Forest Cover Research Articles

Determination of land surface temperatures for some oak stands with Landsat 8 OLI satellite images: a case study from Turkey.

This study investigates the effect of forest cover, development stage and aspect factors on surface temperature (LST) in Demirköy and Düzce Forest Enterprises (FE) using Landsat 8 OLI satellite imagery from July 2021. In Demirköy FE, the highest LST value was recorded in the district center with 47.4 °C, in Düzce FE, the highest LST value was recorded in the provincial center with 44.6 °C, while the lowest value was 14.3°C in Demirköy FE and 13.02 °C in Düzce FE in densely forested areas. When the stands were evaluated, it was found that pure juvenile oak stands had the highest average LST value with 30.48 °C in Demirköy FE and 23.96 °C in Düzce FE, whereas lower LST values were observed in old oak stands due to more dense cover. In terms of aspect, higher temperatures were recorded in the southern and western aspects in both study areas. In Düzce FE, high LST values were also observed in juvenile stands in the northern direction, indicating the influence of local factors such as topography and solar radiation angle. The highest LST values were observed in open forest areas; 6-9 °C higher temperatures were found in Demirköy and 1-5 °C higher in Düzce compared to vegetated areas. Significant correlations were found between LST and vegetation indices, and the highest correlation was found between LST and Normalized Difference Structuring Index (NDBI) (R2, 0.54 in Demirköy and R2, 0.50 in Düzce). NDVI showed a negative relationship with LST (R2, 0.47 in Demirköy, R2, 0.43 in Düzce). These findings reveal the critical role of forest density, tree species, and forest management practices in regulating surface temperatures, especially in the context of urbanization and deforestation.

Forest Management Assessment Using LASSO and the Critical List of Variables for Sustaining the Commons: Four Cases in the Monarch Reserve

In the Monarch Reserve, four cases were selected for exploratory analysis using the critical list of variables for sustaining the commons and LASSO regression to determine which of these are most influential in the successful maintenance of forest cover over a decade. Forest recovery from degraded to dense forest is found to be inversely related to resource size, consistent with theory. However, in four other categories of forest change, the size of the resource is less influential, though it is still a significant, variable with a positive relationship to forest cover change which is inconsistent with theory. The analysis also showed that less overlap, greater poverty and lower dependence on the resource are associated both with forest gain and with mitigation of forest loss, which is contrary to theory and most research. Also associated with poverty in the LASSO regression are homogeneity of identity and ease of enforcement of rules. These show a negative relation to transitions from non-forest to dense forest, with low coefficients. This is inconsistent with theory. Possible reasons for the divergence from theory are discussed in detail, as is the utility of this approach for assessment and monitoring.

Determination of forest canopy cover of different sized stands with diverse structure using ALS data: case study of the Białowieża Forest (Poland)

ABSTRACT The need for objective methods to determine tree canopy cover (CC) across large numbers of stands has led to the development of techniques that utilise airborne laser scanning (ALS) data, which provides a reproducible and detailed representation of canopy geometry. We developed a method for determining CC area and evaluated the estimation accuracy for stands of different sizes, structure and composition. This method is based on tree crown geometries obtained from ALS data, and verified with field measurements using data for 3245 stands of the Białowieża Forest District in Poland. In relatively large stands (3–5 ha), the theoretical error of prediction decreased from 0.13 to 0.10 with increasing stand area. In stands larger than 10 ha, however, the error in estimating CC was less than ±0.10. Although every estimation method comes with its own assumptions and errors, the presented method eliminates the subjectivity in observer bias prevalent in traditional field-based ocular assessments and provides a more transparent and methodologically uniform approach for estimating CC in forests.

Examining thedrivers of forest cover change and deforestation susceptibility in Northeast India using multicriteria decision-makingmodels.

The increasing rates of forest cover change and heightened vulnerability to deforestation present significant environmental challenges in Northeast India. This study investigates the dynamics of forest cover changeand susceptibility to deforestation in this region from 2001 to 2021, utilizing data from the Hansen Global Forest Change (HGFC) productonthe Google Earth Engine (GEE) platform. A suite of multicriteria decision-making (MCDM) models-including VlseKriterijumska optimizacija I Kompromisno Resenje (VIKOR), Simple Additive Weighting (SAW), Evaluation Based on Distance from Average Solution (EDAS), and Weighted Aggregates Sum Product Assessment (WASPAS)-was employed to assess changes in forest cover and deforestation susceptibility across varied zones. Multicollinearity tests confirmed the relevance of the factors influencing deforestation. Statistical validations, such as the Wilcoxon Signed Ranks Test, underscored the models' robustness, revealing statistically significant outcomes. Additionally, Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) analysis demonstrated the superior fit of the VIKOR model (AUC = 0.938) compared to SAW (AUC = 0.901), EDAS (AUC = 0.895), and WASPAS (AUC = 0.864) in predicting current deforestation susceptibility. Validation affirmed the reliability of all MCDM methods, with VIKOR displaying high sensitivity (True Positive Rate, TPR = 0.878) and optimal AUC (0.938). Correlation analyses among the models identified significant inter-relationships, notably a positive correlation between EDAS and SAW, and a negative correlation between VIKOR and SAW. The regions of Assam, Nagaland, Mizoram, and Arunachal Pradesh were identified as experiencing significant forest cover loss, indicating a pronounced susceptibility to future deforestation. These findings underscore the need for immediate intervention to address this critical environmental issue.

Playing with fire: Socioeconomic factors and deliberate wildfires in Texas

Deliberately setting fires, also known as arson, is one of the primary causes of human-caused wildfires in the US. Arson fires pose a significant threat, contributing to the human-caused wildfire problem in the southern US. Since more than 90% of wildfires in the South are human-caused, it is particularly important to examine the factors that influence the distribution of arson wildfires in this region. While most research has combined arson fires together with other human-caused wildfires, a smaller body of studies has focussed specifically on fires started by arson. The focus of this study is to understand the key factors associated with the distribution of intentional wildfires in Texas. We examine the relationship between a combination of key socioeconomic factors and environmental factors and, arson wildfires using Negative Binomial models. As expected, our results indicate that road density, unemployment rate, and crime rate are positively associated with arson fires, and the number of law enforcement officers and wage rate are negatively associated with arson wildfire incidences. The higher percentage of forest cover in a county and higher temperature regimes are also positively associated with arson fire occurrences. As such, we conclude that arson wildfires are more likely in areas with high accessibility to wildlands. Similarly, counties with high unemployment rates and consequently high crime rates are more likely to suffer from arson wildfires. Our results clearly show that a higher presence of law enforcement officers in a county deter arsonists. Similarly, financial well-being as indicated by the higher wage rates in a county plays an apparent role in curbing arson wildfire occurrences. Thus, the results from this study provide valuable insights to policymakers, land managers, and fire prevention specialists to develop targeted interventions aimed at reducing arson risk in Texas and beyond.

Towards ecological civilization: Spatiotemporal heterogeneity and drivers of ecological quality transitions in China (2001–2020)

Global urbanization and climate change have a profound influence on the ecological quality (EQ) of China. In this study, utilizing the Google Earth Engine, we calculated the spatiotemporal heterogeneity of the China Remote Sensing Ecological Index (RSEI) for the period 2001–2020. We analyzed its drivers using land use, socioeconomic, and climate data. According to the results, the national average RSEI values for 2001, 2010, 2016, and 2020 were 0.39, 0.41, 0.46, and 0.45, respectively, and the proportions of the moderate and upper grades were 54 % in 2001, 64 % in 2010, 76 % in 2016, and 73 % in 2020. The RSEI value in the forest cover area was higher than that in the urban built-up and non-vegetation cover area by 0.1–0.2. The correlation coefficients between each variable and RSEI presented a ladder distribution (along the trend distribution of the Huanyong line). Moreover, maximum temperature (Tmmx) consistently contributed the most to RSEI (the contribution rate was between 35 % and 40 %), followed by precipitation accumulation (Pre, the contribution rate was between 18 % and 28 %), and then DEM, GDP, population (PPP), and wind speed (VS), all with relatively lower contributions around 10 %. Furthermore, temperature surpassing 24 °C, precipitation below 90 mm, population exceeding 50, or GDP above 10,000 showed a negative correlation with RSEI. This study analyzed the regional differences in RSEI drivers in different regions of China, providing a reference for local targeted improvement measures.

Correlation between Different Factors of Non-point Source Pollution in Yangtze River Basin

This research used the improved export coefficient model to estimate non-point source load in sub-watersheds and a self-organizing map model to identify the correlation relationship of factors, that can be affected by non-point source load in Yangtze River Basin. Research results indicate that total nitrogen load is 4.87 - 15.53 kg/ha; total phosphorus load is 0.18 - 0.51 kg/ha; Dong Ting Lake sub-basin has the lowest contribution value of total nitrogen and phosphorus; Tai Lake sub-basin has the highest contribution load of total nitrogen and total phosphorus; Jinsha River sub-basin reveals the highest value of total nitrogen and phosphorus contribution on grassy land and desert land; Tai Lake sub-basin has the highest total nitrogen and total phosphorus load on forest, agricultural land, and urban construction land. The correlation relationship of factors reveals a very close correlation with each other as precipitation, total nitrogen load, and total phosphorus load factors have correlation in the highest value, in which total nitrogen load and total phosphorus load factors have a close correlation with each other from the lowest value to highest value; wetland and barren land factors correlate with the lowest value and the highest value; agricultural land, forestland, and grassy land factors correlate with one another in the high value and the highest value. The forest cover rate factor, as well as the population factor, does not correlate with other factors.

Environmental impacts of the billion tree Tsunami project in Khyber Pakhtunkhwa on the dynamics of Agro-Meteorological Droughts

Changes in forest cover are closely associated with the variability in meteorological and hydrological variables. Therefore, this study delves into investigating how forest cover changes impact the environment (i.e., hydro-meteorological variables, including precipitation, streamflow, relative humidity (RH), evapotranspiration (ET), and temperature) using Trend Projection (TP) methods during 1980–2019. The study is carried out in the Khyber Pakhtunkhwa (KP) province of Pakistan, which witnessed deforestation between 1980 and 2010 followed by afforestation (through billion tree tsunami project, BTTP) initiated in 2014. A new drought index, named as agro-meteorological drought index (AMDI), is developed in this study using the remotely sensed data to analyze the impact of forest cover on drought severity. The robust least square regression (RLSR) model is used to regress the normalized difference vegetation index (NDVI) with AMDI at various time scales to investigate the impact of forest cover on drought severity. The RLSR and paired t-test are used to quantify the impact of forest cover and BTTP, in particular, on the environment. Land use maps prepared for KP province over a span of the past four decades revealed significant deforestation during 1985–2005, transitioning gradually to afforestation in the past decade. Results indicated a decline in streamflow throughout different seasons with an increase in forest cover, particularly during the period of afforestation (i.e., 2015–2019). The precipitation, RH (maximum/minimum), and ET displayed an increasing trend over time, whereas a decrease trend is observed in Tmax/Tmin and streamflow at most of the stations. The trend analyses depicted a significant change before and after the BTTP. The paired t-test results revealed that BTTP has statistically significant impact on the environmental variables. Furthermore, the time series plots of AMDI at different time scales indicated that drought events were frequent and severe prior to 2003, whereas significant decrease in both the frequency and severity of drought was observed in the last decade (2010–2019). The RLSR results at pixel scales demonstrated the crucial role of forest covers in alleviating both the frequency and severity of drought events. The elasticities revealed that increase in the forest cover resulted in substantial increase/decrease in each hydro-meteorological variable. Overall, the results highlighted a positive and statistically significant impact of forest cover (i.e., BTTP) on both the environment and drought variability.

Identification of dominant drivers of streamflow spatiotemporal variations in typical mountainous areas in the Hexi Corridor, China

Study regionTypical mountain areas in the Hexi Corridor, China. Study focusWater security and ecosystem sustainability of arid inland river basins are highly dependent on upstream streamflow. However, due to the complex geographical environment and limited observation data in the study region, the attribution of spatiotemporal variations in streamflow influenced by climate change and/or human activities remains unclear. Here, we used partial least squares regression (PLSR) and the Budyko framework to unravel the dominant drivers of spatiotemporal variation in streamflow over the past 30 yr. New hydrological insight for the regionPrecipitation, topographic wetness index, slope, forest land, gross primary productivity, hydrological connectivity, soil organic carbon content, silt content, relative relief, NDVI and gravel content dominated spatial variation in streamflow. Temporal variation of streamflow was sensitive to precipitation and land surface. Specifically, increased precipitation and land surface alteration dominated the increase in streamflow in 50 % of the watersheds and the decrease in streamflow in 33 % of them, respectively. Further, land surface alteration was dominated by expansion of agricultural and built-up areas, weakened hydrological connectivity, increased landscape aggregation and forest cover. Controlling agricultural and built-up areas and the scale of afforestation, and focusing on the dynamics of hydrological connectivity and landscape patterns in the upstream reaches are imperative to maintain the security and sustainability of water resources in the arid inland river basins.

Impact Assessment of Forest Degradation on Peoples Livelihood: The Case of Harena Buluq Woreda Bale Eco Region, Southeast Ethiopia

In developing countries like Ethiopia, forest degradation, characterized by the decline in forest cover and diminished capacity to provide essential goods and services, presents significant environmental, social, and economic challenges. The purpose of this study is to assess the effects of forest degradation on the way of life for the people living in the southeast Ethiopian woreda of Harena Buluq. Examining how forest degradation affects local livelihoods with a particular emphasis on livelihood strategies and the degree of reliance on forest resources is the main goal. In order to obtain qualitative insights, data was gathered by means of a combination of secondary sources and key informant interviews, which were complemented by on-site observations of the consequences of forest degradation. After gathering, the data was examined and shown. The findings underscore the multifaceted impact of forest degradation on the socio-economic fabric of communities in Harena Buluq, highlighting the urgent need for targeted interventions to mitigate its adverse effects and promote sustainable livelihood practices.

Comparative effect of traditional and collaborative watershed management approaches on flood components

AbstractIdentifying the critical areas of flood generation and determining the optimal measures for flood control and management (FCM) is one of the most important basics of watershed management. Therefore, the current study was carried out to prioritize sub‐watersheds (SWs) based on flood generation using physical (Ph‐MA), technical (Te‐MA), comanagerial (Com‐MA), and conjunct management approaches (Con‐MA), as well as determining the quantitative effects of the proposed FCM measures in the Cheshmeh‐Kileh Watershed, Iran. To prioritize SWs based on flood generation, geo‐environmental criteria were used in Ph‐MA, HEC‐HMS software was used in Te‐MA, and semi‐structured interviews with local stakeholders were used in Com‐MA. Finally, using the Condorcet algorithm based on game theory, SWs were prioritized in each approach. In the semi‐structured interviews, stakeholders were asked to provide suggested measures for FCM. Finally, the effect of each measure on the flood components was quantified. Based on the results, the measure of 10% improvement of forest cover in the entire watershed from the category of Con‐MA was selected as the optimal measure. In the return periods of 2, 5, 10, 20, 50, and 100 years, the effectiveness of this measure was 80.08%, 70.09%, 58.10%, 55.67%, 50.58%, and 43.62%, respectively. The influence of Con‐MA based on non‐structural and structural measures on reducing the components of peak flow and flood volume was more than other approaches. In general, it can be concluded that non‐structural measures have more effect than structural measures in FCM in the study watershed. The methods and approaches used in this study can be directly used by local executive managers, decision‐makers, and policymakers of watershed management and flood management activities.

Ecological change increases malaria risk in the Brazilian Amazon

Ecological change in the Brazilian Amazon is closely linked to human mobility and health. Mining, agriculture, logging, and other activities alter highly diverse ecological and demographic contexts and subsequent exposure to diseases such as malaria. Studies that have attempted to quantify the impact of deforestation on malaria in the Brazilian Amazon have produced conflicting results. However, they varied in methodology and data sources. Most importantly, all studies used annual data, neglecting the subannual seasonal dynamics of malaria. Here, we fill the knowledge gap on the subannual relationship between ecological change in the Brazilian Amazon and malaria transmission. Using the highest spatiotemporal resolution available, we estimated the effect of deforestation on malaria cases between 2003 and 2022 using a stratified Bayesian spatiotemporal hierarchical zero-inflated Poisson model fitted with the Integrated Nested Laplace Approximation. The model was also stratified by state. We found that a 1% increase in 1-mo lagged deforestation increased malaria cases in a given month and municipality by 6.3% [95% credible interval (Crl): 6.2, 6.5%]. Based on an interaction term included in the model, the effect of deforestation on malaria was even larger in areas with higher forest cover. We found that the coefficients for deforestation and mobility were highly variable when stratified by state. Our results provide detailed evidence that, on average, deforestation increases malaria transmission, but that the relationship is not spatiotemporally uniform. These results have implications for stratifying malaria control interventions based on ecological dynamics to help Brazil achieve its goal of malaria elimination by 2035.

Hierarchical Analysis of Miombo Woodland Spatial Dynamics in Lualaba Province (Democratic Republic of the Congo), 1990–2024: Integrating Remote Sensing and Landscape Ecology Techniques

Lualaba Province, located in the southeastern Democratic Republic of the Congo (DRC), consists of five territories with varied dominant land uses: agriculture (Dilolo, Kapanga, and Musumba in the west) and mining (Mutshatsha and Lubudi in the east). The province also includes protected areas with significant governance challenges. The tropical dry forests that cover the unique Miombo woodland of Lualaba are threatened by deforestation, which poses risks to biodiversity and local livelihoods that depend on these forests for agriculture and forestry. To quantify the spatio-temporal dynamics of Lualaba’s landscape, we utilized Landsat images from 1990 to 2024, supported by a Random Forest Classifier. Landscape metrics were calculated at multiple hierarchical levels: province, territory, and protected areas. A key contribution of this work is its identification of pronounced deforestation trends in the unique Miombo woodlands, where the overall woodland cover has declined dramatically from 62.9% to less than 25%. This is coupled with a marked increase in landscape fragmentation, isolation of remaining woodland patches, and a shift toward more heterogeneous land use patterns, as evidenced by the Shannon diversity index. Unlike previous research, our study distinguishes between the dynamics in agricultural territories—which are particularly vulnerable to deforestation—and those in mining areas, where Miombo forest cover remains more intact but is still under threat. This nuanced distinction between land use types offers critical insights into the differential impacts of economic activities on the landscape. Our study also uncovers significant deforestation within protected areas, underscoring the failure of current governance structures to safeguard these critical ecosystems. This comprehensive analysis offers a novel contribution to the literature by linking the spatial patterns of deforestation to both agricultural and mining pressures while simultaneously highlighting the governance challenges that exacerbate landscape transformation. Lualaba’s Miombo woodlands are at a critical juncture, and without urgent, coordinated intervention from local and international stakeholders, the ecological and socio-economic foundations of the region will be irreversibly compromised. Urgent action is needed to implement land conservation policies, promote sustainable agricultural practices, strengthen Miombo woodland regulation enforcement, and actively support protected areas.

A multi-scale, multivariate habitat selection model demonstrates high potential for the reintroduction of the clouded leopard Neofelis nebulosa to Taiwan

Abstract Hunting, habitat loss and fragmentation have caused a rapid decline in the distribution and abundance of the clouded leopard Neofelis nebulosa across its range, and in several areas, including Taiwan, the species is now extirpated. Taiwan, a former stronghold for the species, is a candidate for its reintroduction, based on increasing prey abundance and high forest coverage. Such future reintroduction efforts, however, are hampered by a lack of analysis of potential clouded leopard habitat on the island. To address this, we explore habitat suitability for the species in Taiwan. We used a multi-scale, multivariate habitat selection model based on clouded leopard presence–absence data from extensive camera-trap surveys across its current range to predict suitable habitat in Taiwan. Our findings indicate that 38% of Taiwanese territory is potentially suitable habitat for the clouded leopard, of which 46% is under protection. This demonstrates the high potential of Taiwan's habitat for clouded leopard reintroduction.

Drivers of wind and water erosion for river sediments in a typical coarse sandy area in the middle reaches of the Yellow River

Warmer temperatures and the combined effects of wind and water erosion leads to serious soil loss. Identifying the contribution of different drivers to wind and water compound erosion can improve soil erosion management in the watershed. Accordingly, we calculated the erosion energy based on energy theory and applied the mutation test and trend analysis to explore environmental drivers, runoff, and sediment changes for the Kuyehe River Basin. Rainfall erosion energy, wind erosion energy, air temperature, NDVI, and land cover were selected as potential drivers to quantify the contribution of sediment change using a partial least squares regression model. The results indicated that rainfall erosion energy decreased, wind erosion energy increased and then decreased, temperature increased, and NDVI increased during the period 1990–2020. The relationship between runoff and sediment transport in the basin shifted from more water and more sediment to more water and less sediment, and the timing of the mutation was different for runoff and sediment, with the runoff mutation occurring in 1997 and the sediment mutation occurring during 2003–2004. After sediment mutations, wind, NDVI, and land cover of forests showed higher importance for sediment transport changes, while precipitation and temperature were less important. In addition, the importance of cropland, shrub and grassland, and other land cover types varied considerably between sub-basins. Over 70 % of the contribution to sediment change was due to land cover change, while the cumulative contribution of forest and NDVI was about 30 %. The study identified the key drivers of sediment transport changes in the basin and provided valuable insights for future studies of wind and water compound erosion at the basin scale.

DASR-Net: Land Cover Classification Methods for Hybrid Multiattention Multispectral High Spectral Resolution Remote Sensing Imagery

The prompt acquisition of precise land cover categorization data is indispensable for the strategic development of contemporary farming practices, especially within the realm of forestry oversight and preservation. Forests are complex ecosystems that require precise monitoring to assess their health, biodiversity, and response to environmental changes. The existing methods for classifying remotely sensed imagery often encounter challenges due to the intricate spacing of feature classes, intraclass diversity, and interclass similarity, which can lead to weak perceptual ability, insufficient feature expression, and a lack of distinction when classifying forested areas at various scales. In this study, we introduce the DASR-Net algorithm, which integrates a dual attention network (DAN) in parallel with the Residual Network (ResNet) to enhance land cover classification, specifically focusing on improving the classification of forested regions. The dual attention mechanism within DASR-Net is designed to address the complexities inherent in forested landscapes by effectively capturing multiscale semantic information. This is achieved through multiscale null attention, which allows for the detailed examination of forest structures across different scales, and channel attention, which assigns weights to each channel to enhance feature expression using an improved BSE-ResNet bilinear approach. The two-channel parallel architecture of DASR-Net is particularly adept at resolving structural differences within forested areas, thereby avoiding information loss and the excessive fusion of features that can occur with traditional methods. This results in a more discriminative classification of remote sensing imagery, which is essential for accurate forest monitoring and management. To assess the efficacy of DASR-Net, we carried out tests with 10m Sentinel-2 multispectral remote sensing images over the Heshan District, which is renowned for its varied forestry. The findings reveal that the DASR-Net algorithm attains an accuracy rate of 96.36%, outperforming classical neural network models and the transformer (ViT) model. This demonstrates the scientific robustness and promise of the DASR-Net model in assisting with automatic object recognition for precise forest classification. Furthermore, we emphasize the relevance of our proposed model to hyperspectral datasets, which are frequently utilized in agricultural and forest classification tasks. DASR-Net’s enhanced feature extraction and classification capabilities are particularly advantageous for hyperspectral data, where the rich spectral information can be effectively harnessed to differentiate between various forest types and conditions. By doing so, DASR-Net contributes to advancing remote sensing applications in forest monitoring, supporting sustainable forestry practices and environmental conservation efforts. The findings of this study have significant practical implications for urban forestry management. The DASR-Net algorithm can enhance the accuracy of forest cover classification, aiding urban planners in better understanding and monitoring the status of urban forests. This, in turn, facilitates the development of effective forest conservation and restoration strategies, promoting the sustainable development of the urban ecological environment.

Co-Management Effects on Forest Restoration in Protected Areas of Bangladesh: A Remote Sensing and GIS-Based Analysis

Co-management is a promising forest governance strategy that integrates local communities’ traditional rights and forest dependencies while aiming to improve forest cover and ecosystem health. Bangladesh, facing high deforestation rates and limited per capita forest area, has implemented co-management initiatives since 2003 to restore forest cover and support the livelihoods of forest-dependent communities. While the socio-economic impacts of co-management are well studied, its effects on forest cover remain underexplored. This study addresses that gap by using three common spectral vegetation indices (NDVI, EVI, and MSAVI), calculated from Landsat 7 data, to analyze forest cover changes in five major protected areas under co-management. The results indicated that dense forest cover (41–71%) was initially prevalent in these areas, but a significant decline occurred between 2004 and 2015, with slope values ranging from −3.7 to −0.96. In contrast, the non-co-managed control site exhibited a much smaller decline (slope: −0.48 to −0.62) across all indices. Notable increases in agricultural land and forest–agriculture mosaics were also observed in the protected areas under co-management. Global Forest Watch data further confirmed substantial forest cover loss, particularly in CWS (158.77 ha) and SNP (0.49 ha). These findings highlight the need to reassess co-management strategies to address ongoing forest degradation.

How does the value of ecological restoration techniques affect ecosystem carbon density: A resource endowment-based perspective

In the context of carbon neutrality and carbon peaks, innovations in ecological restoration technology play a key role in carbon adsorption and maintaining ecological balance. Regional governments, tasked with formulating differentiated and rational ecologically sustainable development plans and carbon-neutral strategies tailored to various resource endowment conditions, require timely identification of the mechanisms by which ecological restoration technologies affect the carbon density of ecosystems. Based on data from 31 provinces in China from 2006 to 2021, this study employed the Spatial Durbin Model with both time and space fixed effects to examine the impact of the value of ecological restoration technologies on ecosystem carbon density under varying resource endowments. The empirical results indicate that (1) the value of ecological restoration technology has a significantly positive effect on ecosystem carbon density, particularly in neighbouring areas; (2) energy resource endowment exhibits a significantly inverted U-shaped boundary effect, whereby regions with moderate energy endowment can optimise the positive impact of ecological restoration technology; (3) the heterogeneity analysis, based on levels of regional environmental governance and forest cover, shows that in regions with less investment in environmental infrastructure and lower forest cover, ecosystem carbon density is more sensitive to the value of ecological restoration techniques, making their carbon sink effect more pronounced. This study elucidates the key role of ecological restoration techniques in enhancing carbon storage, promoting the goal of carbon neutrality, and identifying optimal pathways for increasing ecosystem carbon stocks across various regions with differing resource endowments. Moreover, it provides a reference for accelerating the achievement of carbon neutrality targets.

Влияние насаждений Quercus robur L. и Juglans rupestris Engelm. на свойства степных почв

The aim of the study has been to determine the condition of Quercus robur L. and Juglans rupestris Engelm. and their influence on the properties of segregated chernozems. The study was conducted in 2020–2021 in the steppe Crimea. The control has been a perennial grass fallow. To characterize the general condition of the trees, a 4-point scale for assessing the condition of the introduced species has been used. Standard methods have been used for soil analysis. A better preservation and a greater number of 50-year old plants in excellent and good condition have been established for Q. robur (67 и 56 %, respectively) compared to J. rupestris (48 и 42 %, respectively) in these environmental conditions, which is explained by the greater adaptive capabilities of oak as an indegenous species. Both tree crops have influenced soil properties. In their phytocenoses, a litter has been formed, more significant under J. rupestris. The dark humus horizon has also turned out to be thicker under the J. rupestris plantation (80 cm) than under the Q. robur plantation and the fallow (72 and 70 cm, respectively). Under Q. robur the soil density has been lower than under J. rupestris by 0.02–0.18 g/cm3. Under the woody plants, the soil structure has improved: the number of lumpy aggregates has decreased, and the number of agronomically valuable and granular particles has increased. The maximum coefficient of structure has been in the soil under the oaks (4.4–6.6), which exceeded the control by 2.5–4.9 times. Under the woody plants, carbonates have been significantly washed out of the soil profile, especially under J. rupestris. The pHH2O value of the alkaline soil under the woody plants has decreased by 0.2–0.4 and has been closely related to carbonate content (r = 0.83–0.91). The humus content under both species has been significantly higher than under the fallow – by 0.30–0.86 %. Afforestation has resulted in the accumulation of total forms of nutrients, to a greater extent under J. rupestris, and the C:N ratio has decreased. Tree species have stored more labile forms of nutrients in the soil. Soil desalination has been noted in the tree cenoses. Thus, J. rupestris and Q. robur are ecologically stable in the conditions of the central steppe of Crimea and can be used to increase forest cover in steppe areas. This will help improve soil fertility.

Urban forest cover and ecosystem service response to fire varies across California communities

Urban fires that result from wildfires are an emerging, extreme event affecting communities and urban forests globally. However, much of the fire effects on urban ecosystems literature is primarily focused on Wildland-Urban Interface (WUI) areas, correlates of building loss, risk mitigation, and wildland vegetation and fuels. Three recent urban fires in California USA provided an opportunity to explore these effects on urban forests: the 2017 Tubbs (Santa Rosa) and Thomas (Ventura) fires and the 2018 Camp fire that burned Paradise. Accordingly, we analyzed pre- and post-fire neighborhood level urban tree cover (nUTC) change over 5 years using Sentinel and LiDAR data (10 m resolution). Then, we explored the effects of fire severity on changes in several regulating ecosystem services (e.g., carbon, air pollution, stormwater). Findings from the Rapid Assessment of Vegetation Condition after Wildfire severity processes and other geospatial datasets show that fire effects were patchy with fire severity within neighborhoods ranging from unburned to extreme. We found that ∼5 years after the fires, and relative to adjacent non-fire affected neighborhoods, Ventura and Santa Rosa’s nUTC is recovering to pre-fire levels while Paradise’s nUTC is being consistently lost over time. Ventura and Santa Rosa had 20–25 % of their fire affected area outside established WUI boundaries. However, local-scale urban tree cover and ecosystem service supply are lagged, and recovery time depends on the surrounding biome and socio-ecological context. In inland, higher elevation communities – like Paradise – the recovery of nUTC and the associated ecosystem services might materialize over a much longer timeframe. Our study provides a roadmap to assess the response of urban wildfire-affected tree cover and ecosystem services across space and time. It is also one of the first assessments of fire effects on urban ecosystems and communities outside of WUI boundaries that are now experiencing increased threat from wildfires globally.