Forest Area Research Articles

Integrative analysis of nutrient dynamics and its hydrological pathways in a tropical montane forest watershed: Implications for landscape management

This study addresses a significant knowledge gap regarding the relationship between nutrient exports and abiotic factors in tropical forested watersheds. Focusing on a tropical forest watershed located on Hainan Island, we investigated the interactions between nitrogen (N) and phosphorus (P) exports and topographic, hydrological, and landscape elements. Using a model-data integration approach, our findings revealed that nutrient exports are predominantly in organic N and P forms, with the values of 15.66 ± 1.87 kg ha−1 yr−1 and 0.26 ± 0.07 kg ha−1 yr−1, respectively. Inorganic N and P were primarily sourced from orchards, while organic N predominantly originates from forest areas. Additionally, lateral flow was identified as the dominant hydrological pathway for nutrient transport, significantly influenced by topographical gradients. A parabolic relationship between organic N export and forest landscape fragmentation was also observed. These findings emphasize the importance of targeted landscape management strategies, such as selective logging, to mitigate N limitation in tropical forest watersheds, advancing the understanding of nutrient export dynamics and providing practical implications for sustainable forest management and conservation efforts.

Ethnoecology: The Community Adaptation Patterns of Forest Management in Grobogan Central Java

The aims of this research are to describe community adaptation patterns and determine community perceptions regarding forest environmental management. This qualitative research was studied using an ethnoecological approach with the research focus being the people of the Perhutani area of ​​Grobogan Regency. This research carried out the data collection through interviews, observation and documentation, with the main informant being Pesanggem as a forest land manager. This research produced the first findings in the form of community adaptation patterns in managing the environment covering three aspects, namely ecological adaptation, economic adaptation and social adaptation in the Pesanggem community in Gedangan Village, Wirosari District under the auspices of BKPH Sambirejo. 1) Ecological adaptation in Pesanggem showed the availability of well water which previously only relied on rainwater, then adjustments to various types of plants, as well as an understanding of prohibitions and sanctions for illegal activities in forest areas; 2) economic adaptation showed an increase in income for Pesanggem from forest land management; 3) social adaptation showed the existence of cooperation between members and other farmer groups within the LMDH forum. The second finding is that the public's perception in interpreting environmental management is that there are 5 values, namely the values ​​of wisdom, independence, religion, creativity and healthy living. Based on the research results, the author recommends that forest area communities be involved in managing and guarding forests for generational sustainability, and Perum Perhutani should invite partners from surrounding school institutions as a means of educating generations in joint forest management.

Great Gerbils (Rhombomys opimus) in Central Asia Are Spreading to Higher Latitudes and Altitudes.

The great gerbil (Rhombomys opimus) is a gregarious rodent in Central Asia and is one of the major pests found in desert forest and grassland areas. The distribution changes and migration routes of R. opimus in Central Asia under climate change remain unexplored. This study employed multi-model ensemble, correlation analysis, jackknife method, and minimum cumulative resistance (MCR) model to simulate the potential habitat of R. opimus under current and future (2030 and 2050) climate scenarios and estimate its possible migration routes. The results indicate that the ensemble model integrating Random Forest (RF), Gradient Boosting Machine (GBM), and Maximum Entropy Model (MaxEnt) performed best within the present climate context. The model predicted the potential distribution of R. opimus in Central Asia with an area under the curve (AUC) of 0.986 and a True Skill Statistic (TSS) of 0.899, demonstrating excellent statistical accuracy and spatial performance. Under future climate scenarios, northern Xinjiang and southeastern Kazakhstan will remain the core areas of R. opimus distribution. However, the optimal habitat region will expand relative to the current one. This expansion will increase with the rising CO2 emission levels and over time, potentially enlarging the suitable area by up to 39.49 × 104 km2. In terms of spatial distribution, the suitable habitat for R. opimus is shifting toward higher latitudes and elevations. For specific migration routes, R. opimus tends to favor paths through farmland and grassland. This study can provide guidance for managing and controlling R. opimus under future climate change scenarios.

Investigating the influence of land cover on land surface temperature

The increasingly serious urban heat island (UHI) effect is unfavorable to urban development. This study utilized land cover data and land surface temperature (LST) data of China in 2020 by using correlation analysis and spatial regression models to analyze the relationships between LST and two influencing factors (land cover and digital elevation model (DEM)). The results showed the following: (1) The correlation between LST and forest was highest in the Northeast China Plain (NCP), Huang-Huai-Hai Plain (HHP), Qinghai Tibet Plateau (QTP), and Loess Plateau (LP). DEM mean displayed its highest correlation in the Northern arid and semiarid region (NAR), Sichuan Basin and surrounding regions (SCR), Yunnan-Guizhou Plateau (YGP), and Middle-lower Yangtze Plain (MYP). Southern China (SC) had the highest correlation between LST and construction land. (2) There was spatial heterogeneity between land cover and LST. Unused land on LP had larger impact on LST. For every 1% increase in the proportion of unused land area, the LST increased by 0.250 °C. LST in some central and western regions of China (the NAR, the LP, the SCR, and the YGP) was mainly affected by local land cover; LST in eastern coastal regions (the HHP, MYP, NCP, SC) and QTP was not only affected by local land cover, but also by LST or land cover of neighboring regions. The warming effect of construction land on LST was more significant, with LST increasing by 0.079 °C to 0.338 °C for every 1% increase in the proportion of construction land area. Coordination of land use planning and synergistic remediation in different regions and rational planning of construction land are essential to mitigate the UHI effect. (3) Water bodies in the NCP, NAR, and MYP had the greatest cooling impact on LST, with LST decreasing by 0.277 °C, 0.246 °C, and 0.079 °C, respectively, for every 1% increase in the proportion of water bodies area. Forest on the QTP, LP, SC, and YGP had the greatest cooling impact on LST, and for every 1% increase in the proportion of forest area, LST decreased by 0.144 °C, 0.089 °C, 0.086 °C, and 0.038 °C, respectively. Actively planting trees and increasing the area of forests and water bodies are of positive significance in alleviating the UHI effect and improving the ecological environment.

Tree species diversity and spatial distribution of carbon stock in forests under different management regimes in Nepal's Western Terai Arc Landscape

Reliable tree species diversity and carbon stock data are crucial for establishing a baseline and aiding monitoring and management decision-making to ensure ecosystem functions and services, including carbon management. However, such data are not available for many primary forests, especially in developing countries like Nepal. For this study, we generated primary baseline data on tree species diversity, biomass, and carbon stock for forests under different management regimes in one of the critical landscapes of western Nepal. We sampled 94 clusters of concentric circular sample plots placed systematically at the nodes of 3 km by 3 km square grids in biological corridors (managed by community forest user groups) and a national park's buffer zones (managed by buffer zone user groups) to study the tree species composition and estimate the carbon stock. The number of tree species and botanical families reported in different sites did not differ significantly. The basal area was found to be highest in Brahmadev Corridor where tree density per unit area was also highest. Our study sites showed a basal area ranging from 26.42 to 40.65 m2/ha, which is higher than previously reported from similar forests. The total biomass (302.12 to 496 tons/ha) and carbon stock (142 to 233.12 tons/ha), both being highest in Brahmadev Corridor, were within the range of comparable data and national averages. The number of stems in all sites showed the reverse J-shaped pattern, indicating a stable population and good species recruitment. The buffer zones had a large proportion of forest area with the highest carbon stock classes of 300–400 tons/ha. The basal area and diameter at breast height showed a significant positive correlation with carbon stock, indicating that the basal area is a key determinant of carbon stock. Our study provides disaggregated information on tree species diversity, the quality of forests in terms of stem density and size-class distribution, and carbon stock and its distribution in different carbon stock classes in two forest management regimes. In this sense, our study is the first of its kind in providing important insights into forest and carbon management because areas with different carbon stocks need different management prescriptions.

Provision of land use and forest density maps in semi-arid areas of Iran using Sentinel-2 satellite images and vegetation indices

Zagros forests of Iran have several environmental, ecological, and socioeconomic values which provide the unique habitats for many endemic species. However, these ecosystems have severely been destroyed by many anthropogenic and natural factors in recent years. Knowledge of area, distribution, and density of these forests and current natural and human-made land uses inside these ecosystems is necessary for protective management of Zagros forests. This research was performed to obtain the land use and forest density maps in Zagros vegetative area of Fars province in southwestern Iran using Sentinel-2A, vegetation indices, Google Earth, and field data. First the boundary of Zagros forests in Fars province was digitized on Google Earth images. Then, Sentinel-2A satellite images covering the forest region in Fars province were obtained from Copernicus website. A pilot area (16000 ha) in the province was considered to investigate the precision of classification methods. Then, Sentinel-2A satellite image of pilot region was classified by some supervised classification methods (ML: maximum likelihood, MD: minimum distance, MaD: mahalanobis distance, SAM: spectral angle mapper, NN: neural network, SVM: support vector machine, and RF: random forest). In addition, the efficiency of different vegetation indices (NDVI, TNDVI, SAVI, and RVI) was evaluated for classifying the forest density in the pilot region. Furthermore, pilot area was considered to test the precision of Google Earth satellite images in this study. For this purpose, 270 field samples were taken as square plots in nine land uses. Selection of the plots inside each land use was randomly. After initial analysis, all satellite images covering the forest area in Fars province were classified by the most accurate algorithm (SVM) to obtain the land use map. On the other hand, the most accurate vegetation index (SAVI) was used to classify the forest density in the study area. Validation of final maps was performed using several random plots on Google Earth images. The initial results of this research indicated that Google Earth images have an overall accuracy (OA) of 96% compared to ground truth in Fars province. Results of land use map showed that Zagros forest has covered an area of 794651.79 ha in Fars province. The findings of this research demonstrated that SVM algorithm (OA: 92.94%, and k: 0.85) has efficiently classified the land uses in Fars province. In addition, SAVI (r2=0.719, p<0.001) has highly been correlated to the forest density in Zagros vegetative area of Fars province. Therefore, land use mapping using SVM algorithm and Sentinel-2A images, and forest density mapping using SAVI is highly recommended in Zagros forests of Fars province in time series which is essential for protective management of these forests in spatio-temporal scale.

Spatio-temporal land use/land cover change analysis and assessment of urban heat island in Ghana: A focus on the Greater Accra Region

The expansion of cities coupled with economic development over the years has transformed many locations into hotspots for massive urban populace. The Greater Accra Region which is the capital city of Ghana is no exception. The urban population growth rate in the region has expanded extensively at the expense of increasingly vegetation cover. As a result, there is an increasing need to investigate urban resilience, land use/land cover (LULC) change, and urban heat islands (UHI) dynamics in the region. The aim of this study is to analyze multi-spectral Landsat images of 2000 and 2020 to examine the LULC change and the UHI trend across the Greater Accra Region of Ghana. The results from the analysis show a significant change in the spatial trend of land surface temperature (LST) and UHI between the years 2000 and 2020. Spatial distribution of LST from 0°C to 28°C and 17°C to 33°C in the years 2000 and 2020 respectively were observed. Urban areas dominated more than half of the study area in 2020, covering 85% (equivalent to 3160 sq. km), and reflecting a 20% increase from the year 2000 to 2020. During the same period, sparse and dense forested areas decreased by 10% (373 sq. km) and 4% (146 sq. km) with a 5% reduction in bare land (equivalent to 177 sq. km). The spatio-temporal analysis revealed a significant surge in the population of urban areas within the study area. The UHI areas also increased from 13.20% in 2000 to 18.20% in 2020. On the other hand, non-UHI areas decreased from 86.80% to 81.80% during the same period. The successful contribution of this research demonstrates the usefulness of spatial models as tools in generating LULC maps for assessing UHI to facilitate future sustainable city planning.

Integrating UAV LiDAR and multispectral data to assess forest status and map disturbance severity in a West African forest patch

Unmanned aerial vehicle (UAV) technologies have emerged as promising tools to improve forest ecosystem assessments. These technologies offer high-resolution data that can significantly enhance evaluations of forest structure, condition, and disturbance severity. UAV sensors such as LiDAR and multispectral provide complementary information about forest attributes, capturing structural and spectral details, yet their integration for comprehensive forest assessment remains understudied. In this paper, we explored the potential of combining UAV LiDAR and multispectral data to assess the disturbance severity of a West African forest patch (Benin). We developed an integrated disturbance index (IDI) that fuses structural properties from LiDAR data and spectral characteristics from multispectral vegetation indices through principal component analysis (PCA). This allowed us to delineate low (> 0.65), medium (0.35–0.65), and high (< 0.35) forest disturbance levels. We applied the IDI to the 560-ha Ewe-Adakplame relict forest in Benin, West Africa, and achieved 95 % overall accuracy in disturbance detection, outperforming both LiDAR-only (80 %) and multispectral-only (75 %) approaches. The IDI revealed that 23 % of the forest area has experienced low disturbance, while 28 % and 49 % face medium and high disturbance levels, respectively. These findings highlight that more than three-quarters of this relict forest is under considerable stress, underscoring the urgent need for tailored conservation strategies to strengthen forest resilience. This method's ability to differentiate disturbance levels can inform resource allocation, prioritize conservation efforts, and guide the development of site-specific management plans. The integration of UAV LiDAR and multispectral data demonstrated here has potential for application across diverse tropical forest patches, providing an effective means to monitor forest health, assess disturbance severity, and support data-driven decision-making in forest conservation and sustainable management.

Estimation of potential groundwater recharge in a growing touristic neotropical dry forest area

Estimation of potential groundwater recharge in a growing touristic neotropical dry forest area

The Change Detection of Mangrove Forests Using Deep Learning with Medium-Resolution Satellite Imagery: A Case Study of Wunbaik Mangrove Forest in Myanmar

This paper presents the development of a U-Net model using four basic optical bands and SRTM data to analyze changes in mangrove forests from 1990 to 2024, with an emphasis on the impact of restoration programs. The model, which employed supervised learning for binary classification by fusing multi-temporal Landsat 8 and Sentinel-2 imagery, achieved a superior accuracy of 99.73% for the 2020 image classification. It was applied to predict the long-term mangrove maps in Wunbaik Mangrove Forest (WMF) and to detect the changes at five-year intervals. The change detection results revealed significant changes in the mangrove forests, with 29.3% deforestation, 5.75% reforestation, and −224.52 ha/yr of annual rate of changes over 34 years. The large areas of mangrove forests have increased since 2010, primarily due to naturally recovered and artificially planted mangroves. Approximately 30% of the increased mangroves from 2015 to 2024 were attributed to mangrove plantations implemented by the government. This study contributes to developing a deep learning model with multi-temporal and multi-source imagery for long-term mangrove monitoring by providing accurate performance and valuable information for effective conservation strategies and restoration programs.

Determination of Total Flavonoid Content from Extract and Fractions of Mangrove Leaves (Rhizopora mucronata)

Indonesia is an archipelagic country with a very wide coastal area. Lombok Island has a coastal area with quite extensive mangrove forest areas. Rhizopora mucronata was the most dominant species found in the region. The Rhizopora mucronata species was a plant from the Rhizoporaceae family reported to have antibacterial, antifungal, and antioxidant activity. These biological activities were caused by the presence of secondary metabolite compounds in R. mucronata, one of which is flavonoids. Research regarding the flavonoid content of R. mucronata leaves has not been explored. Therefore, the study aimed to determine the total flavonoid content in extracts and fractions of R. mucronata leaves using the spectrophotometric UV-Vis method. R. mucronata mangrove leaves simplicia were extracted using the sonication method using 96% ethanol solvent, then fractionated using the liquid-liquid extraction method with n-hexane, ethyl acetate, and water. The percentage yield of 96% ethanol extract, water fraction, ethyl acetate fraction, and n-hexane fraction respectively were 42.33%; 43.147%; 40.49%; 9.74%; and 32.49%. The TLC test showed the presence of flavonoid compounds detected in the extract and fractions which were marked with blue spots. The total flavonoid contents in the 96% ethanol extract, water fraction, ethyl acetate fraction, and n-hexane fraction were respectively (12,980; 14,160; 23,880; and 25,350) mg Quercetin equivalent/gram sample (mg QE/ g). The total flavonoid content of the extract and fractions of R. mucronata leaves were analyzed using the One-way ANOVA test method. The total flavonoid content in the n-hexane fraction and ethyl acetate fraction was significantly higher than the 96% ethanol extract and water fraction. The total flavonoid content of R. mucronata leaves was relatively high, which contributes to several of its biological activities.

An integrated dataset of ground hydrothermal regimes and soil nutrients monitored in some previously burned areas in hemiboreal forests in Northeast China during 2016–2022

Abstract. Under a warming climate, the occurrence of wildfires has been becoming increasingly more frequent in boreal forests and Arctic tundra over the last few decades. Wildfires can cause radical changes in forest ecosystems and the permafrost environment, such as the irreversible degradation of permafrost, succession of boreal forests, rapid and massive losses of soil carbon stock, and increased periglacial geohazards. Since 2016, we have gradually and more systematically established a network for studying soil nutrients and monitoring the hydrothermal state of the active layer and near-surface permafrost in the northern Da Xing'anling Mountains in Northeast China. Soil moisture content (depth of 0–9.4 m), soil organic carbon content (0–3.6 m), total nitrogen content (0–3.6 m), and total phosphorus and potassium content (0–3.6 m) datasets were obtained in 2016 via field sampling and subsequent laboratory tests. Ground temperature (0–20 m) and active layer thickness (2017–2022) datasets were obtained using thermistor cables that were permanently installed in boreholes or interpolated with these temperatures. The present data can be used to simulate changes in permafrost features under a changing climate and wildfire disturbances and to explore the changing interactive mechanisms of the fire–permafrost–carbon system in hemiboreal forests. Furthermore, they can provide baseline data for studies and action plans to support the carbon neutralization initiative and assessment of the ecological safety and management of the permafrost environment. These datasets can be easily accessed via the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Cryos.tpdc.300933, Li and Jin, 2024).

Climate change is expected to reduce the potential distribution of Ceiba glaziovii in Caatinga, the largest area of dry tropical forest in South America

Ecological niche modeling is a widely used tool to predict species distribution considering current, past, or future climate change scenarios across different geographic areas. Modeling scenarios allow researchers to assess the impacts of climate change on species distribution and identify priority areas for conservation. This study aimed to model the current and future potential distribution of Ceiba glaziovii under different climate change scenarios in Brazil. The MaxEnt algorithm was used to correlate species occurrence points with bioclimatic variables in current and future climate scenarios. Four General Circulation Models (GCMs) from CMIP6 were employed: BCC-CSM2-MR, CNRM-CM6-1, IPSL-CM6A-LR, and MIROC6, considering optimistic and pessimistic projections. The contribution of variables and model accuracy were assessed using the Jackknife statistical test and the Area Under the Curve (AUC) parameter. AUC values for current and future scenarios demonstrated high accuracy. The bioclimatic variables of precipitation and temperature were the main contributors to determining areas with higher habitat suitability. In the future climate scenario, there was a reduction in areas with good climatic suitability for all four GCMs, considering optimistic and pessimistic projections. Among the areas with high habitat suitability, the IPSL-CM6A-1 model in the optimistic projection showed the smallest reduction, while in the pessimistic scenario, all areas with high suitability disappeared. The species' climatic niche is expected to decrease under all tested climate change scenarios. The central areas of the Caatinga and its transition zones exhibit the highest climatic suitability in current and future scenarios and should be prioritized for the species' conservation.

Comparison of the Potential of Mangrove Forests as an Estimate of Carbon Stocks : A Review

Global warming is an increase in carbon dioxide gas (CO2) and other gases in the earth's atmosphere, which will make the earth's atmosphere retain more heat coming from the sun. This study aimed to determine the highest carbon stock ratio from various areas with mangrove forest areas. This research was conducted using the grid route method by collecting data from a literature study from different locations in the mangrove forest. The research results obtained that the highest average carbon stock in various regions of Indonesia which has the highest mangrove forest conservation is the Mangrove Area in Pasar Banggi Rembang Village of 737.2 tons/ha with three stations whose species names consist of Rhizophora apiculata, Rhizophora stylosa, and Rhizophora mucronata. High and low carbon content, density, and mangrove species in mangrove leaf litter were influenced by the rate of litter production. This production rate will later affect the high low carbon content in mangrove leaf litter.

Light-demanding canopy tree species do not indicate past human disturbance in the Yangambi rainforest (Democratic Republic of the Congo)

Key messageIn a former paper, we investigated whether the presence of light-demanding tree species in the forest canopy of the Yangambi Biosphere Reserve (central Congo basin) might be a result of past human disturbances (Luambua et al., Ecol Evol 11:18691–18707, 2021). We focussed on the spatial distribution of the most abundant light demanders, but this approach did not yield conclusive results. In the present study, we focus on all species in the forest and conclude that light demanders are not a transient feature of successional tropical forests but an intrinsic component of old-growth forests in Yangambi.ContextCentral African rainforests are characterised by an abundance of light-demanding tree species, which are aggregated in the canopy but underrepresented in the understorey. A popular explanation is that these forests are recovering from slash-and-burn farming activities preceding the relocation of settlements during the colonial era. In a former paper, we showed that the abundance of light-demanding tree species in the Yangambi Biosphere Reserve (central Congo basin) cannot be unambiguously attributed to past human disturbances, using an approach that focused on the spatial distribution of the most abundant light demanders (Luambua et al., Ecol Evol 11:18691–18707, 2021).AimsAs the former study was inconclusive, the present study aims to further test the assumptions behind the ‘recovery from human disturbance hypothesis’, by considering all species in the forest of Yangambi. We addressed four specific research questions: (i) do light demanders occur in large ‘pockets’ occupying large areas of forest? (ii) Are light demanders abundant? (iii) Do they exhibit a regeneration deficit? (iv) Is species composition in pockets of light demanders different from the surrounding forests?MethodsWe identified the location and size of pockets of light demanders in several transects cumulating to 50 km. We installed permanent inventory plots within and outside these pockets and calculated the diameter and age distributions of light demanders within each pocket. We assessed whether pockets of light demanders are different from surrounding forests, using plot clustering analysis.ResultsOur results showed that light demanders were aggregated, but the pockets were small, scarce, and represent a minor fraction of the total forest area. Furthermore, light demanders were not abundant, even in pockets where they were aggregated. Their age distributions did not show a regeneration deficit. Finally, species composition in pockets of light demanders did not differ substantially from surrounding forests where they were scarce or absent.ConclusionsWe conclude that light-demanding canopy species do not indicate past human disturbance in Yangambi and that they are an intrinsic component of old-growth forests rather than a transient feature of successional forests. Our insights show that the large carbon sink observed in mature forests in this region is not driven by successional forest dynamics.

The invasion of Japanese hop (Humulus japonicus) in a restored floodplain forest

Abstract Japanese hop (Humulus japonicus Sieb. &amp; Zucc.) is an emerging invasive plant that has been observed to invade and spread throughout wetlands. As an annual vine, H. japonicus can smother native vegetation, forming dense stands and reducing biodiversity. At a restored floodplain forest in Joslin, Illinois, formerly used as an experimental site to test the effectiveness of different reforestation methods, H. japonicus has invaded stands of the previously dominant invasive, reed canarygrass (Phalaris arundinacea L.). We conducted an observational field study to examine the spatio-temporal dynamics of H. japonicus invasion relative to gradients in canopy cover and species composition. Ten transects, with half the transect extending into and half extending beyond H. japonicus patches, were established in October 2022. Seven quadrats per transect were surveyed for vegetation cover and canopy cover in October 2022, June 2023, and October 2023. Transects were evenly split between forested and open areas based on the reforestation treatments. H. japonicus cover significantly increased from October 2022 to October 2023, which has resulted in a slight decrease and replacement of P. arundinacea across the site. Shade reduced H. japonicus cover, indicating its preference for sunlit conditions. Species richness was higher in forested transects compared to open ones, most likely due to the absence of both P. arundinacea and H. japonicus in shaded transects. Along transects, quadrats that had been invaded by H. japonicus differed in species composition from quadrats that had not been invaded in both October 2022 and October 2023. H. japonicus cover was much lower in June than October, suggesting that temporal niche partitioning may allow P. arundinacea to persist, and indicating that monitoring for H. japonicus should occur late in the growing season. Both invasive species are shade intolerant, suggesting that planting fast-growing trees should be an effective long-term solution for controlling invasion.

Spatiotemporal Variations in Gross Ecosystem Product and Its Relationship with Economic Growth in Ecologically Vulnerable Watershed Areas: A Case Study of Yongding River Basin

Ecosystem service value is crucial for balancing economic growth and ecological preservation in ecologically vulnerable watershed areas. Although Gross Ecosystem Product (GEP) has received significant attention, most existing studies have focused on how to measure it. Few studies have explored spatiotemporal variations in GEP and how land-use changes affect these variations regarding ecological restoration at the river basin level. Additionally, while many studies have examined the relationship between ecosystem service value and economic growth, there is little research on how components of GEP influence economic growth. Analyzing the spatiotemporal structure of GEP and its components could offer new insights into optimizing ecological restoration strategies and promoting sustainable development in vulnerable watershed regions. In this study, we used ArcGIS, InVEST, SPSS, and Python to analyze spatiotemporal variations in GEP in the Yongding River Basin within the Beijing–Tianjin–Hebei Economic Region from 1995 to 2020. Moran’s Index and variance decomposition were applied to analyze the spatiotemporal structure. The grey prediction model forecasted GEP trends from 2025 to 2035. The random forest model was used to assess land-use changes’ impacts on GEP. Paired T-tests were used to compare GEP and GDP, and a dynamic panel model was used to examine how ecosystem service value factors influenced economic growth. The results show the following: (1) Regarding values, GEP accounting and variance decomposition results indicated that ecosystem cultural service value (ECV) and ecosystem regulating service value (ERV) each contributed about half of the total GEP. Ecosystem provisioning service value (EPV) showed an upward trend with fluctuations. Regarding the spatial distribution, Moran’s I analysis showed significant positive spatial correlations for EPV and ERV. The grey prediction model results indicated significant growth in GEP from 2025 to 2035 under current ecological restoration policies, especially for ERV and ECV. (2) In terms of the influence of land-use changes, random forest analysis showed that the forest land area was consistently the most influential factor across GEP, EPV, and ERV. Unused land area was identified as the most significant factor for ECV. (3) Before 2010, GEP was larger than GDP, with significant differences between 1995 and 2000. From 2010 onwards, GDP surpassed GEP, but the differences were not statistically significant. Dynamic panel regression further showed that the water conservation value significantly boosted GDP, whereas the water purification value significantly reduced it. This study highlights the importance of integrating GEP into ecological restoration and economic development to ensure the sustainability of ecologically vulnerable watershed areas.

Forest Enclave Potential Assessment for Nature Conservation and Recreation in City with Dysfunctional Natural System

Forests are an important component of the natural systems of large cities. Their deficiency limits the proper functioning of these systems and creates a shortage of recreational places for residents needing contact with nature. This can be encountered in Rzeszów, a city in southern Poland with 200,000 inhabitants, which is characterised by the smallest forest area (2.8%) of all province capitals in the country. The natural state of small forest enclaves in this city has not been fully investigated. This has led to incorrect management and nature conservation policies. Therefore, the aim of this research conducted since 2015 was to recognise the potential for conservation and recreational functions using the concept of ecosystem services (ES) and ecosystem disservices (EDS), and to develop recommendations for the optimal use of these areas. This article analyses and compares 14 indicators related to ES and EDS of three out of five larger forest patches in the city. The results of this analysis indicate that the current method for protecting these valuable elements of the natural system does not correspond with their actual conservation potential. The application of the ES and EDS concepts to assess the nature conservation potential allowed us to determine it as high in two forest enclaves where such potential had not been indicated so far, and to identify the highest recreational potential in one of the investigated sites.

Landscape-level analysis of disturbance regimes in Chinnar Wildlife Sanctuary, a protected area of Western Ghats, India

ABSTRACT The degradation, fragmentation, and depletion of forests have occurred all over the world, simultaneously, with the exponential growth of the human population. The present study assesses the forest disturbance regime of Chinnar Wildlife Sanctuary (CWS), an ecologically significant biodiversity hotspot of Western Ghats (WG) through satellite image-based forest and land cover mapping and landscape metric analysis. The on-screen analysis based on field knowledge, facilitated, mapping of seven types of vegetation cover. The majority of forest area was covered by dry deciduous forest (39.2 km2 or 43.15%), whereas the minimum area was covered by disturbed forest (2.05 km2 or 2.26%). The disturbance index (DI) was calculated by linearly combining the land use land cover (LULC), road and settlement closeness, fire risk, porosity, patchiness interspersion, normalized difference vegetation index (NDVI), and fragmentation through a probabilistic weight-based linear additive model. The values of the DI (120–310) reclassified into four classes viz. low, moderate, high, and very high. The results revealed that 74.62 km2 (82.52%) of the forest area has low to moderate disturbance, while the rest of the forest area that is 15.8 km2 (17.48%) has high to very high disturbance. These analyses are based on remote sensing and GIS crucial for forest conservation and management strategies formulation.

Declining interest in afforestation under the common agricultural policy. Evidence from Poland and Lithuania

Land afforestation is an important aspect of forested land development. Increasing the area of forest areas through the reforestation of uncultivated, abandoned or agriculturally unsuitable land is considered an important way to diversify economic activities in order to reduce dependence on agricultural activities and improve environmental conditions in rural areas. The main objective of the study is to identify the factors affecting the afforestation of agricultural land carried out in the years 2004–2020 by farmers under the individual financial perspectives of the Rural Development Programme (RDP) in Poland and Lithuania. The study included a review of Polish and Lithuanian regulations aimed at providing financial support for afforestation under the RDP. Moreover, a comparative analysis of the rules and criteria for financial support for afforestation in relation to selected socio-economic indicators of the two countries was carried out. Based on the study results, it can be clearly stated that in both Poland and Lithuania, the support for afforestation under the RDP fails to meet the beneficiaries’ expectations. It would, therefore, be advisable to adapt the Programme to the changing economic conditions and keep the afforested land under the RDP under technical supervision. Support for afforestation should be continued to ensure the improvement in land use and the enhancement of the prospects for long-term economic activity in rural areas as well as to implement the assumptions of the green economy.