Search Results (36)

Human–wildlife conflict (HWC) is a growing concern in the Nuwara Eliya Divisional Secretariat Division (DSD) in the central highlands of Sri Lanka. This study investigates the nature and distribution of HWC, with particular focus on agricultural damage, livestock losses, infrastructure destruction, and human injuries. Data were collected through field surveys, expert opinions, satellite imagery, and census data, including interviews with 720 farmers (conducted between 2021 and 2022) and 25 online questionnaires, which provided expert insights on HWC. Animals such as wild boars, bandicoots, barking deer, toque macaques, porcupines, buffaloes, sambar, and leopards were found to be key to HWC, contributing to crop raiding, livestock predation, and infrastructure damage, and through the analytical hierarchy process (AHP), the wild boar was determined to have the greatest impact. Spatial analysis revealed conflict hotspots near forest and tea plantation boundaries, emphasizing the influence of land use and proximity to wildlife habitats. Mitigation strategies were explored; most farmers utilize multiple conflict reduction strategies, with varying efficacy. These findings underline the importance of developing region-specific strategies for HWC management, promoting sustainable agricultural practices, and fostering coexistence between wildlife and local communities. Full article

Agroforestry systems are recognized as sustainable land use practices that foster environmental health and promote adaptive responses to global change. By harnessing the synergies between trees and agricultural activities, agroforestry systems provide multiple benefits, including soil conservation, biodiversity enhancement, and carbon sequestration. Windbreaks form integral elements of Hungarian agricultural landscapes, and the enhanced agroforestry subsidy framework might have a favorable impact on their expansion, underscoring the importance of evaluating their potential for carbon sequestration. In the present study, we assess the implications of doubling the extent of windbreak plantations in Hungary by planting an additional 14,256 hectares of windbreaks. We evaluate the total carbon sequestration and the annual climate change mitigation potential of the new plantations up to 2050. For the modeling, we use the recently developed Windbreak module of the Forest Industry Carbon Model, which is a yield table-based model specific to Hungary and allows for the estimation of living biomass, dead organic matter, and soil carbon balance. We project that new windbreak plantations will sequester 913 kt C by 2050, representing an average annual climate change mitigation potential of 144 kt CO₂ eq. Our findings reveal that doubling the extent of windbreak plantations could achieve an extra 5% carbon sequestration in forested areas as compared to business-as-usual (BAU) conditions. We conclude that new windbreak plantations on agricultural field boundaries have substantial climate change mitigation potential, underscoring agroforestry’s contribution to agricultural resilience and achieving Hungary’s climate goals set for the land-use (LULUCF) sector. Full article

Black locust (Robinia pseudoacacia L.), one of the major afforestation species adopted in vegetation restoration, is notable for its rapid root growth and drought resistance. It plays a vital role in improving the natural environment and soil fertility, contributing significantly to soil and water conservation and biodiversity protection. However, compared with natural forests, due to the low diversity, simple structure and poor stability, planted forests including Robinia pseudoacacia L. are more sensitive to the changing climate, especially in the aspects of growth trend and adaptive range. Studying the ecological characteristics and geographical boundaries of Robinia pseudoacacia L. is therefore important to explore the adaptation of suitable niches to climate change. Here, based on 162 effective distribution records in China and 22 environmental variables, the potential distribution of suitable niches for Robinia pseudoacacia L. plantations in past, present and future climates was simulated by using a Maximum Entropy (MaxEnt) model. The results showed that the accuracy of the MaxEnt model was excellent and the area under the curve (AUC) value reached 0.937. Key environmental factors constraining the distribution and suitable intervals were identified, and the geographical distribution and area changes of Robinia pseudoacacia L. plantations in future climate scenarios were also predicted. The results showed that the current suitable niches for Robinia pseudoacacia L. plantations covered 9.2 × 10⁵ km², mainly distributed in the Loess Plateau, Huai River Basin, Sichuan Basin, eastern part of the Yunnan–Guizhou Plateau, Shandong Peninsula, and Liaodong Peninsula. The main environmental variables constraining the distribution included the mean temperature of the driest quarter, precipitation of driest the quarter, temperature seasonality and altitude. Among them, the temperature of the driest quarter was the most important factor. Over the past 90 years, the suitable niches in the Sichuan Basin and Yunnan–Guizhou Plateau have not changed significantly, while the suitable niches north of the Qinling Mountains have expanded northward by 2° and the eastern area of Liaoning Province has expanded northward by 1.2°. In future climate scenarios, the potential suitable niches for Robinia pseudoacacia L. are expected to expand significantly in both the periods 2041–2060 and 2061–2080, with a notable increase in highly suitable niches, widely distributed in southern China. A warning was issued for the native vegetation in the above-mentioned areas. This work will be beneficial for developing reasonable afforestation strategies and understanding the adaptability of planted forests to climate change. Full article

Plantation distribution information is of great significance to the government’s macro-control, optimization of planting layout, and realization of efficient agricultural production. Existing studies primarily relied on high spatiotemporal resolution remote sensing data to address same-spectrum, different-object classification by extracting phenological information from temporal imagery. However, the classification problem of orchard or artificial forest, where the spectral and textural features are similar and their phenological characteristics are alike, still presents a substantial challenge. To address this challenge, we innovatively proposed a multi-index entropy weighting DTW method (ETW-DTW), building upon the traditional DTW method with single-feature inputs. In contrast to previous DTW classification approaches, this method introduces multi-band information and utilizes entropy weighting to increase the inter-class distances. This allowed for accurate classification of orchard categories, even in scenarios where the spectral textures were similar and the phenology was alike. We also investigated the impact of fusing optical and Synthetic Aperture Radar (SAR) data on the classification accuracy. By combining Sentinel-1 and Sentinel-2 time series imagery, we validated the enhanced classification effectiveness with the inclusion of SAR data. The experimental results demonstrated a noticeable improvement in orchard classification accuracy under conditions of similar spectral characteristics and phenological patterns, providing comprehensive information for orchard mapping. Additionally, we further explored the improvement in results based on two different parcel-based classification strategies compared to pixel-based classification methods. By comparing the classification results, we found that the parcel-based averaging method has advantages in clearly defining orchard boundaries and reducing noise interference. In conclusion, the introduction of the ETW-DTW method is of significant practical importance in addressing the challenge of same-spectrum, different-object classification. The obtained orchard distribution can provide valuable information for the government to optimize the planting structure and layout and regulate the macroeconomic benefits of the fruit industry. Full article

The precise acquisition of phenotypic parameters for individual trees in plantation forests is important for forest management and resource exploration. The use of Light-Detection and Ranging (LiDAR) technology mounted on Unmanned Aerial Vehicles (UAVs) has become a critical method for forest resource monitoring. Achieving the accurate segmentation of individual tree crowns (ITCs) from UAV LiDAR data remains a significant technical challenge, especially in broad-leaved plantations such as rubber plantations. In this study, we designed an individual tree segmentation framework applicable to dense rubber plantations with complex canopy structures. First, the feature extraction module of PointNet++ was enhanced to precisely extract understory branches. Then, a graph-based segmentation algorithm focusing on the extracted branch and trunk points was designed to segment the point cloud of the rubber plantation. During the segmentation process, a directed acyclic graph is constructed using components generated through grey image clustering in the forest. The edge weights in this graph are determined according to scores calculated using the topologies and heights of the components. Subsequently, ITC segmentation is performed by trimming the edges of the graph to obtain multiple subgraphs representing individual trees. Four different plots were selected to validate the effectiveness of our method, and the widths obtained from our segmented ITCs were compared with the field measurement. As results, the improved PointNet++ achieved an average recall of 94.6% for tree trunk detection, along with an average precision of 96.2%. The accuracy of tree-crown segmentation in the four plots achieved maximal and minimal R² values of 98.2% and 92.5%, respectively. Further comparative analysis revealed that our method outperforms traditional methods in terms of segmentation accuracy, even in rubber plantations characterized by dense canopies with indistinct boundaries. Thus, our algorithm exhibits great potential for the accurate segmentation of rubber trees, facilitating the acquisition of structural information critical to rubber plantation management. Full article

Forests are essential for maintaining the ecological balance of the planet and providing critical ecosystem services. Amidst an increasing rate of global forest loss due to various natural and anthropogenic factors, many countries are committed to battling forest loss by planting new forests. Despite the reported national statistics on the land area in plantations, accurately delineating boundaries of planted forests with remotely sensed data remains a great challenge. In this study, we explored several deep learning approaches based on Convolutional Neural Networks (CNNs) for mapping the extent of planted forests in the Korean Peninsula. Our methodology involved data preprocessing, the application of data augmentation techniques, and rigorous model training, with performance assessed using various evaluation metrics. To ensure robust performance and accuracy, we validated the model’s predictions across the Korean Peninsula. Our analysis showed that the integration of the Near Infrared band from 10 m Sentinel-2 remote sensing images with the UNet deep learning model, incorporated with unfrozen ResNet-34 backbone architecture, produced the best model performance. With a recall of 64% and precision of 76.8%, the UNet model surpassed the other pixel-based deep learning models, including DeepLab and Pyramid Sense Parsing, in terms of classification accuracy. When compared to the ensemble-based Random Forest (RF) machine learning model, the RF approach demonstrates a significantly lower recall rate of 55.2% and greater precision of 92%. These findings highlight the unique strength of deep learning and machine learning approaches for mapping planted forests in diverse geographical regions on Earth. Full article

Tropical peatlands in Southeast Asia have experienced widespread subsidence due to forest clearance and drainage for agriculture, oil palm and pulp wood production, causing concerns about their function as a long-term carbon store. Peatland drainage leads to subsidence (lowering of peatland surface), an indicator of degraded peatlands, while stability/uplift indicates peatland accumulation and ecosystem health. We used the Advanced Pixel System using the Intermittent SBAS (ASPIS-DInSAR) technique with biophysical and geographical data to investigate the impact of peatland drainage and agriculture on spatial patterns of subsidence in Selangor, Malaysia. Results showed pronounced subsidence in areas subjected to drainage for agricultural and oil palm plantations, while stable areas were associated with intact forests. The most powerful predictors of subsidence rates were the distance from the drainage canal or peat boundary; however, other drivers such as soil properties and water table levels were also important. The maximum subsidence rate detected was lower than that documented by ground-based methods. Therefore, whilst the APSIS-DInSAR technique may underestimate absolute subsidence rates, it gives valuable information on the direction of motion and spatial variability of subsidence. The study confirms widespread and severe peatland degradation in Selangor, highlighting the value of DInSAR for identifying priority zones for restoration and emphasising the need for conservation and restoration efforts to preserve Selangor peatlands and prevent further environmental impacts. Full article

Drought, an intricate natural phenomenon globally, significantly influences the gross primary productivity (GPP) and carbon sink potential of tropical forests. Present research on the drought response primarily focuses on natural forests, such as the Amazon rainforest, with relatively limited studies on tropical plantations. Therefore, for a comprehensive understanding of global climate change, accurately evaluating and analyzing the sensitivity and vulnerability of rubber plantation GPP to various drought characteristics is crucial. The Standardized Precipitation Evapotranspiration Index (SPEI) was used in this research to quantify drought intensity. The Spatially Explicit Individual Based Dynamic Global Vegetation Model (SEIB-DGVM) was localized based on observation data from the Hainan Danzhou Tropical Agro-ecosystem National Observation and Research Station. Subsequently, the calibrated model was utilized to simulate the dynamic process of rubber plantation GPP under multi-gradient drought scenarios (2 extreme boundaries × 3 drought initiation seasons × 4 drought intensities × 12 drought durations × 12 SPEI time scales). The results show that the sensitivity and vulnerability of rubber plantation GPP exhibit significant differences under drought scenarios in different initiation seasons; GPP exhibits higher sensitivity to extreme, long-duration flash droughts in the early rainy season. Regarding vulnerability, the impact of extreme, long-duration flash droughts on GPP is most pronounced. This research lays the foundation for estimating the impact of droughts on the GPP of rubber plantations under future climate change scenarios, providing a scientific basis for enhancing regional ecological restoration and protection. Full article

To explore the radial variation in wood properties of slash pine (Pinus elliottii Engelm.) during its growth process and to achieve the early prediction of these properties, our study was carried out in three slash pine harvest-age plantations in Ganzhou, Jian, and Jingdezhen, Jiangxi province of South China. Wood core samples were collected from 360 sample trees from the three plantations. SilviScan technology was utilized to acquire wood property parameters, such as tangential fiber widths (TFWs), radial fiber widths (RFWs), fiber wall thickness (FWT), fiber coarseness (FC), microfibril angle (MFA), modulus of elasticity (MOE), wood density (WD) and ring width (RD). Subsequent systematic analysis focused on the phenotypic and radial variation patterns of wood properties, aiming to establish a clear boundary between juvenile and mature wood. Based on determining the boundary between juvenile and mature wood, a regression equation was used to establish the relationship between the properties of juvenile wood and the ring ages. This relationship was then extended to the mature wood section to predict the properties of mature wood. Our results indicated significant differences in wood properties across different locations. The coefficients of variation for RD and MOE were higher than other properties, suggesting a significant potential for selective breeding. Distinct radial variation patterns in wood properties from the pith to the bark were observed. The boundary between juvenile and mature wood was reached at the age of 22. The prediction models developed for each wood property showed high accuracy, with determination coefficients exceeding 0.87. Additionally, the relative and standard errors between the measured and predicted values were kept below 10.15%, indicating robust predictability. Mature wood exhibited greater strength compared to juvenile wood. The approach of using juvenile wood properties to predict those of mature wood is validated. This method provides a feasible avenue for the early prediction of wood properties in slash pine. Full article

Forest biomass dynamics are important indicators of forest productivity and carbon sinks, which are useful for evaluating forest ecological benefits and management options. Rapid and accurate methods for monitoring forest biomass would serve this purpose well. This study aimed at measuring aboveground biomass (AGB) and stand growth from tree crown parameters derived using unmanned aerial vehicle–light detection and ranging (UAV–LiDAR). We focused on 17-year-old Chinese fir plantations in a subtropical area in China and monitored them using UAV–LiDAR from February 2019 to February 2020. Two effective crown height (ECH) detection methods based on drone discrete point clouds were evaluated using ground survey data. Based on the evaluation results, the voxel method based on point cloud segmentation (root-mean-squared error (RMSE) = 0.62 m, relative RMSE (rRMSE) = 4.26%) was better than the tree crown boundary pixel sum method based on canopy height segmentation (RMSE = 1.26 m, rRMSE = 8.63%). The effective crown area (ECA) of an individual tree extracted using ECH was strongly correlated with the annual biomass growth (coefficient of determination (R²) = 0.47). The estimation of annual growth of individual tree crowns based on annual tree height increase (ΔH) derived from LiDAR was statistically significant (R² = 0.33, p < 0.01). After adding the crown projection area or ECA, the model accuracy R² increased to 0.57 or 0.63, respectively. As the scale increased to the plot level, the direct model with ECA (RMSE = 1.59 Mg∙ha⁻¹∙a⁻¹, rRMSE = 15.02%) had a better performance than the indirect model using tree height and crown diameter (RMSE = 1.81 Mg∙ha⁻¹∙a⁻¹, rRMSE = 17.10%). The mean annual growth rate of AGB per middle-aged Chinese fir tree was determined to be 8.45 kg∙a⁻¹ using ECA and ΔH, and the plot-level growth rate was 11.47 Mg∙ha⁻¹∙a⁻¹. We conclude that the rapid and accurate monitoring of the annual growth of Chinese fir can be achieved based on multitemporal UAV–LiDAR and effective crown information. Full article

Extensive oil palm plantations worldwide are dependent on insect pollination, specifically by introduced African weevils (Elaidobius spp.). The effectiveness of these weevils has been questioned following poor pollination and yield loss in Malaysia. Indigenous thrip (Thysanoptera) species, and moths (Lepidoptera) in the genus Pyroderces, may also be pollinators of oil palm, while the role of bees (Hymenoptera) and flies (Diptera) is unknown. The potential of native pollinators remains uncertain because of the almost total clearing of forest habitat from oil palm landscapes. In this study, we investigate the value of small high conservation value (HCV) forests as sources of potential native insect pollinators of oil palm in northern Sarawak. We further examine the filtering effect of oil palm-dominated landscapes on the species assemblages of six potential pollinator insect orders: Blattodea, Coleoptera, Diptera, Hemiptera, Hymenoptera and Lepidoptera. Orders differed in both species composition and abundance between forest and oil palm plantations, with an average of 28.1% of species unique to oil palm. Oil palm presented a soft permeable boundary to Coleoptera, Hymenoptera and Lepidoptera. Their species richness and abundance differed little between habitats with distance, despite species turnover. In contrast, oil palm presented a harder boundary to Diptera with a decline in both species richness and abundance with distance into oil palm. The abundance of the oil palm weevil (Elaedobius kamerunicus) was low compared to the native dominants, but similar to levels displayed by native thrips that may be pollinators of oil palm. The functional diversity of well-known pollinator guilds—bees and flies—was similar in forest and oil palm, suggesting that potential pollinators may yet exist among native orders of insects. Contrary to the prevailing opinion, even small forest patches in oil palm landscapes may provide native pollinator pressure. Full article

The forests of Central Asia are biodiversity hotspots at risk from rapid climate change, but they are understudied in terms of the climate–growth relationships of trees. This classical dendroclimatic case study was performed for six conifer forest stands near their semiarid boundaries across Kazakhstan: (1–3) Pinus sylvestris L., temperate forest steppes; (4–5) Picea schrenkiana Fisch. & C.A. Mey, foothills, the Western Tien Shan, southeast; (6) Juniperus seravschanica Kom., montane zone, the Western Tien Shan, southern subtropics. Due to large distances, correlations between local tree-ring width (TRW) chronologies are significant only within species (pine, 0.19–0.50; spruce, 0.55). The most stable climatic response is negative correlations of TRW with maximum temperatures of the previous (from −0.37 to −0.50) and current (from −0.17 to −0.44) growing season. The strength of the positive response to annual precipitation (0.10–0.48) and Standardized Precipitation Evapotranspiration Index (0.15–0.49) depends on local aridity. The timeframe of climatic responses shifts to earlier months north-to-south. For years with maximum and minimum TRW, differences in seasonal maximal temperatures (by ~1–3 °C) and precipitation (by ~12–83%) were also found. Heat stress being the primary factor limiting conifer growth across Kazakhstan, we suggest experiments there on heat protection measures in plantations and for urban trees, alongside broadening the coverage of the dendroclimatic net with accents on the impact of habitat conditions and climate-induced long-term growth dynamics. Full article

In 2021, an unprecedented major movement of Asian elephants in China aroused the curiosity of hundreds of millions of people around the world. For research objectives, we firstly reproduce the movement route of Asian elephants and reveal their geographical spatial characteristics and landscape characteristics using multisource data; secondly, we reveal the reason for this Asian elephant movement. We found Asian elephants went far beyond the northernmost movement boundary from past years. Most of the areas along the movement route fell within the higher accessibility to road traffic. Over the past 20 years, the rubber and tea areas of Xishuangbanna and Pu’er have increased by 91.1% and 120.1%, respectively, from 2005 to 2019. Asian elephants spent 18 days in areas with suitable food, but relatively low vegetation coverage. The 2021 movement was most likely a “purposeful” trip rather than a “detour”. The elephants chose the most rewarding way to move forward, which showed they are far smarter than we thought. They may have left to find food due to exhausted food supply. The expansion of rubber and tea plantations has caused the habitat of Asian elephants to shrink, which was one of the reasons for the northward movement of them. Full article

The accurate classification of forest types is critical for sustainable forest management. In this study, a novel multiscale global graph convolutional neural network (MSG-GCN) was compared with random forest (RF), U-Net, and U-Net++ models in terms of the classification of natural mixed forest (NMX), natural broadleaved forest (NBL), and conifer plantation (CP) using very high-resolution aerial photographs from the University of Tokyo Chiba Forest in central Japan. Our MSG-GCN architecture is novel in the following respects: The convolutional kernel scale of the encoder is unlike those of other models; local attention replaces the conventional U-Net++ skip connection; a multiscale graph convolutional neural block is embedded into the end layer of the encoder module; and various decoding layers are spliced to preserve high- and low-level feature information and to improve the decision capacity for boundary cells. The MSG-GCN achieved higher classification accuracy than other state-of-the-art (SOTA) methods. The classification accuracy in terms of NMX was lower compared with NBL and CP. The RF method produced severe salt-and-pepper noise. The U-Net and U-Net++ methods frequently produced error patches and the edges between different forest types were rough and blurred. In contrast, the MSG-GCN method had fewer misclassification patches and showed clear edges between different forest types. Most areas misclassified by MSG-GCN were on edges, while misclassification patches were randomly distributed in internal areas for U-Net and U-Net++. We made full use of artificial intelligence and very high-resolution remote sensing data to create accurate maps to aid forest management and facilitate efficient and accurate forest resource inventory taking in Japan. Full article

Nitrogen (N) deposition significantly affects forest dynamics, carbon stocks and biodiversity, and numerous assessments of N fluxes and impacts exist in temperate latitudes. In tropical latitudes, however, there are few such assessments. In this study, we measured the inorganic N concentration (wet deposition) deposited in rainfall and rainfall pH throughout one year at the boundary of a forest reserve in Malaysian Borneo. We considered that the N deposition may be either from forest and agricultural fires or derived from agricultural fertiliser. Therefore, we determined the wind trajectories using the HYSPLIT model provided by NOAA, the location of fires throughout the landscape throughout one year using NASA’s FIRM system, and obtained the land use cover map of Malaysia and Indonesia. We then correlated our monthly cumulative wet N deposition with the cumulative number of fires and the cumulative area of oil palm plantation that wind trajectories arriving at our study site passed over before reaching the rainfall sampling site. At 7.45 kg N ha⁻¹ year⁻¹, our study site had the highest annual wet inorganic N deposition recorded for a Malaysian forest environment. The fire season and the cumulative agricultural area crossed by the winds had no significant effect on N deposition, rainfall N concentration, or rainfall pH. We suggest that future research should use ¹⁵N isotopes in rainfall to provide further information on the sources of N deposition in tropical forests such as this. Full article