Forest Mapping Research Articles

Relevant information from the perspective of sustainable forest management – auditing socio-environmental information and data

Purpose: The study aims to: (1) deepen the theory of defining new concepts such as relevant information and information needs, and (2) examine the essence of financial and non-financial information and its usefulness to forest stakeholders. The research problem was formulated as follows: “Since forest management entities generate and disclose financial and non-financial information, which of these sources of information will be considered most useful (relevant) by information users?” In the context of environmental and social challenges, accounting 5.0 must generate, report, and disclose information according to stakeholders’ expectations. Methodology/approach: The following methods were used: literature analysis and critique, document examination, logical analysis and construction, the Delphi method, and snowballing. The Pearson chi-square independence test and the Kruskal-Wallis test were used. Findings: The most important characteristics of information are reliability and timeliness. The most useful information for stakeholders includes forest maps and conservation information, a “forest dictionary”, and rules for timber sales. Information about campaigns, charitable actions, forest hazards, and forest access restrictions is useful. Research limitations: A limitation of the research is the national sample size of the survey, with a higher percentage of internal forest stakeholders than external stake-holders. Practical implications: The practical implications of the article are based on the collected research material, which can contribute to the development of a standard questionnaire for investigating the information needs of stakeholders in the future. Originality/value: The study is the first empirical research of its kind in Poland and involves the participation of foreign experts. Contributions to social science theory include: (1) an original definition of “relevant information”; (2) a definition of “information needs”; (3) comparative frameworks for the impact areas of the sustainable development concept in correlation with information, relevant information, and stake-holders; (4) a conceptual table of differences between types of information from a sustainability perspective.

Integrating Active and Passive Remote Sensing Data for Forest Age Estimation in Shangri-La City, China

The accurate mapping of age structure and access to spatially explicit information are essential to optimal planning and policy-making for forest ecosystems, including forest management and sustainable economic development. Specifically, surveying and mapping the age structure of forests is crucial for calculating the carbon sequestration capacity of forest ecosystems. However, spatial heterogeneity and limited accessibility make forest age mapping in mountainous areas challenging. Here, we present a new workflow using ICESat-2 LiDAR data integrated with multisource remote sensing imagery to estimate forest age in Shangri-La, China. Two methods—a climate-driven exponential model and a random forest algorithm—are compared to infer the age structure of the five dominant species in Shangri-La. The climate-driven model, with an R2 of 0.67 and an RMSE of 12.79 years, outperforms the random forest model. The derived wall-to-wall forest age map at 30 m resolution reveals that nearly all forests in Shangri-La are mature or overmature, especially among the high-elevation species Abies fabri (Mast.) Craib and Picea asperata Mast., compared with Pinus yunnanensis Franch., Quercus aquifolioides Rehd. and E.H. Wils. and Pinus densata Mast., where the age structure is more evenly distributed across different elevation ranges. Younger forests are frequently found around human settlements and along the Jinsha River valley, whereas older forests are located in remote and high-elevation areas that are less disturbed. The combined use of active and passive remote sensing data has resulted in substantial improvements in the spatial detail and accuracy of wall-to-wall age mapping, which is expected to be a cost-effective approach for supporting forest management and carbon accounting in this important ecological region. The method developed here can be scaled to other mountain areas both to understand the age patterns and structure of mountain forests and to provide critical information for forestation, reforestation and carbon accounting in surface-to-high mountain areas, which are increasingly crucial for climate mitigation.

A Multi-Scale Deep Learning Algorithm for Enhanced Forest Fire Danger Prediction Using Remote Sensing Images

Forest fire danger prediction models often face challenges due to spatial and temporal limitations, as well as a lack of universality caused by regional inconsistencies in fire danger features. To address these issues, we propose a novel algorithm, squeeze-excitation spatial multi-scale transformer learning (SESMTML), which is designed to extract multi-scale fire danger features from remote sensing images. SESMTML includes several key modules: the multi-scale deep feature extraction module (MSDFEM) captures global visual and multi-scale convolutional features, the multi-scale fire danger perception module (MFDPM) explores contextual relationships, the multi-scale information aggregation module (MIAM) aggregates correlations of multi-level fire danger features, and the fire danger level fusion module (FDLFM) integrates the contributions of global and multi-level features for predicting forest fire danger. Experimental results demonstrate the model’s significant superiority, achieving an accuracy of 83.18%, representing a 22.58% improvement over previous models and outperforming many widely used deep learning methods. Additionally, a detailed forest fire danger prediction map was generated using a test study area at the junction of the Miyun and Pinggu districts in Beijing, further confirming the model’s effectiveness. SESMTML shows strong potential for practical application in forest fire danger prediction and offers new insights for future research utilizing remote sensing images.

An Improved LandTrendr Algorithm for Forest Disturbance Detection Using Optimized Temporal Trajectories of the Spectrum: A Case Study in Yunnan Province, China

Forest disturbance mapping plays an important role in furthering our understanding of forest dynamics. The Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) algorithm is widely used in forest disturbance mapping. However, it neglects the quality of the temporal trajectory and its change trends for forest disturbance mapping. Therefore, the aim of this paper is to improve LandTrendr (iLandTrendr) for forest disturbance mapping by optimizing its temporal trajectories and the post-processing of detection results. Specifically, the temporal trajectory of complex forest disturbance types was optimized using the Savitzky–Golay (SG) filter with constraints. That is, the smooth value generated from the SG filter for the disturbance year was replaced by the satellite observations when the nonlinear abrupt signals were included in the multi-temporal data. The forest disturbance detected by LandTrendr was further modified using the consistency of spectral variation trends. A case study using iLandTrendr to detect forest disturbance in Yunnan Province was conducted. Compared to the LandTrendr method, which has an overall accuracy (OA) of 35.88%, iLandTrendr generated forest disturbance mapping with an OA of 89.32%, which was significantly higher. The total mapped area of disturbance was 1,985,820.9 km2, accounting for 49.69% of the total area. The disturbances were predominately caused by natural factors, such as wildfires, pests and diseases, and forest degradation, accounting for 85.31% of the total disturbed area. iLandTrendr can quickly and accurately detect the occurrence year of complex forest disturbance types and can be extended for the forest disturbance mapping of a large area.

An analysis of the safety of Sevoflurane drugs: A disproportionality analysis based on Food and Drug Administration Adverse Event Reporting System.

Sevoflurane is a volatile anesthetic that can tolerate inhalation induction and is widely used for inducing anesthesia due to its pleasant odor. As a drug that has been on the market for nearly 30 years, the vast majority of adverse reactions have been documented. This study aims to improve the adverse reactions related to Sevoflurane through the mining, organizing and analysis of Food and Drug Administration Adverse Event Reporting System database data. We collected, organized, and analyzed reports from the first quarter of 2004 to the fourth quarter of 2022. We performed disproportionality analysis algorithms, including reporting odds ratio, the proportional reporting ratio values, to quantify the signal values of different adverse events (AEs). A total of 1126 AEs and 27 system organ classes were identified by performing statistics analysis system software. By combining algorithm calculations, we create a forest map of the top 30 AEs of the reporting odds ratio signal. Based on the reviewing relevant literature, we found that the vast majority of AEs have been reported in relevant studies. However, there is currently no study revealing the correlation between atrial fibrillation and Sevoflurane, which means that atrial fibrillation may be an unreported AE of Sevoflurane. In the present study, we found that atrial fibrillation may be a new adverse reaction of Sevoflurane through the Food and Drug Administration Adverse Event Reporting System database, which can function as a novel guideline to guide us in the more standardized use of Sevoflurane in clinical practice.

Mapping temperate old-growth forests in Central Europe using ALS and Sentinel-2A multispectral data

Old-growth forests are essential to preserve biodiversity and play an important role in sequestering carbon and mitigating climate change. However, their existence across Europe is vulnerable due to the scarcity of their distribution, logging, and environmental threats. Therefore, providing the current status of old-growth forests across Europe is essential to aiding informed conservation efforts and sustainable forest management. Remote sensing techniques have proven effective for mapping and monitoring forests over large areas. However, relying solely on remote sensing spectral or structural information cannot capture comprehensive horizontal and vertical structure complexity profiles associated with old-growth forest characteristics. To overcome this issue, we combined spectral information from Sentinel-2A multispectral imagery with 3D structural information from high-density point clouds of airborne laser scanning (ALS) imagery to map old-growth forests over an extended area. Four features from the ALS data and fifteen from Sentinel-2A comprising raw band (spectral reflectance), vegetation indices (VIs), and texture were selected to create three datasets used in the classification process using the random forest algorithm. The results demonstrated that combining ALS and Sentinel-2A features improved the classification performance and yielded the highest accuracy for old-growth class, with an F1-score of 92% and producer’s and user’s accuracies of 93% and 90%, respectively. The findings suggest that features from ALS and Sentinel-2A data sensitive to forest structure are essential for identifying old-growth forests. Integrating open-access satellite imageries, such as Sentinel-2A and ALS data, can benefit forest managers, stakeholders, and conservationists in monitoring old-growth forest preservation across a broader spatial extent.

Mapping forest phenological shift in Nilgiri Biosphere Reserve, Western Ghats: Response to climate change

In the present study, the spatiotemporal alterations in phenological metrics were analyzed in different forest types of Nilgiri Biosphere Reserve (NBR), Western Ghats, India with special reference to the impacts of changing climate on the forest phenology over two-phase periods (Phase 1: 2001–2010 and Phase 2: 2011–2020). Three phenological measures including the start/end/length of the season (SOS/EOS/LOS) were obtained using 16 days of MODIS Enhanced Vegetation Index (EVI) dataset over the two decades. The study exhibited a gradual delay in SOS during phase 1, and an advancing SOS during phase 2 in the tropical forests of NBR. Delayed EOS was observed in all forest types in NBR barring Moist Deciduous Forests (MDF) during phase 1, while opposite trend of advancing EOS was observed in deciduous forests (MDF and DDF: Dry Deciduous Forests), but delayed EOS was observed in evergreen forests (SEF: Semi Evergreen Forests and WEF: Wet Evergreen Forests) during phase 2. These variations in forest phenology are linked with an increase in annual mean temperature (0.01 °C year−1) along with a decrease in annual mean precipitation (3.97 mm year−1) during 1950–2018 as observed using IMD-based meteorological datasets. The results highlighted reduction in the total LOS with a delayed SOS and advanced EOS in NBR, which were prevalent during phase 1 conversely to phase 2 due to the varied intensity of changes in climatic conditions. However, disaggregating decade-long intervals into 5-year segments enables a finer resolution analysis of phenological trends. The study contributes to the development of long-term strategies for forest ecosystem restoration under the influence of global climate change by providing an insightful understanding of the non-systematic shifts in forest phenology attributed by rising warming impacts and erratic precipitation patterns.

Using quantile mapping and random forest for bias‐correction of high‐resolution reanalysis precipitation data and CMIP6 climate projections over Iran

AbstractClimate change is expected to cause important changes in precipitation patterns in Iran until the end of 21st century. This study aims at evaluating projections of climate change over Iran by using five climate model outputs (including ACCESS‐ESM1‐5, BCC‐CSM2‐MR, CanESM5, CMCC‐ESM2 and MRI‐ESM2‐0) of the Coupled Model Intercomparison Project phase 6 (CMIP6), and performing bias‐correction using a novel combination of quantile mapping (QM) and random forest (RF) between the years 2015 and 2100 under three shared socioeconomics pathways (SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5). First, bias‐correction was performed on ERA5‐Land reanalysis data as reference period (1990–2020) using the QM method, then the corrected ERA5‐Land reanalysis data was considered as measured data. Based on the corrected ERA5‐Land reanalysis data (1990–2020) and historical simulations (1990–2014), the future projections (2015–2100) were also bias‐corrected utilizing the QM method. Next, the accuracy of the QM method was validated by comparing the corrected ERA5‐Land reanalysis data with model outputs for overlapping years between 2015 and 2020. This comparison revealed persistent biases; hence, a combination of QM‐RF method was applied to rectify future climate projections until the end of the 21st century. Based on the QM result, CMCC‐ESM2 revealed the highest RMSE in both SSP2‐4.5 and SSP3‐7.0 amounting to 331.74 and 201.84 mm·year−1, respectively. Particularly, the exclusive use of the QM method displayed substantial errors in projecting annual precipitation based on SSP5‐8.5, notably in the case of ACCESS‐ESM1‐5 (RMSE = 431.39 mm·year−1), while the RMSE reduced after using QM‐RF method (197.75 mm·year−1). Obviously, a significant enhancement in results was observed upon implementing the QM‐RF combination method in CMCC‐ESM2 under both SSP2‐4.5 (RMSE = 139.30 mm·year−1) and SSP3‐7.0 (RMSE = 151.43 mm·year−1) showcasing approximately reduction in RMSE values by 192.43 and 50.41 mm·year−1, respectively. Although each bias‐corrected model output was evaluated individually, multi‐model ensemble (MME) was also created to project the annual future precipitation pattern in Iran. By considering that combination of QM‐RF method revealed the lower errors in correcting model outputs, we used the QM‐RF technique to create the MME. Based on SSP2‐4.5, the MME climate projections highlight imminent precipitation reductions (>10%) across large regions of Iran, conversely projecting increases ranging from 10% to over 20% in southern areas under SSP3‐7.0. Moreover, MME projected dramatic declines under SSP5‐8.5, especially impacting central, eastern, and northwest Iran. Notably, the most pronounced possibly decline patterns are projected for arid regions (central plateau) and eastern areas under SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5.

Enhancing Forest Conservation Efforts: Mapping of High Conservation Value Forests in the Republic of Moldova

Enhancing Forest Conservation Efforts: Mapping of High Conservation Value Forests in the Republic of Moldova

Comparative Analysis of Different Forest Fire Susceptibility Models in Benguet, the Philippines

Fire is a common threat to forests in our country. It is usually uncontrolled and consumes the natural fuels of the forest, affecting both the vegetation and the soil. In the Philippines, forest fires are mostly frequent in regions with naturally occurring vegetation that is highly susceptible to forest fire, and one of them is the Cordillera Administrative Region. This study was conducted to [1] determine forest fire occurrences in Benguet province, [2] generate forest fire hazard maps using three indices [structural fire index (SFI), fire risk index (FRI), and hybrid fire index (HFI)], and [3] recommend potential forest management strategies to reduce fire susceptibility or manage fire occurrence in the province. NDMI, elevation, slope, aspect, distance from roads, and vicinity to settlements were the influencing factors used in the occurrence of forest fires. Moreover, forest fire occurrences in the province were collected from DENR records. Results revealed that from 2019–2021, a total of 90 forest fire incidents were observed in the area. In terms of the indices, results showed that maps using HFI and FRI revealed better results than the SFI model. Furthermore, there were forest fire incidences that coincided with the FRI model, particularly in those areas identified as having high and very high susceptibility ratings.

High-resolution operational soil moisture monitoring for forests in central Germany

Abstract. The forests of central Germany (Saxony, Saxony-Anhalt, and Thuringia) are vital components of the local ecosystems, the economy, and recreation. However, in recent years, these forests have faced significant challenges due to prolonged climate-change-induced droughts, causing water shortages, tree stress, and pest outbreaks. One of the key components of the forests' vitality and productivity is the availability of soil moisture. Given the anticipated increase in the frequency and severity of drought events, there is a growing demand for accurate and real-time soil moisture information. This underscores the need for development of an appropriate monitoring tool to make forest management strategies more effective. The article introduces an operational high-resolution soil moisture monitoring framework for the forests in central Germany. The key components of this system include the advanced LWF-BROOK90 1D water balance model, a large database of the National Federal Forest Inventory, high-resolution forest soil maps, real-time climate data from the German Meteorological Service, and a web information platform for the presentation of daily updated results. This system informs the public and empowers forest managers and other decision-makers to take targeted, local measures for sustainable forest management, aiding in both drought mitigation and long-term forest health in the face of climate change. The validation of the system using soil moisture measurements from 51 stations with various sensor depths (up to 100 cm) showed an overall good agreement (0.76 median Pearson correlation), which was found to be higher for deciduous rather than coniferous forests. Finally, the framework is discussed against the background of the main limitations of existing monitoring systems and how operational soil moisture measurements contribute to better interpretation of simulations.

UAV survey mapping of illegal deforestation in Madagascar

Societal Impact StatementUnmanned aerial vehicle (UAV) imagery highlights the extent of illegal deforestation in protected areas for the biodiverse humid forest of central Madagascar. The ultra‐high‐resolution (<10‐cm pixel) images enable the creation of detailed forest 3D base maps and provide the means to quantify forest stand losses. To help communities safeguard their forests, local non‐governmental organisations can use UAV maps in combination with weekly deforestation alerts to facilitate an immediate on‐ground response that significantly restricts illegal activity. Integrating ultra‐high‐resolution UAV mapping and coarse‐resolution freely available satellite imagery should have much wider applications in Madagascar and the humid tropics for community‐based conservation.Summary This study of the Ambohimahamasina humid forest shows that small UAVs offer a detailed (<10‐cm pixel), rapid and cost‐effective solution to provide maps of detailed deforestation patterns not visible in satellite imagery. Calculating forest extent and volume are valuable ways to rapidly assess forest losses and prioritise areas for ground patrols. The use of 3‐dimensional measurements for above ground carbon estimates indicate how, in the future, these metrics could be used to calculate carbon payments for conservation programs. By combining UAV and free satellite imagery, an effective alert system has been developed that supports community initiatives in the protection of their natural forest resources. The wealth of ultra‐high‐resolution UAV data collected in this study provides insights into forest dynamics, supports local community forest management, and has the potential to measure the value of the forest.

Estradiol and testosterone associated with risk of breast cancer: a meta-analysis

Background: To investigate the correlation between estradiol and testosterone in patients with breast cancer. Methods: The literatures on the correlation between estradiol and testosterone on the risk of breast cancer were searched and collected. The time limit is that each database is established until December 2023. After screening, the modified Jadad scale was used to evaluate the quality of the research literature. NoteExpress 3.2 was used for literature management, and Excel 2003 was used for data collection and extraction. Statistical analysis was performed using RevMan 5.4.1 software to determine whether there was heterogeneity in the study according to the size of Q test (P-value), and then the OR value of combined effects was calculated using fixed or random effects models and forest maps were drawn. At the same time, the literatures with the greatest weight were excluded for sensitivity analysis, and the literature bias was evaluated by drawing funnel plot. Results: A total of 628 literatures were retrieved, and 11 case-control trials met the criteria for inclusion. Meta-analysis results showed that the level of E2 in breast cancer patients was higher than that in non-breast cancer control group, but the difference was not statistically significant (OR=121.56, 95%CI (-3.32-264.44), P=0.06). The level of E2 in premenopausal patients with breast cancer was higher than that in non-breast cancer control group, but the difference was not statistically significant (OR=8.26, 95%CI (-2.83-19.34), P=0.14). The level of E2 in postmenopausal patients with breast cancer was higher than that in non-breast cancer control group, and the difference was statistically significant (OR=20.36, 95%CI (7.04-33.68), P=0.003). Preoperative T level was higher in patients with breast cancer than in non-breast cancer control group, but the difference was not statistically significant (OR=14.77, 95%CI (-14.11-43.65), P=0.32). The T level before and after surgery in breast cancer patients was higher than that in non-breast cancer control group, and the difference was statistically significant (OR=12.91, 95%CI (4.43-21.39), P=0.003). Sensitivity analysis showed that the combined effect size results were stable and reliable OR (95%CI) was 24.41 (10.21~38.61), P=0.0007. Funnel plot results showed publication bias. Conclusion There is a positive correlation between the levels of estradiol and testosterone and the occurrence and development of breast cancer after menopause.

Mapping Forest Growing Stock and Its Current Annual Increment Using Random Forest and Remote Sensing Data in Northeast Italy

The role of forests in providing multiple goods and services has been recognized worldwide. In such a context, reliable spatial predictions of forest attributes such as tree volume and current increment are fundamental for conducting forest monitoring, improving restoration programs, and supporting decision-making processes. This article presents the methodology and the results of the wall-to-wall spatialization of the growing stock volume and the current annual increment measured in 273 plots of data of the Italian National Forest Inventory over an area of more than 3260 km2 in the Friuli Venezia Giulia region (Northeast Italy). To this aim, a random forest model was tested using as predictors 4 spectral indices from Sentinel-2, a high-resolution Canopy Height Model derived from LiDAR, and geo-morphological data. According to the Leave One Out cross-validation procedure, the model for the growing stock shows an R2 and an RMSE% of 0.67 and 41%, respectively. Instead, an R2 of 0.47 and an RMSE% of 57% were obtained for the current annual increment. The validation with an independent dataset further improved the models’ performances, yielding significantly higher R2 values of 0.84 and 0.83 for volume and for increment, respectively. Our results underline a relatively higher importance of LiDAR-derived metrics compared to other covariates in estimating both attributes, as they were even twice as important as vegetation indices for growing stock. Therefore, these metrics are promising for the development of a national LiDAR-based model.

Is Your Training Data Really Ground Truth? A Quality Assessment of Manual Annotation for Individual Tree Crown Delineation

For the accurate and automatic mapping of forest stands based on very-high-resolution satellite imagery and digital orthophotos, precise object detection at the individual tree level is necessary. Currently, supervised deep learning models are primarily applied for this task. To train a reliable model, it is crucial to have an accurate tree crown annotation dataset. The current method of generating these training datasets still relies on manual annotation and labeling. Because of the intricate contours of tree crowns, vegetation density in natural forests and the insufficient ground sampling distance of the imagery, manually generated annotations are error-prone. It is unlikely that the manually delineated tree crowns represent the true conditions on the ground. If these error-prone annotations are used as training data for deep learning models, this may lead to inaccurate mapping results for the models. This study critically validates manual tree crown annotations on two study sites: a forest-like plantation on a cemetery and a natural city forest. The validation is based on tree reference data in the form of an official tree register and tree segments extracted from UAV laser scanning (ULS) data for the quality assessment of a training dataset. The validation results reveal that the manual annotations detect only 37% of the tree crowns in the forest-like plantation area and 10% of the tree crowns in the natural forest correctly. Furthermore, it is frequent for multiple trees to be interpreted in the annotation as a single tree at both study sites.

How important is Forest Age in explaining the species composition of Near-natural Spruce Forests?

Multiple ecological drivers, along with forest age, determine the species composition of boreal forest ecosystems. However, the role of age in successional changes in forests cannot be understood without taking site conditions, the disturbance regime and forest structure into account. In this study, we ask two research questions: 1. What is the relationship between forest age and overall species composition in older near-natural spruce forests, i.e. forests of age beyond harvest maturity? 2. Do species associated with different forest habitats respond similarly to variation in forest age? Data were collected in 257 Norway spruce dominated 0.25 ha plots from three study areas in Southeastern and Central Norway. Species inventories were conducted for lichens and bryophytes on trees and rocks, vascular plants on the forest floor, and for deadwood-associated bryophytes and polypore fungi. Although NMDS ordination analyses of the total species composition identified a main axis related to the age of the oldest trees in two of the study areas, variation partitioning analyses showed that age explained a small fraction of variation of the species composition compared to site conditions, logging history, forest structure, and differences between the sites in all habitats. The unique variation explained by forest age species was, however, significant for all habitats. The fraction of variation in species composition explained by forest age was the largest for lichens and bryophytes on trees, and for deadwood-associated bryophytes and polypore fungi. Our results suggest that practical mapping of near-natural forests for management purposes inventories should include site conditions, forest structure and between site differences in addition to forest age.

Forest Fire Risk Assessment and Mapping Using Remote Sensing and GIS Techniques: A Case Study in Nghe An Province, Vietnam

Forest Fire Risk Assessment and Mapping Using Remote Sensing and GIS Techniques: A Case Study in Nghe An Province, Vietnam

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.

Forestry Extension Performance in Enhancing Forest Farmer Knowledge and Skills: A Case Study in Tompobulu, Maros Regency

Forestry Extension Workers, as one of the important parties in maintaining and preserving forest resources in Indonesia, are expected to have an understanding of good extension methods and techniques so that farmers around the forest can improve their knowledge and skills in managing and utilising forest products. This study aimed to assess the performance of forestry extension workers in Tompobulu District. And also evaluate their role in enhancing the knowledge and skills of forest farmer groups in Maros Regency. It was located in six villages, namely Bonto Somba Village, Bonto Manurung, Bonto Matinggi, Bonto Manai, Tompubulu, and Pucak Maros Regency with the number of samples in the study was 57. The study employed qualitative descriptive analysis using a Likert scale, and quantitative analysis using the t-test. The performance of forestry extension workers that significantly influences increasing the knowledge and skills of forest farmer groups consists of six indicator components, namely: facilitating forest farmer groups to prepare forest product utilisation maps; facilitating forest farmer groups to prepare annual work programs and plans; facilitating forest farmer group members to improve human resource quality; facilitating forest farmer groups to disseminate or spread information on forest product technology; facilitating forest farmer groups to grow their empowerment and independence; and facilitating forest farmer groups to increase production results of superior forest product commodities. Meanwhile, the performance of forestry extension workers that does not significantly influence increasing the knowledge and skills of forest farmer groups is Facilitating Business Partnerships Between Forest Farmer Groups, Entrepreneurs, and the Government, Facilitating Forest Farmer Groups to Access Business Capital with Financial Institutions, Banks, and the Government; and Facilitating Increasing the Income and Welfare of Forest Farmer Groups.

Data driven forest fire susceptibility mapping in Bangladesh

Forests are essential natural resources that serve to facilitate economic activity while also providing an essential impact on climate regulation and the carbon cycle. In the Chittagong hill tracts, forest fires significantly contribute to the eradication of forests and the depletion of biodiversity. As a result, addressing the forest fire hazard is crucial for mitigating its potential negative effects. By implementing machine learning (ML) and deep learning (DL) algorithms, the primary aim of this research is to determine which areas within the CHT are most prone to forest fires. Artificial Neural Network (ANN), Classification and Regression Trees (CART), Categorical Boosting (Catboost), Convolutional Neural Network (CNN), Deep Neural Network (DNN), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGboost) are among these algorithms. This research investigates 267 past forest fire incidents while incorporating fifteen predictive parameters for forest fires. The utmost accuracy is demonstrated by the Catboost model, which has an AUC value of 0.83. Applied models the Bandarbon and Rangamati regions of the Chittagong hill tracts have been thoroughly analyzed and discovered to be extremely and moderately vulnerable to forest fires due to their intricate physical and social characteristics. The Catboost model indicates that 67.38% of the total regions and 0.59% of the regions in Chittagoong hill tracts are classified as moderate and high susceptible zones, respectively. The results of this study present the models utilized in the present analysis exhibit a high level of effectiveness. Furthermore, the findings indicated that the NDVI and annual temperature had the most influence. The application of both ML and DL approaches in the Chittagong hill tracts for the first time resulted in models that exhibited excellent accuracy and precision, combining perfectly with the actual conditions on the ground. This study will undeniably assist the local government in implementing efficient governance and preservation strategies for the sustainable maintenance of forest resources, while enhancing the well-being of individuals living in and around the forest. Forest fire susceptibility assessment can be utilized in various locations worldwide that include significant natural and human-induced characteristics.