Management Of Forest Resources Research Articles

A FULLY AUTOMATIC FOREST PARAMETERS EXTRACTION AT SINGLE-TREE LEVEL: A COMPARISON OF MLS AND TLS APPLICATIONS

Abstract. Forests are vital for ecological, economic, and social reasons, and adopting sustainable forest management practices is necessary. While traditional forest monitoring techniques provide detailed data, they are time-consuming; conversely, geomatic techniques can provide more detailed data for forest resource management. This study aims to assess the suitability of Mobile Mapping Systems (MMS) with simultaneous localisation and mapping (SLAM) technology for precision forestry purposes in challenging environments. We compared the performance of MMS data with Terrestrial Laser Scanning (TLS) data and evaluated the Forest Structural Complexity Tool (FSCT), which was developed for TLS datasets, on MMS data. The case study area is a highly sloped coniferous forest in the Italian Alps affected by a severe fire in 2017. Data were processed using a fully automated open-source Python tool that detects each tree's position, Diameter at Breast Height (DBH), and height. The validation procedure was conducted with respect to the TLS point cloud manually segmented. The results show that using MMS with SLAM technology is suitable for precision forestry purposes in challenging environments and that FSCT performs well on MMS data.

Forest owner or shareholder? Ownership feelings in a jointly-owned forest

ABSTRACT Parcelisation-induced fragmentation is a common development trend in forest ownership in many Western countries. In Finland, the idea of a jointly-owned forest (JOF) has been introduced to facilitate the effective management of forest resource in parcelised and fragmented forest owning contexts. A JOF is an area of combined holdings intended for the practice of sustainable forestry for the benefit of the shareholders. The establishment of JOFs alters the core idea of ownership as it diminishes individual forest owners’ ability to control their holdings. We suggest that the feelings of ownership are important, yet under researched, aspects in the development and operation of JOFs. In this study, we focus on these ownership feelings by asking what kind of owner types are associated with JOFs. We build a qualitative typology of four owner types which we named active owners, investors, legacy fosterers, and detached owners. Our study contributes to the discussion on JOFs by showing that shared ownership can incorporate a variety of owner types with different ownership feelings. We claim that the need to understand these different owner types is important to facilitate the development of tools and policy instruments that will contribute to the establishment and management of jointly-owned forests.

A future EU overall strategy for agriculture and forest genetic resources management: Finding consensus through policymakers’ participation

The Genetic Resources Strategy for Europe (GRSE) is an overarching, integrated strategy for the conservation and use of plant, animal and forest genetic resources both the domesticated and wild to ensure more sustainable EU land use policies for the future. It was developed in a three-step consensus seeking iterative process involving EU, national and regional level policymakers, and umbrella organisations representatives. The GRSE provides the missing link between basic biodiversity management (genetic resources) and associated policies and strategies addressing agriculture, forestry and the environment. It also provides guidance for more effective and harmonised genetic resources management in the EU and beyond, and promotes stakeholders’ collaboration in all these areas. In this study, we use a foresight participatory approach to understand the perspectives of identified stakeholders in the adoption of the GRSE at local, national and European level. This methodology enabled researchers to discuss the role of genetic resources management in addressing geographical, economic and societal needs in Europe and beyond, and anticipate the long-term beneficial impacts. The study assessed the outlook for the adoption of the GRSE in the wider policymaking perspective. It applied a three-step Delphi methodology i) interviews, ii) survey, and iii) focus group discussion. It involved a 26-member policy makers panel representing EC and national-level policymakers, and umbrella organisations from across the European Union. Our study reveals a lack of consensus regarding the need to align policy and legal frameworks at national and European levels, but consensus was found in the need to jointly adopt national action plans across member states. Based on these results we identified three main priorities for addressing the gaps within current genetic resources management and conservation approaches: i) align fragmented policy for and develop legislation to harmonise the operational differences between European countries, ii) find funding mechanisms for improving genetic resources infrastructure, research and business development across Europe, and iii) raise awareness and collaboration to involve actors from different levels and sectors.

An Individual Tree Segmentation Method That Combines LiDAR Data and Spectral Imagery

The dynamic monitoring of forest resources is an integral component of forest resource management and forest eco-system stability maintenance. In recent years, LiDAR (Light Detection and Ranging) has been increasingly utilized in precision forest surveys due to its great penetrating ability and capacity to detect forest vertical structure information. However, the present airborne LiDAR data individual tree segmentation algorithms are not highly adaptable to forest types, particularly in mixed coniferous and broad-leaved forest zones, where the accuracy of individual tree extraction is low, and trees are incorrectly recognized and missed. In order to address these issues, in this study, spectral images and LiDAR data of a red pine conifer–broadleaf mixed forest in the Changbai Mountain Nature Reserve in Jilin Province were chosen, and the normalized point cloud was segmented iteratively using the distance-threshold-based individual tree segmentation method to obtain the initial segmented individual tree vertices. For individual trees with deviations in the initial vertex identification position, and unidentified individual trees, identification anchor points of real tree vertices are added within the canopy of the trees. These identification anchor points have strong position directivity in LiDAR data, which can mark the individual trees whose vertices were misidentified or missed during the initial individual tree segmentation process and identify these two tuples. The tree vertices may be inserted precisely based on the 3D shape of the individual tree point cloud, and the seed-point-based individual tree segmentation method is used to segment the normalized point cloud and finish the extraction of individual trees in red pine mixed conifer forests. The results indicate that, compared to the previous individual tree segmentation approach based on the relative spacing between individual trees, this study enhances the accuracy of individual tree segmentation from 83% to 96%. The extremely high segmentation accuracy indicates that the proposed method can accurately identify individual trees based on remote sensing techniques to segment forest individual trees, can provide a data basis for subsequent individual tree information extraction, and has great potential in practical applications.

Assessing and Mapping Forest Functions through a GIS-Based, Multi-Criteria Approach as a Participative Planning Tool: An Application Analysis

A relatively new planning tool in Italy is the Local Forest Plan, which stands at a broader level in the land planning scale compared to the Forest Management Plan but at a finer scale considering a Regional or even National Forest Plan. This intermediate scale was considered the most appropriate for working out a planning process based on a multi-criteria assessment of forest functions. The proper functioning of forest ecosystems can provide services conceived as benefits people can obtain through ecological processes that sustain and satisfy essential needs of human life. Four particular forest functions providing services were identified: protective, productive, naturalistic and touristic, respectively. A set of functional criteria, as well as attributes within criteria, were recognized to perform an “Analytic Hierarchy Process” (AHP). A specific application of this methodological approach was selected as a case study. This multi-criteria decision-making process involved the participation of five selected experts in a preliminary phase, followed by the participation of thirty representative stakeholders who contributed to the forest planning process and the subsequent selection of actions to be taken. Data related to forest types and management, physical and morphological features of the forested terrain and infrastructure such as forest roads, touristic trails and hiking pathways, together with natural protected areas or wildlife preservation areas, were explicitly located in space by using a “geographical information service” (GIS) software. The combined application of AHP and GIS can be considered as a significant methodological innovation presented in the case study, together with the implementation of a participative process aimed at the management of forest resources and the creation of possible new professional and entrepreneurial forest activities for the benefit of the entire residential community.

Natural Forests or Cultural Forests? Forest Changes within Italian Protected Areas in the Last 85 Years

The cultural dimension of many forests is not adequately considered by current forest definitions, policies, inventories, and management. Moreover, the concept of “protected area” as a synonym of “natural area” ignores that many forests today included in protected areas (PAs) have been managed and exploited for centuries. The abandonment of rural areas that occurred in the last 60 years in EU countries caused the expansion of forests, with the risk of a homogenization of forest typologies, the loss of cultural features, and the loss of biodiversity at the landscape scale. The aim of this study is to investigate the origin and evolution of forests within Italian PAs in the last 85 years through GIS-based spatial analyses. In 1936, only a minority of the surface included in current PAs was covered by forests, ranging from 32% in Natura 2000 sites to 35% in regional PAs. Forest surface in Italian PAs increased in the last 85 years (from +33.1% in regional PAs to +45.2% in national PAs), mainly due to secondary successions, with a growth rate in Natura 2000 sites equal to 8709 ha/year. In regional PAs, 40.1% of the current forest cover originated after 1936, and this percentage increases to 43% in Natura 2000 sites and to 44.7% in national PAs. The altitudinal range of 500–1000 m a.s.l. is the one most affected by forest spread. In addition, most of the forests in 1936 were regularly managed as coppice: 44% in national PAs, 56% in Natura 2000 sites, and 62% in regional PAs. The study confirms that most of the forest surface included in Italian PAs has a clear cultural origin and is the consequence of the abandonment of pastures and/or cultivations that occurred in the last 85 years. PA management should take into consideration that open areas are shrinking and that the diversity of habitats and forest communities is strongly linked to the persistence of traditional human activities. The current forest characteristics (species composition, vertical and horizontal structure) are also the result of past management, and only active management of forest resources can counteract the homogenization of forest communities and landscape structure.

Updating forest road networks using single photon LiDAR in northern Forest environments

Abstract Knowledge about the condition and location of forest roads is important for forest management. Coupling accurate forest road information with planning and conservation strategies supports forest resource management. In Canada, spatial data of forestry road networks are available provincially; however, they lack spatial accuracy, and up-to-date information on key attributes such as road width is missing. In this study, we apply a novel approach to update forest road networks and characterize road conditions in Ontario’s Boreal and Great Lakes—St. Lawrence (GLSL) Forest regions. We use airborne laser scanning (ALS), to facilitate the identification of forest roads across densely forested landscapes. We categorized roads into four classes based on driveable width, edge vegetation, as well as surface and edge degradation as derived from high-density Single Photon LiDAR (SPL) data. Using a novel road extraction method, we produced a road probability raster and map road centerlines. We validated road location and attribute information using Global Navigation Satellite System (GNSS) ground truth data in two Ontario forest management units, in the boreal forest and the GLSL. Road segments in some regions have been altered to account for land cover changes, such as flooding or fallen trees. In other situations, the road path may deviate from the planned layout of the road, which is not always followed in the field. Our results highlight inaccuracies in the existing road networks, with 30 per cent of ‘Full access’ roads and 29 per cent of ‘Partial access’ roads being undriveable by standard vehicles and 45 per cent of ‘Status unknown’ roads, which make up 48 per cent of the pre-existing network, being driveable by standard vehicles. Results show that the average positional accuracy of updated road centerlines is 0.4 m, and the average road width error is 2 m. The production of spatially accurate forest road networks and road attribute information is important for characterizing large road networks for which often minimal information is available.

Evaluating semi- and nonparametric regression algorithms in quantifying stem taper and volume with alternative test data selection strategies

Abstract Accurately quantifying stem taper is essential to predict diameter at any given height along the stem and to estimate tree volume for various sections of the stem. With increased computing power, semi- and nonparamatric methods have been proposed as alternative approaches for modelling tree taper. The main objective of this study was to assess the accuracy of stem taper predicted for four pine and four hardwood species by semi- and nonparametric models. Specifically, generalized additive models (GAM), random forests (RF) and regression-enhanced random forests (RERF) were compared with two widely-used parametric models: variable-exponent function (VAR) and segmented polynomial regression model (SEG). Test datasets selected from four different data splitting methods were used to examine the prediction accuracy of the models. Results showed that all examined models can be used to quantify stem taper and volume for all species when prediction is limited to be within the range of tree sizes used in model building. The nonparametric RF algorithm generally produced higher bias and lower precision than the semiparametric (GAM and RERF) and parametric models (VAR and SEG). For all species, GAM, VAR and SEG provided more robust predictions of stem taper than RF and RERF algorithms, especially when small or large trees were withheld for model testing. The data splitting strategies used in this work provide an efficient ‘stress test’ to evaluate model performance when collecting an independent test dataset is not feasible. The findings of this work provide additional insights for forest practitioners and resource managers to select appropriate methods in stem taper modelling.

Resetting the forestry commons: Constitutionality as a conflict resolution strategy in African woodlands

This paper comparatively analyses three case studies in Mali, Senegal and Tanzania regarding common property institutions for forest commons and their transformations during colonial and post-colonial times. Then common pool resource management was fragmented and common property was increasingly turned into state and private property and local control over resources was removed. This led to grabbing processes by foreign investors and conflicts between local and outside users of forest resources and degradation of these resources. The paper further outlines the reactions to these processes. It also highlights the neglect of the gender and marginal groups dimensions in studies of constitutionality (developing a sense of local ownership in the bottom-up institution crafting processes for the governance of the commons). In the cases of Mali and Senegal these constitutionality processes were for managing the forest resources. This led to a mitigation of existing resource conflicts. In Tanzania, out of a land-use conflict, local people had managed to create a kind of an informal convention with a British investor about land use in the valleys, which however remains fragile. The conflict arose because women had largely been left out in land deal negotiations and institution-building about the acquired land, as well as were confronted with institution-shopping by the investor and from below by fellow community- and family-men. This impacted heavily on their workload and hampered their abilities to fulfil their care work. Thus, this paper fills the gap on gender (and more differentiated – intersectionality, and generally the involvement of marginal groups) in the constitutionality process to support the “resetting of the forest commons”. These have been neglected factors that need to be addressed for successful forest resources management and governance.

Mapping tree species diversity in temperate montane forests using Sentinel-1 and Sentinel-2 imagery and topography data

Detailed information on spatial patterns of tree species diversity (TSD) is essential for biodiversity assessment, forest disturbance monitoring, and the management and conservation of forest resources. TSD mapping approaches based on the Spectral Variability Hypothesis (SVH) could provide a reliable alternative to image classification methods. However, such methods have not been tested in large-scale TSD mapping using Sentinel-1 and Sentinel-2 images. In this study, we developed a new workflow for large-scale TSD mapping in an approximately 4000 km2 temperate montane forest using Sentinel-1 and Sentinel-2 imagery-based heterogeneity metrics and topographic data. Through a systematic comparison of model performance in 24 prediction scenarios with different combinations of input variables, and a correlation analysis between six image heterogeneity metrics and two in-situ TSD indicators (species richness S and Shannon-Wiener diversity H′), we assessed the effects of vegetation phenology, image heterogeneity metrics, and sensor type on the accuracy of TSD predictions. Our results show that (1) the combination of Sentinel-1 and Sentinel-2 imagery produced higher accuracy of TSD predictions compared to the Sentinel-2 data alone, and that the further inclusion of topographic data yielded the highest accuracy (S: R2 = 0.562, RMSE = 1.502; H′: R2 = 0.628, RMSE = 0.231); (2) both Multi-Temporal and Spectral-Temporal-Metric data capture phenology-related information of tree species and significantly improved the accuracy of TSD predictions; (3) texture metrics outperformed other image heterogeneity metrics (i.e., Coefficient of Variation, Rao's Q, Convex Hull Volume, Spectral Angle Mapper, and the Convex Hull Area), and the enhanced vegetation index (EVI) derived image heterogeneity metrics were most effective in predicting TSD; and (4) the spatial distribution of TSD showed a clear decrease trend along the altitudinal gradient (r = −0.61 for S and − 0.45 for H′) and varied significantly among forest types. Our results suggest a good potential of the SVH-based approaches combined with Sentinel-1 and Sentinel-2 imagery and topographic data for large-scale TSD mapping in temperate montane forests. The TSD maps generated in our study will be valuable for forest biodiversity assessments and for developing management and conservation measures.

Kebijakan Hukum Rencana Induk Pembangunan Industri dalam Pemanfaatan Potensi Sumber Daya Industri Kehutanan di Indonesia

This article's objective is to review and discuss the legal guidelines of Indonesia's Master Plan for Industrial Development for the Use of Potential Forestry Industry Resources. The normative methods of legal research that were used to write this article. Forests are important natural resources that provide many ecological, economic and social benefits. Paper is just one of many products and services for which the forestry industry provides raw materials, wood, furniture and building materials. The industry is subject to various laws and regulations governing the management, harvesting and utilization of forest resources. This paper provides an overview of industrial law in the forestry industry, covering key laws and regulations, their impact on the industry, and the legal framework. Industry forest law refers to the legal framework governing the management and utilization of forests for industrial purposes. It includes regulations on logging, reforestation, forest conservation, and the protection of indigenous communities. The aim is to balance the economic benefits of industrial forestry with the need to ensure sustainable forest management and protect the environment.

Adaptive forest management

Adaptive management is the basic concept of forest ecosystem management strategy in conditions of increased risks. Therefore, there is a need to develop key theories and information on which adaptive forest management is based. Adaptive management of forest resources has become the premise of modern forestry development strategies in Europe, and the main ideas about this method of management have developed primarily on the knowledge of forest ecology. Adaptive management is the monitoring of activities (monitoring) in order to adjust future activities to the desired effects of management. The main causes that have influenced the application of adaptive management in forestry are related to strategic forestry principles and global challenges. Forest ecosystems are exposed to natural and social risks that cause disturbances and adverse effects of management. In order to reduce the risk for the realization of strategic goals in forestry, there is a need to develop management mechanisms for the adaptation of forest ecosystems. Global activities, in addition to the implementation of measures for mitigation (mitigation) of climate change, include measures for adaptation (adaptation) to climate change, especially for underdeveloped countries and developing countries, in order to ensure sustainable development and mitigate the negative consequences for forest ecosystems. It is necessary to go beyond the management framework based mainly on a simplified balance between volume, increment and stature, and to systematically integrate the results of all interventions and external influences on forests in order to improve the condition of forests and adapt to changes. The best indicator of endangerment of species and forest plant communities is the reduction of their range and biodiversity. Most climate models predict large changes within forests due to the high rates of climate change and the limitations of adaptation of many tree species to climatic extremes. Forests are slowly adapting to new conditions, especially due to increasing temperatures, increasing carbon dioxide concentrations and increasing nitrate deposition. The most influential climate variable for forests is the one that determines wind disturbance. The key to forest management in risk conditions lies in increasing the participation of more resistant species adapted to future climatic conditions, ie in promoting the diversity of tree species and their provenances and the heterogeneity of structural forms of forests. To create such forests, it isnecessary to cultivate forests whose characteristics are close to natural and potential forest communities for appropriate habitat conditions and indigenous species. Adaptive forest management implies a multidisciplinary approach and a four-phase cycle of strengthening understanding of complex biophysical, socioeconomic and political systems with constant monitoring (ecosystem control) and acquiring new empirical knowledge whose positive effects are applied in practice. The advantage of adaptive management is that plans and activities are adjusted based on the results of monitoring in the newly created unforeseen conditions.

Identification of Tree Species in Forest Communities at Different Altitudes Based on Multi-Source Aerial Remote Sensing Data

The accurate identification of forest tree species is important for forest resource management and investigation. Using single remote sensing data for tree species identification cannot quantify both vertical and horizontal structural characteristics of tree species, so the classification accuracy is limited. Therefore, this study explores the application value of combining airborne high-resolution multispectral imagery and LiDAR data to classify tree species in study areas of different altitudes. Three study areas with different altitudes in Muyu Town, Shennongjia Forest Area were selected. Based on the object-oriented method for image segmentation, multi-source remote sensing feature extraction was performed. The recursive feature elimination algorithm was used to filter out the feature variables that were optimal for classifying tree species in each altitude study area. Four machine learning algorithms, SVM, KNN, RF, and XGBoost, were combined to classify tree species at each altitude and evaluate the accuracy. The results show that the diversity of tree layers decreased with the altitude in the different study areas. The texture features and height features extracted from LiDAR data responded better to the forest community structure in the different study areas. Coniferous species showed better classification than broad-leaved species within the same study areas. The XGBoost classification algorithm showed the highest accuracy of 87.63% (kappa coefficient of 0.85), 88.24% (kappa coefficient of 0.86), and 84.03% (kappa coefficient of 0.81) for the three altitude study areas, respectively. The combination of multi-source remote sensing numbers with the feature filtering algorithm and the XGBoost algorithm enabled accurate forest tree species classification.

Remote Sensing and GIS tools for Forest Resource Management-A Review

Global importance and thus need to quantify the status of forest dynamics is increasing day by day. It is more so in the case of a country like India which is on the deficit side of forest cover in countering ecological balance. Information required for forest resource management includes extant, type of forest, felling/cutting, deforestation, afforestation, forest fire, forest flood, and extraction of bio-physical parameters like age of plantation, total biomass, canopy density etc. Widespread, vast span and difficult terrain, and inaccessibility makes it difficult to collect timely and periodic information about above mentioned parameter through conventional means. Forest mapping is one of the fields that witnessed the earliest application of remote sensing and GIS for nonmilitary purposes. using remote sensing data and GIS techniques, a forest manager can generate information regarding forest cover, types of forest present within an area of interest, human encroachment extent into forest land/protected areas, encroachment of desert-like conditions and so on [3].For the purpose of understanding the working of remote sensing and GIS, the basics are explained below.

A dataset of groundwater level in the small watershed of Dinghushan Forest (2002 – 2020)

Groundwater is one of the important components in the hydrological cycle of the basin. Its dynamic change is of great value for understanding the hydrological regulation mechanism, the cycle of material elements and ecosystem service function of terrestrial ecosystem. Covered with subtropical monsoon evergreen broad-leaved forest and its transitional vegetation types, Dinghushan National Nature Reserve has been well protected since the 1950s. Dinghushan Forest Ecosystem Research Station set up four groundwater level observation wells downstream of the complete catchment area of Dinghushan Nature Reserve according to the observation specifications and quality control requirements of terrestrial ecosystem water environment, so as to carry out long-term standardized positioning observation of the depth of groundwater level. This dataset collates the long-term monitoring data of four groundwater level observation wells from 2002 to 2020. It is aimed to provide basic data support for the study of groundwater response process, the evaluation of hydrological service function of forest ecosystem and the sustainable management of forest resources under the background of global climate change.

Forest Security Appraisal of Community Management Interventions Against Threats of Forest Commons at Offinso North District in Ghana

The paper examines the viability of community forestry and how community participation affects the effectivenessof forest resource security and management. Both qualitative and quantitative techniques with stakeholders’observations and perceptions of managers’ responses to major threats of forest commons were employed in thisstudy. The paper establishes forest security through comparative analysis and discourse of solely governmentmanagement intervention, solely community (private) management intervention and government-communitypartnership for forest management arrangements which are occurring in the Offinso Forest District of Ghana.The paper concludes that, community involvement in forest management interventions delivers better protectionof forest commons; and the extent and autonomy of local community to participate actively in forest resourcemanagement significantly influences resilience against threats of forest commons. Hence, stronger communityparticipation enhances the effectiveness of forest security efforts. Nevertheless, viability of communityparticipation to deliver effective forest resource security is influenced by adequacy of resources in support ofcommunity forestry, empowerment and sense of ownership of communities for forest common management.

Quercus suber Allometry in the West Mediterranean Basin

The necessity for accurate biomass estimates is greater than ever for the sustainable management of forest resources, which is an increasingly pressing matter due to climate change. The most used method to estimate biomass for operational purposes is through allometric equations. Typically, each country develops their own models to be applied at the local scale because it is more convenient. But, for Quercus suber, a joint regional model can be more beneficial, since the species is distributed across the Mediterranean and is challenging to account for due to felling limitations and the nature of mature cork biomass itself. We found that these characteristics are reflected in the biomass datasets and compatibility was, perhaps, the largest impediment to such a model. The use of dummy variables to differentiate between countries, as well as compromises in the limits of biomass compartments, allowed us to develop two joint models to estimate aboveground biomass in Portugal, Spain and Tunisia. One model as a function of diameter and another as a function of diameter and total tree height. In addition, we developed a separate model for roots (modelling efficiency of fitting = 0.89), since it was not possible to assure additivity of the whole tree. All coefficients were estimated using Seemingly Unrelated Regressions (SUR) and model fitting assured additivity in the aboveground compartments—leaves and woody biomass (modelling efficiency of fitting = 0.89 and 0.93, respectively). This work proves that it is possible to have a biologically sound and efficient model for the three countries, despite differences in the observed allometric patterns.

A Multicriteria Analysis to Support Natural Resource Governance: The Case of Chestnut Forests

Efficient natural resource management prevents and reduces negative impacts, such as environmental damage, misappropriation of resources, and conflicts; several strategies can be leveraged to conserve, protect, and enhance natural resources. Multicriteria decision analysis (MCDA) is useful in providing solutions to addressing decision choice problems. In this study, the natural resource under evaluation is the chestnut forest, with the objective of valorising its supply chains. The methodology applied is A’WOT, which allows previously identified factors, using a qualitative SWOT matrix, to be ordered through an objective quantification using the AHP (analytic hierarchy process), which is a multicriteria decision support method. The survey was conducted with a group of chestnut resource (n = 20) experts. The SWOT matrix identified a total of 20 factors: 6 strengths and 6 weaknesses and 4 factors each for opportunities and threats. The results express a clear stakeholder interest, which identifies the significant role of civil society in directing management choices for the provision and enhancement of ecosystem and vocational services. This study evaluated the adaptability of decision support tools applied to a real case of forest resource management to identify and order factors useful to enhance the resource and stimulate the supply chains to achieve greater added value. In a general sense, the methodological potential emerged to replicate or improve the research in other geographical regions, whether regional or extraregional, or even on a larger scale, such as on a national level.

Comparison between biophysical analysis model and dimidiate pixel model for the estimation of forest canopy density

Forest canopy density (FCD) is an essential factor in forest resources investigation and plays a vital role in forest resources management. To replace the traditional time-consuming and labor-intensive survey methods, it is of great importance to study how to select a suitable model at specific temporal and spatial scales for rapid and accurate estimation of FCD in large areas. To reduce workload and errors, we present an approach combing Landsat 5 TM data and biophysical analysis algorithms without considering thermal index (TI) information to model FCD in a temperate forest in Northern China. The scaled vegetation index (SVD) and scaled shadow index were used as variables for modeling and mapping FCD. The dimidiate pixel model based on the normalized difference vegetation index was also used to estimate FCD. We compared the forest management inventory data of 2012 with the FCD results from the biophysical analysis model and the dimidiate pixel model. The results showed that the biophysical analysis model (prediction precision P = 93.62 % , coefficient of determination R2 = 0.886, root mean square error RMSE = 0.057) has higher accuracy than the dimidiate pixel model (P = 89.92 % , R2 = 0.725, RMSE = 0.078) in estimating FCD. This study shows that the biophysical analysis model without TI information has better performance than the dimidiate pixel model, and can be used for monitoring large scale FCD.

AMDNet: A Modern UAV RGB Remote-Sensing Tree Species Image Segmentation Model Based on Dual-Attention Residual and Structure Re-Parameterization

Tree species classification is an important and challenging task in image recognition and the management of forest resources. Moreover, the task of tree species classification based on remote sensing images can significantly improve the efficiency of the tree species survey and save costs. In recent years, many large models have achieved high accuracy in the task of tree species classification in an airborne remote-sensing manner, but due to their fixed geometric structure, traditional convolutional neural networks are inherently limited to the local receptive field and can only provide segmental context information. The limitation of insufficient context information greatly affects the segmentation accuracy. In this paper, a dual-attention residual network (AMDNet) and a re-parameterized model approach are proposed to capture the global context information, fuse the weight, reduce the model volume, and maintain the computational efficiency. Firstly, we propose MobileNetV2 as the backbone network for feature extraction to further improve the feature identification by modeling semantic dependencies in the spatial dimension and channel dimension and adding the output of the two attention modules. Then, the attention perception features are generated by stacking the attention modules, and the in-depth residual attention network is trained using attention residual learning, through which more accurate segmentation results can be obtained. Secondly, we adopt the approach of structure re-parameterization, use a multi-branch topology for training, carry out weighted averaging on multiple trained models, and fuse multiple branch modules into a completely equivalent module in inference. The proposed approach results in a reduction in the number of parameters and an accelerated inference speed while also achieving improved classification accuracy. In addition, the model training strategy is optimized based on Transformer to enhance the accuracy of segmentation. The model was used to conduct classification experiments on aerial orthophotos of Hongya Forest Farm in Sichuan, China, and the MIOU of tree species recognition using the test equipment reached 93.8%. Compared with current models such as UNet, our model exhibits a better performance in terms of both speed and accuracy, in addition to its enhanced deployment capacity, and its speed advantage is more conducive to real-time segmentation, thereby representing a novel approach for the classification of tree species in remote sensing imagery with significant potential for practical applications.