Forest Quality Research Articles

STRUKTUR DAN KOMPOSISI HUTAN BEKAS PERLADANGAN DI DESA HILIFALAGO KECAMATAN ONOLALU KABUPATEN NIAS SELATAN

Forests can experience a decline as a result of various activities carried out by humans, one of which is shifting cultivation. Decreasing forest quality reduces plant carbon absorption and storage, thereby affecting plant biological activity and impacting biodiversity. This study aims (1) to understand the structure and of ex-swidden forest in Hilifalago village, Onolalu district, South Nias Regency, (2) to understand the composition of ex-swidden forest. This study uses a descriptive quantitative approach. This study used the plot method with a size of 40 x 40 m, the sampling technique was purposive sampling. Based on the results of the study, it was found that the structure of ex-swidden forest in Hilifalago Village, Onolalu District consisted of upper and lower canopy layers, namely stratum A, B, C, and D and the most commonly found was the canopy layer in stratum C, while the composition of ex-swidden forest in the district Onolalu consists of 8 families and 10 species with 44 individuals. The highest important value index was found in Hevea brasiliensis Muell. Arg. (110.68%) and the family that was classified as the dominant family was the Euphorbiaceae

Water and climate: Global environmental sustainability and the current state in a developing country, Nicaragua

Environmental sustainability means taking steps to secure adequate management of natural resources in all human productive and livelihood activities. These steps involve a strategy of rational sustainable exploitation and the introduction of effective conservation measures which means integral management on all levels, national, regional and global. The specific situation of Nicaragua, as a prime example of a country in development with a vast richness in natural resources is analyzed. To name a few, in total renewable water resources the country has the 14th highest total yearly internal renewable water resources per capita (24,161/capita/year) in the Americas and as a tropical country has a large variety of biodiversity and ecosystems such as tropical dry and humid forests and 910 km of coastal area in the Caribbean and Pacific coast. These natural resources and more could bring potential to promote its own development. In spite of this, the country is on an unsustainable path as many parts of the world but with even greater challenges due to limited economic sustainability, high vulnerability to climate change and the lack of institutional capacity to establish better management. Unsustainable practices ranging from over-exploitation of resources to drastic land use changes have created environmental problems which consequently affect human wellbeing and health. Additionally, ecosystems and the quality of resources are under pressure which lead to a reduction in quality of water, forests and biodiversity. For example, the dry tropical forests have been reduced in 90% from 2000 to 2011 in the Pacific and Central areas and humid tropical forests are constantly being invaded and converted to agricultural and pasture lands. Water quality has been affected from contamination from agriculture, industries and the natural volcanic geology (arsenic) in groundwater as well as eutrophication and increased sedimentation from the watersheds of both lakes and rivers. Climate change is exacerbating these problems and causing new ones. After illustrating the present state of resources in Nicaragua, strategic suggestions of needed components to introduce an integral and improved management of the water resources have been laid out as a proposal for the future that include improvement in institutional capacity and governance to promote better management.

Pathway and driving forces to complete forest transition in inner Mongolia of China

China has experienced an extensive forest transition. However, the biophysical conditions and socio-economic effects in different regions are quite different in such a large country. Few studies on the forest transition of China have focused on how forest transitions occur and what process of forest transition in quality experience at the regional level. This study conducts a regional analysis on forest transition in Inner Mongolia from the dual perspectives of forest quantity and quality using data from 1980 to 2018. Radial Basis Function Neural Network (RBFNN) modeling combining the sensitivity analysis method is used to comprehensively analyze potential socio-economic factors that may drive the forest transition observed in Inner Mongolia. The results show that the changing trend of the forest area in Inner Mongolia shaped a “N" curve, while the standing forest stock showed a general slow-growing trend. It is important to note that Inner Mongolia achieved a complete forest transition around 1993. The forest transition witnessed in Inner Mongolia mainly followed two pathways: the “economic development pathway” and the “national forest policy pathway.” The results suggest that the forest area in Inner Mongolia is changing into an “N" shaped curve with the number of live standing trees in the forests generally growing slowly. The results further suggest that, rather than only analyzing the spatial expansion of forest area, ecological construction is also an important determinant of future forest quality.

Soil quality in urban forests under different understory management practices

AbstractForest vegetation management plays an important role in maintaining soil quality and function. In highly managed urban forests, the clearing of understory vegetation has potential to cause loss of soil nutrients, resulting in stoichiometric imbalances and a decrease in soil function. Whether the presence of ornamental understory in urban forests can mitigate the negative influences on soil quality remains highly uncertain. Here, we collected soil samples from a plantation forest in Zijin Shan National Forest Park in two stands, each with three adjacent plots with different understory management practices: one with natural understory vegetation (≥10 species), a second with managed understories of ornamental groundcover [Reineckea carnea (Andrews) Kunth or Ophiopogon bodinieri H.Lév], and a third with cleared understory. Compared with plots with natural understories, we found lower levels of soil total carbon (C), total nitrogen (N), and microbial C and N, in plots with no understories. Correspondingly, ratios of soil C:phosphorus (P) and N:P, and microbial C:N, were lower in the absence of understories in one of the two stands. The magnitude of these effects differed between the two stands, with greater effects observed in the stand with higher soil quality. These influences were smaller with groundcover plants in both stands, particularly O. bodinieri H.Lév. While we cannot rule out the effects of other influences on soil properties, these results offer support for the hypothesis that clearing of understories is potentially detrimental to soil quality, and that maintaining ornamental groundcover can alleviate these adverse influences in urban forest.

Spatial optimization of ecological ditches for non-point source pollutants under urban growth scenarios.

Non-point source (NPS) pollution is regarded as the major threat to water quality worldwide, and ecological ditches (EDs) are considered an important and widely used method to collect and move NPS pollutants from fields to downstream water bodies. However, few studies have been conducted to optimize the spatial locations of EDs, particularly when the watershed experiences urbanization and rapid land-use changes. As land-use patterns change the spatial distribution of NPS loads, this study used a cellular automata-Markov method to simulate future land-use changes in a typical agricultural watershed. Three scenarios are included as follows: historical trend, rapid urbanization, and ecological protection scenarios. The spatial distributions of particulate phosphorus loads were simulated using the revised universal soil loss equation and sediment transport distribution model. The results suggested that the total particulate phosphorus (TP) load in the Zhuxi watershed decreased by 10,555.2kg from 2000 to 2020, primarily because the quality and quantity of forests in Zhuxi County improved over the last 20years. The TP load in Zhuxi watershed would be 2588.49, 2639.15, and 2553.32kg in 2040 in historical trend, rapid urbanization, and ecological protection scenarios, respectively, compared with 2308.1kg in 2020. This indicated that urban expansion increases the TP load, and the faster the expansion rate, the more the TP load. Consequently, the optimal locations of EDs were determined based on the intercepted loads and the period during which they existed during land-use changes. The results suggested that rapid urbanization would consequently reduce the space available for building EDs and also increase the cost of building EDs to control the NPS pollution in the watershed.

Above-Ground Biomass Estimation for Coniferous Forests in Northern China Using Regression Kriging and Landsat 9 Images

Accurate estimation of forest above-ground biomass (AGB) is critical for assessing forest quality and carbon stocks, which can improve understanding of the vegetation growth processes and the global carbon cycle. Landsat 9, the latest launched Landsat satellite, is the successor and continuation of Landsat 8, providing a highly promising data resource for land cover change, forest surveys, and terrestrial ecosystem monitoring. Regression kriging was developed in the study to improve the AGB estimation and mapping using the Landsat 9 image in Wangyedian forest farm, northern China. Multiple linear regression (MLR), support vector machine (SVM), back propagation neural network (BPNN), and random forest (RF) were used as the original models to predict the AGB trends, and the optimal model was used to overlay the results of kriging interpolation based on the residuals to obtain the new AGB predictions. In addition, Landsat 8 images in Wangyedian were used for comparison and verification with Landsat 9. The results showed that all bands of Landsat 8 and Landsat 9 maintained a high degree of uniformity, with positive correlation coefficients ranging from 0.77 to 0.89 (p < 0.01). RF achieved the highest estimation accuracy among all the original models based on the two data sources. However, kriging regression can significantly reduce the estimation error, with the root mean square error (RMSE) decreasing by 55.4% and 51.1%, for Landsat 8 and Landsat 9, respectively, compared to the original RF. Further, the R2 and the lowest RMSE for Landsat 8 were 0.88 and 16.83 t/ha, while, for Landsat 9, they were 0.87 and 17.91 t/ha. The use of regression kriging combined with Landsat 9 imagery has great potential for achieving efficient and highly accurate forest AGB estimates, providing a new reference for long-term monitoring of forest resource dynamics.

Visual Aesthetic Quality Assessment of Urban Forests: A Conceptual Framework

Visual aesthetic quality is the visual pleasure level that attracts people and makes them prefer certain areas. Visual aesthetic quality is valued and considered for urban forests but remains challenging. This could be due to a lack of understanding of visual aesthetic quality assessment variables based on visual aesthetic theories. This study supports an integrated conceptual framework based on the result of a systematic literature review study to describe and measure aesthetics that incorporates objective and subjective factors through urban forest visual character and urban forest visual quality. The results include defining and understanding a description of visual aesthetic factors and variables as well as a thorough explanation of visual aesthetic theories to comprehend how to assess the visual aesthetic quality of urban forests. This study agrees with and supports the visual aesthetic theoretical framework, and we believe that due to our shared evolutionary history, humans have a standard set of urban forest visual aesthetic features with preferences that change according to cultural and personal variances. Furthermore, this research provides a foundation of visual aesthetic variables of urban forests that will assist urban forest researchers, urban forest managers, and decision-makers in managing and protecting the visual aesthetics of urban forests.

Intelligent computing based forecasting of deforestation using fire alerts: A deep learning approach

Deforestation is depletion of the forest cover and degradation in forest quality mainly through repeated fires, over-exploitation, and diseases. In a forest ecosystem, occurrence of wildfires is a natural phenomena. The curse of global warming and man-made interventions have made the wildfires increasingly extreme and widespread. Though, extremely challenging due to rapidly changing climate, accurate prediction of these fire events can significantly improve forestation worldwide. In this paper, we have addressed this issue by proposing a deep learning (DL) framework using long short term memory (LSTM) model. The proposed mechanism accurately forecasts weekly fire alerts and associated burnt area (ha) utilizing historical fire data provided by GLOBAL FOREST WATCH. Pakistan is taken as a case study since its deforestation rate is among the highest in the world while having one of the lowest forest covers. Number of epochs, dense layers, hidden layers and hidden layer units are varied to optimize the model for high estimation accuracy and low root mean square error (RMSE). Simulation results show that the proposed method can predict the forest fire occurrences with 95% accuracy by employing a suitable hyperparameter tuning.

Humid tropical vertebrates are at lower risk of extinction and population decline in forests with higher structural integrity.

Reducing deforestation underpins global biodiversity conservation efforts. However, this focus on retaining forest cover overlooks the multitude of anthropogenic pressures that can degrade forest quality and imperil biodiversity. We use remotely sensed indices of tropical rainforest structural condition and associated human pressures to quantify the relative importance of forest cover, structural condition and integrity (the cumulative effect of condition and pressures) on vertebrate species extinction risk and population trends across the global humid tropics. We found that tropical rainforests of high integrity (structurally intact and under low pressures) were associated with lower likelihood of species being threatened and having declining populations, compared with forest cover alone (without consideration of condition and pressures). Further, species were more likely to be threatened or have declining populations if their geographic ranges contained high proportions of degraded forest than if their ranges contained lower proportions of forest cover but of high quality. Our work suggests that biodiversity conservation policies to preserve forest integrity are now urgently required alongside ongoing efforts to halt deforestation in the hyperdiverse humid tropics.

Assessment of the Health of Community Forests Based on Rubber (Hevea brasiliensis) in Tulang Bawang Regency

The knowledge and awareness of community forest managers about the importance of forest health are still relatively low, especially in community forests based on rubber (Hevea brasiliensis) plantations. Whereas through forest health measurement, reliable data and information will be obtained that can be used to ensure the quantity and quality of community forests as a form of management decisions for community forest managers. This study aimed to determine the health status of rubber plantation-based community forests in Ujung Gunung Village, Menggala District, Tulang Bawang Regency. The stages of implementing this research include: determining the number of clusters based on management prescriptions (planting year and spacing), making cluster plots based on the design of Forest Health Monitoring (FHM) clusters, collecting data by measuring forest health indicators (productivity, vitality and site quality) based on the FHM technique, data processing and analysis were carried out by calculating forest health values. The results showed that the health status of rubber community forests had a range of values from 3.97 to 7.03. The average value of forest health based on rubber plants in Ujung Gunung Village, Menggala District, Tulang Bawang Regency is 5.59, which is included in the medium value range. Thus, the value of community forest health status is moderate.

Forest Aboveground Biomass Estimation and Response to Climate Change Based on Remote Sensing Data

As the largest and most important natural terrestrial ecosystem, forest plays a crucial role in reducing the concentrations of greenhouse gases in the atmosphere, mitigating global warming, maintaining the global ecological balance, and promoting global biological evolution and community succession. The accurate and rapid assessment of forest biomass is highly significant for estimating the regional carbon budget and monitoring forest change. In this study, Landsat images and China’s National Forest Continuous Inventory data of 1999, 2004, 2009, and 2014 were used to establish extreme gradient boosting (XGBoost) models for forest aboveground biomass (AGB) estimation based on forest type in the Xiangjiang River Basin, Hunan Province, China. Kriging interpolation of the AGB residuals was used to correct the error of AGB estimation. Then, a new XGBoost model was established using the final corrected AGB maps and climate data to estimate the AGB under different climate scenarios during the 2050s and 2070s. The results indicated that AGB estimation using the XGBoost model with correction via Kriging interpolation of the AGB residuals can significantly improve the accuracy of AGB estimation. The total AGB of the study area increased over time from 1999 to 2014, indicating that the forest quality improved in the study area. Under the different climate scenarios, the total AGB during the 2050s and 2070s was predicted to decline continuously with increasing of greenhouse gas emissions, indicating that greenhouse gas emissions have a negative impact on forest growth. The results of this study can provide data support for evaluating the ecological function and value of forest ecosystems, and for formulating reasonable forest management measures to mitigate the effects of climate change.

Linking green bond yields to the species composition of forests for improving forest quality and sustainability

Forests in India's Himalayan region are undergoing rapid degradation owing to various climatic and man-made stressors, such as invasive species spread and excessive lopping and harvesting of trees. Invasive plants, such as L. camara, pose significant threats to the rich biodiversity of the region due to their ability to replace native vegetation and spread rapidly under a warming climate. Additionally, chir pines (P. roxburghii) are replacing banj oak (Q. leucotrichophora) forests. Oak forests support rich biodiversity and are also preferred by the local communities for meeting their livelihood requirements. In this study, we ask whether green bonds could be utilized for financing the restoration of desirable forest species and enhancing their resilience to natural hazards such as fires. In particular, we explore the possibility of linking green bond yields to the species composition of forests for incentivizing the protection of oak forests. We develop an optimization model of community led restoration of oak and pine forests that are infested with L. camara. Restoring oak forests mitigates the risk of forest fires, improves forest water supply, and controls the spread of the invasive lantana. Findings indicate that such species composition based green bonds, when coupled with payments for ecosystem services programs, can indeed incentivize higher restoration efforts, resulting in an improvement in the biodiversity richness of the region.

Mapping Forest Stock Volume Based on Growth Characteristics of Crown Using Multi-Temporal Landsat 8 OLI and ZY-3 Stereo Images in Planted Eucalyptus Forest

Labeled as a fast-growing tree species, eucalyptus has outstanding carbon sequestration capacity. Forest stock volume (FSV) is regarded as an important parameter for evaluating the quality of planted eucalyptus forests. However, it is an intractable problem to map FSV of planted eucalyptus forests using optical images because of growth characteristics of the crown and low saturation levels. To improve the accuracy of FSV in planted eucalyptus forests, time series Landsat 8 OLI (LC8) images and ZY-3 stereo images were acquired in the study area. Additionally, then, three composite images were proposed using acquired Landsat 8 OLI images based on the size and shape of eucalyptus crowns, and several spectra variables were extracted from these composite images. Furthermore, corrected canopy height model (CCHM) was also extracted from ZY-3 stereo images. Meanwhile, four models (random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), and multiple linear regression (MLR)) were used to estimate the FSV with various variable sets using the importance of the alternative variables ranked by RF. The results show that the sensitivity between proposed spectral variables and FSV is significantly improved using proposed composed images based on the growth characteristics of the crown, especially for young eucalyptus forests. After adding CCHM and stand age to the optimal variable set, the average relative root mean square error (rRMSE) of estimated FSV decreased from 41.01% to 29.94% for single LC8 images and from 32.64% to 26.47% for proposed composite LC8 images, respectively. After using the variable set extracted from composite LC8 images, the number of samples with overestimated FSV was significantly decreased for the young forest. Furthermore, forest height plays an important role in improving the accuracy of mapping FSV, whether young or mature eucalyptus forest. It was also proved that composite images related to crown close and CCHM have great potential to delay the saturation phenomenon for mapping FSV in planted eucalyptus forest.

Mapping forest and site quality of planted Chinese fir forest using sentinel images.

Normally, forest quality (FQ) and site quality (SQ) play an important role in evaluating actual and potential forest productivity. Traditionally, these assessment indices (FQ and SQ) are mainly based on forest parameters extracted from ground measurement (forest height, age, density, forest stem volume (FSV), and DBH), which is labor-intensive and difficult to access in certain remote forest areas. Recently, remote sensing images combined with a small number of samples were gradually applied to map forest parameters because of the various advantages of remote sensing technology, such as low cost, spatial coverage, and high efficiency. However, FQ and SQ related to forest parameters are rarely estimated using remote sensing images and machine learning models. In this study, the Sentinel images and ground samples of planted Chinese fir forest located in the ecological “green-core” area of Changzhutan urban cluster, were initially employed to explore the feasibility of mapping the FQ and SQ. And then, four types of alternative variables (backscattering coefficients (VV and VH), multi-spectral bands, vegetation indices, and texture characteristics) were extracted from Sentinel-1A and Sentinel-2A images, respectively. After selecting variables using a stepwise regression model, three machine learning models (SVR, RF, and KNN) were employed to estimate various forest parameters. Finally, the FQ of the study region was directly mapped by the weights sum of related factors extracted by the factor analysis method, and the SQ was also extracted using mapped forest height and age. The results illustrated that the accuracy of estimated forest parameters (DBH, H, and Age) was significantly higher than FSV, FCC, and Age and the largest and smallest rRMSEs were observed from FSV (0.38~0.40) and forest height (0.20~0.21), respectively. Using mapped forest parameters, it also resulted that the rRMSEs of estimated FQ and SQ were 0.19 and 0.15, respectively. Furthermore, after normalization and grading, the grades of forest quality were mainly concentrated in grades I, II, and III in the study region. Though the accuracy of mapping FQ and SQ is limited by the saturation phenomenon, it is significantly proved that using machine learning models and Sentinel images has great potential to indirectly map FQ and SQ.

Effects of forest amount and fragmentation on different bird guilds reveal strategies of forest restoration in Three Gorges Reservoir area of Yangtze River, China

The construction of the Three Gorges Dam Project had changed the forest landscapes and threatened the bird diversity of the Three Gorges Reservoir (TGR) area. For restoring degraded ecosystems and protecting biodiversity, the government has carried out a series of ecological projects in recent years, which has significantly restored the forest of the TGR area. However, the fragmentation of forest landscapes in the TGR area was not clearly improved by these ecological projects, and we were concerned that some birds might be impacted. Therefore, in this research, we studied the effects of forest amount and fragmentation on diversity of different bird guilds, to develop the effective strategies of forest restoration for bird preservation in the TGR area. We first selected a series of forest landscapes where forest amount and fragmentation were independent of each other to conduct bird surveys, and then used structural equation models to examine their direct and indirect effects on species richness and relative abundance of four bird guilds (vertical-profile generalists, understory birds, edge users and ground users). The results showed that forest amount had direct positive effects on diversity of all bird guilds, and fragmentation had indirect negative effects on diversity of vertical-profile generalists and understory birds that was mediated through vegetation condition. We believe that to increase forest amount in the landscapes undoubtedly plays an important role in bird conservation. Meanwhile, we also need to be alert to the threats that fragmentation may pose to birds by reducing forest quality. We suggest that optimizing landscape configuration and improving forest quality should both be emphasized while restoring the forest. For example, restoring the areas between forest patches preferentially to increase connectivity in the landscapes, and transforming the degraded mono-specific forests into mixed forests through replanting to optimize the stand structure.

Improving aboveground biomass estimation of natural forests on the Tibetan Plateau using spaceborne LiDAR and machine learning algorithms

• ICESat-2 has the potential to improve the estimation accuracy and efficiency of forest aboveground biomass on a large scale. • The Google Earth Engine cloud platform was used for Sentinel-2 images acquisition and composition. • The optimized extreme learning machine achieved the highest estimation accuracy and reasonable forest aboveground biomass spatial distribution. Natural forests have the most complex structure and richest biodiversity among terrestrial ecosystems and are essential for maintaining the carbon balance and stability of the biosphere. Aboveground biomass (AGB) is a primary indicator used to evaluate forests and can directly measure forest growth and the quality of natural forests. Accurate and rapid AGB estimations can significantly improve the efficiency of forest management and deepen the understanding of the forest carbon cycle. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), one of the most recently launched spaceborne light detection and ranging (LiDAR) instruments, can penetrate forest canopies and obtain accurate, large-scale forest vertical parameter measurements. However, the discrete footprints offered by ICESat-2 cannot provide comprehensive spatial AGB distribution data. In this study, an optimized extreme learning machine (ELM) method was proposed to estimate the AGB of natural forests on the eastern Qinghai-Xizang Plateau, China. The synthetic Sentinel-2 images acquired from the Google Earth Engine (GEE) were synergized with ICESat-2 to realize continuous AGB mapping. To verify the effectiveness of the optimized method, support vector machine (SVM), k-nearest neighbor (kNN), and backpropagation (BP) neural network models were also established for comparison. The measured AGB data extracted from the forest management inventory (FMI) were used for model accuracy evaluation. The results show that the optimized ELM achieved the best estimation effect among all the analyzed models, with an R 2 value of 0.68 and a root mean square error (RMSE) value of 25.14 mg/ha. The optimized ELM obtained the minimum RMSE and greatly improved the AGB prediction efficiency. These findings prove the ability of ICESat-2 in estimating the AGB of natural forests, which can provide a new way for large-scale forest resources investigation in high-altitude areas with a harsh environment.

Evaluation of mangrove rehabilitation and afforestation in the southern coasts of Iran

The increasing multiple ecosystem services of mangrove forests, especially in the coastal regions have highlighted a need for conservation and afforestation of these forests. However, economic development and activities on the coasts have generated severe pollution issues that caused irreparable damages to the areas and quality of mangrove forests. As a result, rehabilitating the affected areas and forest planting are increasingly important, whereby some form of an assessment is needed to determine their sustainable performance and effectives. This study has used the indicators of forest resource sustainability, and the sustainability of planting sites to evaluate mangrove plantings in Iran's southern coast. Findings showed that there was a total of 47 mangrove planting sites on the coasts of the three provinces studied with an area of 9584.5 ha. There were 26 afforestation practice sites with an area of 5724 ha, and 21 combined rehabilitation and afforestation practice sites with an area of 3860.5 ha identified in this study. Approximately 76.6% of planting sites had been lost and the remaining areas had experienced an average density drop of 44%. Results of the stability class analysis revealed that 23 planting sites were in an extremely unsustainable state, 15 sites were considered as highly unsustainable, six sites were in a state of tendency to be unsustainable, whereas only three sites were regarded as sustainable. Findings from this study can assist managers and decision makers to review the site selection processes and pattern of successful planting sites, to facilitate better site selection and enhance the monitoring of mangrove rehabilitation or afforestation.

Random Survival Forests Incorporated by the Nadaraya-Watson Regression

An attention-based random survival forest (Att-RSF) is presented in the paper. The first main idea behind this model is to adapt the Nadaraya-Watson kernel regression to the random survival forest so that the regression weights or kernels can be regarded as trainable attention weights under important condition that predictions of the random survival forest are represented in the form of functions, for example, the survival function and the cumulative hazard function. Each trainable weight assigned to a tree and a training or testing example is defined by two factors: by the ability of corresponding tree to predict and by the peculiarity of an example which falls into a leaf of the tree. The second main idea behind Att-RSF is to apply the Huber's contamination model to represent the attention weights as the linear function of the trainable attention parameters. The Harrell's C-index (concordance index) measuring the prediction quality of the random survival forest is used to form the loss function for training the attention weights. The C-index jointly with the contamination model lead to the standard quadratic optimization problem for computing the weights, which has many simple algorithms for its solution. Numerical experiments with real datasets containing survival data illustrate Att-RSF.

Measurement and Spatial Econometric Analysis of Forest Carbon Sequestration Efficiency in Zhejiang Province, China

Maximizing the carbon sequestration of forested land is important for achieving carbon neutrality. Although some studies have discussed forest carbon sequestration efficiency (FCSE) from the perspective of total factor production, it is being increasingly recognized that forestland use regulates sequestration and emissions. When viewing forestland use as input and carbon emissions as output, there is a lack of empirical evidence on FCSE and its influencing factors. Here, a superefficiency slacks-based measurement model was applied to estimate FCSE for 66 counties in Zhejiang Province, China. The influencing factors and spatial spillover effects of FCSE were also analyzed using a spatial autocorrelation model. The findings showed that over the sample observation period, county FCSE ranged from 0.199 to 1.258, with considerable gaps. The global Moran’s I index showed that county-level FCSE was markedly spatially autocorrelated. Spatially, forestland use, cutting, pests, and diseases had negative spatial spillover effects on FCSE, whereas average annual temperature and precipitation displayed positive spillover effects. These findings suggest that the overall coordination of forest resource supervision and management among counties should be strengthened. The implementation of forestry management models aimed at consolidating or increasing forest carbon sequestration should be emphasized to improve forest quality, thereby promoting FCSE enhancement.

Evaluation of the Forest Quantity, Quality and Management through Gray Relational Analysis Method

Forests cover 30 per cent of the Earth’s land surface, almost four billion hectares. They are necessary to sustain human health, economic growth and the environment. Also, approximately 25 per cent of the global population depends on forests for food and work. The world population is expected to reach 9.6 billion by 2050. Therefore, there needs to be quick action at all levels to make sure that forests are managed in a way that is good for the environment and our way of life in the future. The Sustainable Forest Management Goals are included in the major headings of Sustainable Development Goals and the United Nations Strategic Plan for Forests 2017–2030. The data for the worldwide and six geographical areas were assessed using the Gray Relational Analysis (GRA) approach, which is one of the Multi-Criteria Decision Making methodologies. The major goal of the study is to use the GRA mathematical approach to assess data from 6 geographical areas, totalling 245 regions and nations, and 236 countries and regions worldwide. The second purpose is to contribute to the existing literature by expanding the geographical scope, number of indicators, and the time period covered by the study. The study also aims to provide information on new forest quality and management technologies, as well as the change of geographical areas over 30 years. South America consistently comes out on top in interregional comparisons. On the other hand, Oceania ranks last in the rankings. While the scores for 1990 increased markedly for all regions and worldwide in 2000, the performance values for the years 2000, 2010 and 2020 are fairly close to each other. The findings and methods of this study are aimed to be a useful resource for future researchers and policymakers.