Forest Distribution Research Articles

Optimising forest rehabilitation and restoration through remote sensing and machine learning: Mapping natural forests in the eThekwini Municipality

Forests are crucial in delivering ecosystem services that underpin human well-being and biodiversity conservation. However, these vital ecosystems are threatened by forest degradation and rapid urbanisation. This study addresses this challenge by proposing a comprehensive framework for mapping natural forests at the municipal scale. The framework integrates remote sensing techniques with machine learning algorithms to provide valuable insights into the extent of natural forests within the eThekwini Municipality. The study utilised Landsat 7, Landsat 8, and Landsat 9 satellite imagery to analyse and map the historical and current distribution of natural forests. Five spectral indices, namely, Normalized Differential Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Chlorophyll Index Green (CIG), Enhanced Vegetation Index (EVI), and Enhanced Vegetation Index-2 (EVI-2), which were calculated from Landsat bands, were employed in the analysis. Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), and Extreme Gradient Boosting (XGBoost) machine learning algorithms were used to model forest distribution. Accuracy was assessed through confusion matrices, Receiver Operating Characteristic (ROC) Curves, area under the ROC curve (AUC), and the F1 scores. LightGBM achieved the highest overall accuracy (90.76%), followed by CatBoost (89.56%) and XGBoost (84.34%). LightGBM also obtained the best F1 score (90.76%). These findings highlight LightGBM's effectiveness in classifying natural forests, making it the preferred model for mapping the historical extent of natural forests in the eThekwini Municipality. However, classifications based on Landsat 7 significantly underestimated the extent of natural forests within the study area, whereas Landsat 8 and Landsat 9 data revealed an increase in natural forests from 2015 to 2023. These findings will guide effective and targeted forest rehabilitation and restoration efforts, ensuring the preservation and enhancement of forest ecosystem services.

A Multi-Baseline Forest Height Estimation Method Combining Analytic and Geometric Expression of the RVoG Model

As an important parameter of forest biomass, forest height is of great significance for the calculation of forest carbon stock and the study of the carbon cycle in large-scale regions. The main idea of the current forest height inversion methods using multi-baseline P-band polarimetric interferometric synthetic aperture radar (PolInSAR) data is to select the best baseline for forest height inversion. However, the approach of selecting the optimal baseline for forest height inversion results in the process of forest height inversion being unable to fully utilize the abundant observation data. In this paper, to solve the problem, we propose a multi-baseline forest height inversion method combining analytic and geometric expression of the random volume over ground (RVoG) model, which takes into account the advantages of the selection of the optimal observation baseline and the utilization of multi-baseline information. In this approach, for any related pixel, an optimal baseline is selected according to the geometric structure of the coherence region shape and the functional model for forest height inversion is established by the RVoG model’s analytic expression. In this way, the other baseline observations are transformed into a constraint condition according to the RVoG model’s geometric expression and are also involved in the forest height inversion. PolInSAR data were used to validate the proposed multi-baseline forest height inversion method. The results show that the accuracy of the forest height inversion with the algorithm proposed in this paper in a coniferous forest area and tropical rainforest area was improved by 17% and 39%, respectively. The method proposed in this paper provides a multi-baseline PolInSAR forest height inversion scheme for exploring regional high-precision forest height distribution. The scheme is an applicable method for large-scale, high-precision forest height inversion tasks.

Improving extraction of forest canopy height through reprocessing ICESat-2 ATLAS and GEDI data in sparsely forested plain regions

ABSTRACT Forest canopy height (FCH) is one of the most important variables for carbon stock estimation. While many studies have focused on extracting FCH from spaceborne LiDAR in regions with spatially continuous and large patch sizes of forested lands, limited research has addressed the challenges of FCH extraction in plain regions with sparse and fragmented forest distributions. In this study, we proposed innovative processing approaches to extract FCH from ICESat-2 photons and GEDI footprints in the plain regions of Anhui Province, China. Specifically, we proposed a sectional photon denoising method for processing ICESat-2 data and a geolocation error correction method for processing GEDI data. Airborne LiDAR data were used to validate the extracted FCH products across typical plain regions. The results demonstrated the effectiveness of the proposed methods in improving FCH extraction accuracy. Evaluation indicated that the directly extracted FCH products from ATL08 and GEDI L2A had Pearson’s correlation coefficients (r) of 0.6 and 0.93, respectively. After processing with the proposed methods, the 2019 FCH products from ICESat-2 exhibited r of 0.82 and relative root mean square error (rRMSE) of 31.11% based on 3,217 ICESat-2 segments, and the products from GEDI showed r of 0.96 and rRMSE of 18.35% based on 4,862 GEDI footprints. Further application of these methods to extract FCH products from GEDI and ICESat-2 for the years 2020, 2021, and 2022 indicated their promise for addressing sparse vegetation coverage in plain regions.

Machine learning-based monitoring of mangrove ecosystem dynamics in the Indus Delta

Mangrove forests play a vital role in carbon sequestration, typhoon-induced wave attenuation, and the provision of ecological services. However, mangrove ecosystems have experienced large-scale loss globally due to rising sea levels and anthropogenic activities. This study investigates the dynamic changes in mangrove cover within the mega-Indus delta, the largest delta in Pakistan and Southern Asia, using multi-temporal remote sensing data and machine learning techniques from 1988 to 2023. The results indicate an increasing trend in mangrove areas in the Indus Delta, with an average annual growth rate of 18.72 %. The spatial distribution of mangrove forests tends to concentrate towards the landward areas, extending along tidal channels, while losses primarily occur in the seaward regions. Rising sea levels pose a potential threat to the survival of these mangroves. The strong southwest monsoon-driven waves are the leading cause of shoreline erosion of the Indus Delta mangroves. Meanwhile, the reduction in riverine sediment discharge is not associated with the increase in mangrove area. Instead, the tidal currents influenced by the southwest monsoon carry sediments into the delta’s tidal channels, causing them to fill and create suitable habitats for mangroves, which are the primary drivers of the observed mangrove expansion in the Indus Delta. Additionally, afforestation activities observed in the northwest and southwest parts of the study area have contributed to the restoration of mangroves. The loss of mangroves in the northernmost part of the northwest region was attributed to an oil spill incident. This study highlights the dynamic nature of mangrove ecosystems in the Indus Delta, characterized by an arid climate and low population density. The findings provide valuable insights into the factors influencing mangrove gain and loss and can inform management strategies for global mangrove restoration efforts.

Phylogenetic relationships and historical biogeography of silkmoths (Lepidoptera: Bombycidae) suggest an origin in Southern Gondwana

Silkmoths (Bombycidae) have a disjunct distribution predominantly in the Southern Hemisphere and Asia. Here we reconstruct the phylogenetic history of the family to test competing hypotheses on their origin and assess how vicariance and long-distance dispersal shaped their current distribution. We sequenced up to 5,074 base pairs from six loci (COI, EF1-α, wgl, CAD, GAPDH, and RpS5) to infer the historical biogeography of Bombycidae. The multilocus dataset covering 20 genera (80 %) of the family, including 17 genera (94 %) of Bombycinae and 3 genera (43 %) of Epiinae, was used to estimate phylogenetic patterns, divergence times and biogeographic reconstruction. Dating estimates extrapolated from secondary calibration sources indicate the Bombycidae stem-group originated approximately 64 Mya. The subfamilies Epiinae (South America) and Bombycinae (Australia, Asia, East Palaearctic, and Africa) were reciprocally monophyletic, diverging at c. 56 Mya (95 % credibility interval: 66–46 Mya). The ‘basal’ lineage of Bombycinae — Gastridiota + Elachyophtalma — split from the rest of Bombycinae c. 53 Mya (95 % credibility interval: 63–43 Mya). Gastridiota is a monobasic genus with a relictual distribution in subtropical forests of eastern Australia. The Oriental and African genera comprised a monophyletic group: the Oriental region was inferred to have been colonized from a long-distance dispersal event from Australia to South-East Asia c. 53 Mya or possibly later (c. 36–26 Mya); Africa was subsequently colonized by dispersal from Asia c. 16 Mya (95 % credibility interval: 21–12 Mya). Based on the strongly supported phylogenetic relationships and estimates of divergence times, we conclude that Bombycidae had its origin in the fragment of Southern Gondwana consisting of Australia, Antarctica and South America during the Paleocene. The disjunction between South America (Epiinae) and Australia (Bombycinae) is best explained by vicariance in the Eocene, whereas the disjunct distribution in Asia and Africa is best explained by more recent dispersal events.

Multi-objective optimization of forest structure for broadleaf mixed forest under different CMIP5 scenarios

Climate change is an important global research topic and one of the most pressing issues facing the world today. It significantly impacts forest distribution, species composition, stand structure, growth, and other functions, therefore, it is crucial to simulate the dynamic changes of forest structure under future climate change and develop optimized solutions to adapt to it. Combined with the various stand structural parameters, based on the equal or unequal weights of each parameter at the stand level, to establish a dynamic stand spatial structure multi-objective optimization index (L-index) under different climate changes, and to implement broadleaf mixed forest spatial structure optimization research in Yanan Forest Farm under RCP 2.6, RCP 4.5, RCP 8.5 climate scenarios after 5 years. The results indicated that the L-index showed an increase ranging from 13.8 % to 35.7 % under equal weights in different climate scenarios. Additionally, the optimal harvesting intensity ranged from 13.2 % to 20.5 %, with the highest intensity observed in the RCP4.5 climate scenario; compared to the RCP4.5 and RCP8.5 climate scenarios, the RCP2.6 scenario shows the largest trend and magnitude of change in the indicators, suggesting that the state of stand growth is more sensitive under the RCP2.6 scenario. The L-index showed an increase ranging from 13.7 % to 34.4 % under the unequal weights in different climate scenarios, in which the optimal harvesting intensity ranged from 14.6 % to 19.8 %. More importantly, it should be noted that the values of the increases in the L-index and the relative increased proportion (RIP) were both higher than those when unequal weights were employed. Moreover, it should be highlighted that the increments in the L-index and RIP were both greater than those observed when unequal weights were applied for all three plots under different climate scenarios. Overall, the adjusted the complete mixing degree index (Mc) increased, angle index (W), dominance ratio index (U), and Hegyi competition index (CI) decreased compared to before optimizing with the equal or unequal weights of the stand structural variable, which is evident that the adjustments made to the stand’s structure have resulted in a significant improvement. Thus, the multi-objective optimization for forest spatial dynamics constructed is both reasonable and effective, and it more scientific forest management under different climatic scenarios.

Dispersal limits poleward expansion of mangroves on the west coast of North America

While much attention has been paid to the climatic controls of species' range limits, other factors such as dispersal limitation are also important. Temperature is an important control of the distribution of coastal mangrove forests, and mangrove expansion at multiple poleward range limits has been linked to increasing temperatures. However, mangrove abundances at other poleward range limits have been surprisingly insensitive to climate change, indicating other drivers of range limitation. For example, along the west coast of North America, the poleward mangrove range limits are found on the Baja California and mainland coasts of Mexico, between 26°48ʹ and 30°18ʹN. Non‐climatic factors may play an important role in setting these range limits as 1) the abundance of range limit populations has been relatively insensitive to climate variability and 2) an introduced population of mangroves has persisted hundreds of kilometers north of the natural range limits. We combined a species distribution model with a high‐resolution oceanographic transport model to identify the roles of climate and dispersal limitation in controlling mangrove distributions. We identified estuarine habitat that is likely climatically suitable for mangroves north of the current range limits. However, propagules from current mangrove populations are unlikely to reach these suitable locations due to prevailing ocean currents and geomorphic factors that create a patchy distribution of estuarine habitat with large between‐patch distances. Thus, although climate change is driving range shifts of mangroves in multiple regions around the world, dispersal is currently limiting poleward mangrove expansion at several range limits, including the west coast of North America.

Warming stimulates mangrove carbon sequestration in rising sea level at their northern limit: An in situ simulation

AbstractGlobal warming and sea‐level rise are directly influencing the growth, distribution, and greenhouse gas emissions of mangrove forests. However, mangrove forests growing at their latitudinal limits are relatively susceptible to warming; nevertheless, few studies have focused on GHG emissions of latitudinal limits mangrove forests as related to global climate change. To address this knowledge gap, a multiyear in situ control experiment was established in a restored plantation at the northern distribution limit of Kandelia obovata, the most cold‐tolerant mangrove species in China, to simulate both warming and sea‐level rise. We investigated the growth patterns and sediment greenhouse gas emissions of a K. obovata population and identified the primary factors contributing to these changes. The results showed that warming and moderate sea‐level rise enhanced biomass by more than 18%, indicating that warming stimulated plant growth while excessive sea‐level rise inhibited it. The sediment greenhouse gas emissions ranged from 45.5 to 484.6 mgCO2 m−2 h−1, 5.6 to 590.3 μgCH4 m−2 h−1, and 11.4 to 385 μgN2O m−2 h−1, which increased with warming while decreased with sea‐level rise, acting as the net source of greenhouse gas emission. Our study predicted that sea‐level rise, while directly changing sediment properties, had combined effects with warming on these studied mangrove forests that were predicted to emit more greenhouse gases from sediments in the future. These findings indicated that K. obovata plantations within their latitudinal limits tend to accumulate more CO2 for biomass carbon storage under warming conditions, while stimulating sediment greenhouse gas emissions, which will offset their climate mitigating effect in future climatic scenarios.

Mercury distribution and transfer in mangrove forests in urban areas under simulated rising sea levels

Mercury distribution and transfer in mangrove forests in urban areas under simulated rising sea levels

Effect of climate change on current and future potential distribution of Strawberry tree (Arbutus unedo L.) in Türkiye

The demand for nontimber forest products (NTFPs) has increased significantly in recent years. Hundreds of plant species that grow naturally in Türkiye have medicinal and aromatic value. Medicinal and aromatic plants are primarily used as a sources of tea, spices, condiments and essential oils. Species belonging to the genus Arbutus L. are used for decorative purposes and as fuel wood in many wood-based industries, they also have a wide range of uses in packaging, chairs making and furniture production. Additionally, the fruits of these trees are widely consumed by humans and animals because they are rich in sugar and vitamin C. It is predicted that changes in climatic conditions will significantly change the distribution, composition and function of forests threatening biodiversity. The purpose of this study was to model current and future potential geographical distributions of Arbutus unedo L., which is among the species that naturally grow in Türkiye and is of substantial value in terms of its ecological contribution to forest ecosystems, based on species presence data and environmental variables (bioclimatic variables and altitude). The current and future distribution area models for Arbutus unedo L. indicate that the potential distribution areas of the species in the coming years will gradually decrease, and in the SSP5 8.5 model, which represents the highest level of world resource usage this gradual decrease will reach its highest level and there will be no suitable distribution area left for the species. Therefore, it is predicted that the species will become endangered. In-situ and ex-situ conservation measures need to be taken to ensure the sustainability of the species in forestry and landscape areas.

Extraction of 10 m Resolution Global Mangrove in 2022

With the intensification of global climate change, there is an increasing emphasis on protecting natural resources. Mangrove forests, critical to tropical and subtropical intertidal ecosystems, have garnered considerable attention in recent years for their strong carbon sink capacity, rich species diversity, and abundant natural resources. This study utilizes the 2020 global mangrove vector data as a baseline to construct a reasonable buffer zone by calculating the increase in mangrove crown width. The Google Earth Engine (GEE) platform and its Sentinel-2 data from 2022 are employed to acquire synthetic images across all regions using the mosaic algorithm. Then, mangrove forests are extracted using the Otsu algorithm, and a map depicting the global spatial distribution of mangrove forests in 2022 is obtained. The average overall accuracy of the extracted mangrove forests in this study reaches 92.4%, and it is determined that the global mangrove forest area expanded by 4920.6 km2 between 2020 and 2022, This study provides crucial data support for the global monitoring of mangrove changes and holds significant importance for protecting and restoring mangrove ecosystems.

The distribution and drivers of tree cover in savannas and forests across India

The distribution of forest and savanna biomes and the role of resources (climate and soil) and disturbances (fire and herbivory) in determining tree-grass dynamics remains elusive and variable across geographies. This is especially problematic in Indian savannas which have been historically misclassified as degraded forests and are targeted for tree-planting. Here, we examine biome distribution and determinants through the lens of tree cover across India. Our analyses reveal four distinct zones of differing tree cover, with intermediate zones containing savanna vegetation. Rainfall seasonality determines maximum possible tree cover non-linearly. Once rainfall seasonality is factored out, soil sand fraction and topography partially explain residual variation of tree cover. High domestic livestock herbivory and other anthropogenic pressures reduce tree cover. Lastly, lack of detectable fires precludes robust conclusions about the relationship between fire and tree cover. By considering these environmental drivers in restoration planning, we can improve upon simplistic tree planting initiatives that may be detrimental to Indian savannas.

Spatial Pattern of Forest Age in China Estimated by the Fusion of Multiscale Information

Forest age is one of most important biological factors that determines the magnitude of vegetation carbon sequestration. A spatially explicit forest age dataset is crucial for forest carbon dynamics modeling at the regional scale. However, owing to the high spatial heterogeneity in forest age, accurate high-resolution forest age data are still lacking, which causes uncertainty in carbon sink potential prediction. In this study, we obtained a 1 km resolution forest map based on the fusion of multiscale age information, i.e., the ninth (2014–2018) forest inventory statistics of China, with high accuracy at the province scale, and a field-observed dataset covering 6779 sites, with high accuracy at the site scale. Specifically, we first constructed a random forest (RF) model based on field-observed data. Utilizing this model, we then generated a spatially explicit forest age map with a 1 km resolution (random forest age map, RF map) using remotely sensed data such as tree height, elevation, meteorology, and forest distribution. This was then used as the basis for downscaling the provincial-scale forest inventory statistics of the forest ages and retrieving constrained maps of forest age (forest inventory constrained age maps, FIC map), which exhibit high statistical accuracy at both the province scale and site scale. The main results included the following: (1) RF can be used to estimate the site-scale forest age accurately (R2 = 0.89) and has the potential to predict the spatial pattern of forest age. However, (2) owing to the impacts of sampling error (e.g., field-observed sites are usually located in areas exhibiting relatively favorable environmental conditions) and the spatial mismatch among different datasets, the regional-scale forest age predicted by the RF model could be overestimated by 71.6%. (3) The results of the downscaling of the inventory statistics indicate that the average age of forests in China is 35.1 years (standard deviation of 21.9 years), with high spatial heterogeneity. Specifically, forests are older in mountainous and hilly areas, such as northeast, southwest, and northwest China, than in southern China. The spatially explicit dataset of the forest age retrieved in this study encompasses synthesized multiscale forest age information and is valuable for the research community in assessing the carbon sink potential and modeling carbon dynamics.

Estimated distribution of high nature value forest in the Republic of Ireland

High Nature Value (HNV) farmland and forest systems play a vital role in supporting biodiversity and delivery of ecosystem services. Estimates of HNV forest area and distribution in the European Union (EU) are rarely conducted, despite having been a requirement of Rural Development Programmes. This work represents the first attempt to identify and estimate the area of HNV forest in the Republic of Ireland in a repeatable and transparent way. Relevant geo-datasets available for Ireland were collated and analysed. We investigated whether the datasets contained information on the indicators used in a recently-developed Nature Value (NV) index, and explored the potential of proxy indicators to determine the likelihood of a mapped area of forest being HNV. Based on these analyses, a likelihood map of the distribution of forest in different NV categories was produced and an accuracy assessment conducted. Results from this study suggest that HNV forest accounts for approximately 1 % of the Irish land area, or 8 % of the total forest area. Accuracy assessments indicated substantial agreement between the likelihood map classifications and the calculated NV status of National Forest Inventory plots. The methodology presented here could also be applied to existing similar datasets to estimate the extent and distribution of HNV forest in other regions. The mapped output provides a likelihood of a forest area being HNV and can provide evidence to inform the development of forest conservation policies.

Применение методов пространственного анализа при оценке степени горимости лесов Хабаровского края

Forest fires are the dominant factor in the transformation of natural landscapes in the boreal zone. The area of the Khabarovsk Territory, 93.6 % of which belongs to the forest fund lands, currently has a forest cover of only 66.5 %, which is largely due to forest fires. This makes urgent the problem of reducing the forest fire frequency in the region, including in terms of assessing the actual fire frequency for predicting the risks of landscape fires. The article shows the possibilities of using spatial analysis methods of the Spatial Analyst module of the ArcGIS 10.8 software package to assess the degree of actual forest fire frequency based on calculating the number of fires per 1 million and 250 thousand ha and the area of fires per 1000 ha for a sliding window of a given size. Based on the data from the Remote Monitoring Information System of the Federal Forestry Agency for 2000–2020 and the data on the number of fires from the Fire Information for Resource Management System website for 2000–2021, an assessment of the actual fire frequency for the Khabarovsk Territory has been carried out. The analysis of the results reflects the high spatial and seasonal heterogeneity of the spread of landscape fires in the territory and their proximity to transport and residential structures. Areas with high fire frequency often do not coincide with the boundaries of forestry enterprises, and the maximum indicators are typical for non-forest areas that are not covered by forestry statistics. For different seasons of the year, along with quantitative differences, spatial shifts of firing points and areas with their high density are also observed. Most of the lands in the region (88.3 %) in terms of fire danger belong to the “low” and “below medium” fire frequency categories, which is primarily due to the low degree of development of these areas. The assessment of the degree of fire frequency in the territory on the basis of annual and seasonal maps of the density of firing points, calculated using the sliding window method, makes it possible to fairly objectively and in detail reflect the average long-term risk of fire occurence outside of reference to forestry boundaries. In combination with a map of the distribution of forests by natural fire hazard classes based on pyrological characteristics, the results of the reseаrch can become the basis for calculating the background fire risk, reflecting the risk of fire occurrence and development.

Unique Plant Resources and Distribution Patterns in the Valley Forest of the Irtysh River Basin.

The river valley forests of the Irtysh River Basin are a germplasm bank of Salicaceae species and rare plant resources in China, and the distribution varies with the river and is highly distinctive. However, there is a dearth of systematic research on the characteristics of plant resources. In this study, a comprehensive investigation was conducted in the trunk stream and six tributaries with valley forest distribution in the Irtysh River Basin, and 244 quadrats were set up. The analysis focused on the composition of the flora and resource characteristics. The results reveal the following: (1) The valley forests of the Irtysh River Basin contain 256 species of plants belonging to 57 families and 178 genera, among which 19 species of trees, 23 species of shrubs, and 214 species of herbs were investigated. (2) Among the identified species, 226 (88.67%) were recognized as resource plants, with medicinal plants being the most abundant (176 species, 68.75% of the total). (3) The distribution patterns of trees, shrubs, and herbs of each resource type vary across rivers. Elevation drop, river length, and river distance all significantly affect the number of specie. This study elucidated the current status and distributional characteristics of plant resources in the valley forests of the Irtysh River Basin, which is essential for both biodiversity conservation and sustainable resource utilization.

Developing a smart forest protection mini program with Spring Boot: Integrating big data and image recognition technologies

Forests are essential for ecological stability yet pose significant management challenges due to their expansive and remote nature. Despite advancements in data acquisition, there is a lack of tools that effectively combine image recognition with big data analytics for real-time monitoring. This study introduces a "Smart Forest Protection" mini program, developed using Spring Boot, which integrates these technologies to facilitate immediate and efficient forest management. The program features intelligent plant recognition, user-driven incident reporting, waste recycling point displays, and a platform for disseminating forest conservation knowledge. Additionally, a big data visualization platform illustrates the spatiotemporal distribution of forests in Zhuhai, aiding governmental and environmental agencies in making informed decisions that enhance forest conservation and promote sustainable practices.

The relationship between structure and ecosystem services of forest and grassland based on pattern analysis method: A case study of the Mongolian Plateau

Global warming has led to severe land desertification on the Mongolian plateau. It puts great environmental pressure on vegetation communities. This pressure leads to fragmentation of land use and landscape patterns, thus triggering changes in the spatial distribution patterns of vegetation. The spatial distribution pattern of vegetation is crucial for the performance of its ecosystem services. However, there is not enough research on the relationship between large-scale spatial distribution patterns of vegetation and ecosystem services. Therefore, this study is to construct an ecological spatial network on the Mongolian Plateau based on landscape ecology and complex network theory. Combining pattern analysis methods to analyze the network, we obtained the spatial and temporal trends of forest and grass spatial distribution patterns from 2000 to 2100, and explored the relationship between the topological properties of source patches and ecosystem services in different patterns. It was found that there are four basic patterns of spatial distribution of forest and grass in the Mongolian Plateau. The Core-Linked Ring pattern accounts for 40.74 % and exhibits the highest stability. Under the SSP5-RCP8.5 scenario, source patches are reduced by 22.76 % in 2100. Topological indicators of source patches showed significant correlations with ecosystem services. For example, the CUE of grassland patches in the Centralized Star pattern was positively correlated with betweeness centrality. The most significant improvement in WUE after optimization is 19.90 % compared to pre-optimization. The conclusion of the study shows that the spatial distribution pattern of vegetation can be used to enhance the stability of ecological spatial network and improve ecosystem services at a larger scale. It can provide a certain reference for the study of spatial patterns of vegetation distribution in arid and semi-arid areas.

Elevational distribution of forests and its spatiotemporal dynamics in subtropical China from 2000 to 2019

Elevational distribution of forests and its spatiotemporal dynamics in subtropical China from 2000 to 2019

A Big History of Land Clearance and Deforestation

The gathering and hunting humans who evolved from earlier manifestations of Homo changed the distribution of forests on the planet through their use of fire to direct biological productivity to their sustenance, and through their contribution to the elimination of much of the global terrestrial megafauna. Land clearance at any scale awaited the development of agriculture, the several independent origins of which may indicate that it is an emergent outcome from the combination of a social animal who can transmit knowledge through generations and who lives in environments that support high numbers of food plants. The transition from uncleared forest and treeless land to land cleared for agriculture was slow, often reversed, and limited by the necessity to produce more energy in food production than in the inputs that created comestibles. Increases in cleared land until the nineteenth century were largely a product of the displacement of gathering and hunting people by disease-ridden European agriculturalists and world trade imposed on non-Europeans by colonialists. The explosion in fossil fuel usage from the nineteenth century onwards enabled exponential growth in human populations and cleared land, with the consequence of a crash in forest cover. Ironically, attempts to mitigate global warming caused by increased fossil fuel use, deforestation and land clearance have resulted in more land clearance for biofuels. While settlements, roads, logging, plantation establishment and dam construction have all contributed to the decrease in the native terrestrial cover of the planet, their contribution has been minor compared to the massive impact of agricultural development.