influencing factors include temperature at the research site, seawater salinity, daily tides, and the activity of microorganisms in the soil that support the decomposition process.

Artificial Intelligence in Aspect of Deforestation and Plantation Sustainability: Bibliometric Approach

This study examines the application of Artificial Intelligence (AI) in addressing deforestation and promoting sustainability in plantations using a bibliometric approach. Deforestation, a critical global issue, results from agricultural expansion, plantation development, and land-use changes, leading to significant environmental degradation. AI has been proposed as a powerful tool to monitor and manage deforestation more effectively, offering solutions such as satellite imagery analysis and predictive models. Through a bibliometric analysis spanning the last decade (2013–2023), this study uses VOSviewer to visualize co-citation networks, identifying key research trends and clusters related to AI in deforestation and plantation sustainability. The findings reveal that research is concentrated in regions like Indonesia and Brazil, where AI technologies like machine learning are employed to predict deforestation and enhance resource management. Emerging research areas include the integration of AI with the Internet of Things (IoT) and blockchain for improved data management and sustainability practices. This analysis provides insights into the growing role of AI in mitigating deforestation and offers recommendations for future research, including addressing ethical challenges and regulatory frameworks to further enhance sustainable plantation management.

Grassland or Cropland? Land Use Dilemma and Ecological Solutions in Inner Mongolia

ABSTRACTInner Mongolia plays a critical role in both ecological conservation and food provision in China. However, some researchers have argued that focusing on and improving only one side of the equation necessarily threatens the functionality of the opposite side. To address this problem, we compared a “business‐as‐usual” scenario (BAU) with a “sustainable land use planning” scenario (SLU) constructed by simulating spatiotemporal changes in croplands and grasslands in Inner Mongolia from 2020 to 2030. Additionally, we analyzed the changes in ecosystem services and protein supply associated with changes in land use. We found that, in the BAU scenario, grasslands would decrease by 1.85% over the simulation period, while croplands would increase by 9.94%, with ecosystem services decreasing under both land uses. In contrast, land use changes over the same period in the SLU scenario are more significant, with increases of 11.33% and 2.78% in grassland and cropland, respectively, but, in this case, with ecosystem services increasing under both land uses. Moreover, protein supply increased under both scenarios, but SLU scenario can provide 33% more protein than the BAU scenario. The interconversion of cropland and grassland is the main type of land conversion in the study region, while cropland, grassland, and bare land show a triangular cycle of conversion. In addition, the implementation of scenario planning can realize multiple dividend for cultivation, livestock, and ecology in Inner Mongolia.

Nature's Contributions to People: Systematic Mapping of Studies and Inventory of Methodologies and Indicators for Quantifying Hydrological Ecosystem Services

Objective: Evaluate the estimation of nature's contributions to people - NCP (i.e. ecosystem services - ES) through bibliographic mapping and systematization of methodologies, tools and indicators of hydrological ecosystem services (HES). Theoretical Framework: Predatory exploitation of ecosystems is causing increasingly severe impacts on humanity. Accelerated by population growth, changes in land use and the decoupling of measures recommended by science from those adopted by nations, climate change is triggering alterations in the global hydrological cycle. Adaptation strategies depend on the production of information on the supply and flow of HES. Method: Consultation of the Web of Science, Scopus and Science Direct databases, and systematic bibliographic mapping (2014-2024), with bibliometric analysis in the VOSviewer software, and recording of information on NCP, ES, metrics, methodologies for quantifying HES and respective indicators. Results and Discussion: A total of 743 abstracts were analyzed and 196 articles were selected. Of these, 103 estimated HES, with a predominance of studies on a river basin scale. Seventy-nine indicators were mapped, mostly biophysical, associated with water flow regulation and water quality. China was the most represented country in terms of mapped area. The most widely used models were InVEST and SWAT. Research Implications: Systematization of information for users interested in mapping and quantifying HES, with an indication of established aspects, methods, and knowledge gaps. Originality/Value: Mapping based on HES indicators, aggregation of studies under different ES frameworks and provision of a dynamic results panel, with spatialization of studies and various data filtering possibilities.