Critical Height Sampling(CHS)estimates stand volume free from any model and tree form assumptions.Despite its introduction more than four decades ago,CHS has not been widely applied in the field due to perceived chall...Critical Height Sampling(CHS)estimates stand volume free from any model and tree form assumptions.Despite its introduction more than four decades ago,CHS has not been widely applied in the field due to perceived challenges in measurement.The objectives of this study were to compare estimated stand volume between CHS and sampling methods that used volume or taper models,the equivalence of the sampling methods,and their relative efficiency.We established 65 field plots in planted forests of two coniferous tree species.We estimated stand volume for a range of Basal Area Factors(BAFs).Results showed that CHS produced the most similar mean stand volume across BAFs and tree species with maximum differences between BAFs of 5-18m^(3)·ha^(−1).Horizontal Point Sampling(HPS)using volume models produced very large variability in mean stand volume across BAFs with the differences up to 126m^(3)·ha^(−1).However,CHS was less precise and less efficient than HPS.Furthermore,none of the sampling methods were statistically interchangeable with CHS at an allowable tolerance of≤55m^(3)·ha^(−1).About 72%of critical height measurements were below crown base indicating that critical height was more accessible to measurement than expected.Our study suggests that the consistency in the mean estimates of CHS is a major advantage when planning a forest inventory.When checking against CHS,results hint that HPS estimates might contain potential model bias.These strengths of CHS could outweigh its lower precision.Our study also implies serious implications in financial terms when choosing a sampling method.Lastly,CHS could potentially benefit forest management as an alternate option of estimating stand volume when volume or taper models are lacking or are not reliable.展开更多
The inherent challenges arising from variations in user-captured viewpoints and object orientation disparities in real-world scenarios pose significant difficulties in establishing robust correspondence relationships ...The inherent challenges arising from variations in user-captured viewpoints and object orientation disparities in real-world scenarios pose significant difficulties in establishing robust correspondence relationships between image pairs.Methods based on geometric transformation estimation usually perform affine transformation of the global image for viewpoint correction,which not only increases the time complexity but also generates a large number of redundant features.To solve this problem,this paper proposes an adaptive affine transformation model(AATM)to achieve robust image matching by dividing special regions with pixel information and employing feature extraction algorithms with different granularities.First,the input image is divided into significant and non-significant regions by an adaptive algorithm.Second,for the salient region,the feature point extraction is accelerated by optimizing the longitude angle sampling algorithm and constructing the affine invariant nonlinear scale space,introducing the Hessian integral image and box filter.Then,for the non-significant region of the weak texture scene through the uniform step sampling algorithm,a dense feature description can be obtained in the weak texture scenes,so that more robust features are extracted for both significant and non-significant regions.The results of extensive experiments on two datasets show that the AATM algorithm outperforms similar algorithms in terms of the number of correctly matched pairs,elapsed time,and root mean square error(RMSE),indicating that the AATM can obtain more robust matches in scenes with large angle tilting and scale transformations.展开更多
Background: The importance of structurally diverse forests for the conservation of biodiversity and provision of a wide range of ecosystem services has been widely recognised. However, tools to quantify structural div...Background: The importance of structurally diverse forests for the conservation of biodiversity and provision of a wide range of ecosystem services has been widely recognised. However, tools to quantify structural diversity of forests in an objective and quantitative way across many forest types and sites are still needed, for example to support biodiversity monitoring. The existing approaches to quantify forest structural diversity are based on small geographical regions or single forest types, typically using only small data sets.Results: Here we developed an index of structural diversity based on National Forest Inventory(NFI) data of BadenWurttemberg, Germany, a state with 1.3 million ha of diverse forest types in different ownerships. Based on a literature review, 11 aspects of structural diversity were identified a priori as crucially important to describe structural diversity. An initial comprehensive list of 52 variables derived from National Forest Inventory(NFI) data related to structural diversity was reduced by applying five selection criteria to arrive at one variable for each aspect of structural diversity. These variables comprise 1) quadratic mean diameter at breast height(DBH), 2) standard deviation of DBH, 3) standard deviation of stand height, 4) number of decay classes, 5) bark-diversity index, 6) trees with DBH ≥ 40 cm, 7) diversity of flowering and fructification, 8) average mean diameter of downed deadwood, 9) mean DBH of standing deadwood, 10) tree species richness and 11) tree species richness in the regeneration layer. These variables were combined into a simple,additive index to quantify the level of structural diversity, which assumes values between 0 and 1. We applied this index in an exemplary way to broad forest categories and ownerships to assess its feasibility to analyse structural diversity in large-scale forest inventories.Conclusions: The forest structure index presented here can be derived in a similar way from standard inventory variables for most other large-scale forest inventories to provide important information about biodiversity relevant forest conditions and thus provide an evidence-base for forest management and planning as well as reporting.展开更多
文摘Critical Height Sampling(CHS)estimates stand volume free from any model and tree form assumptions.Despite its introduction more than four decades ago,CHS has not been widely applied in the field due to perceived challenges in measurement.The objectives of this study were to compare estimated stand volume between CHS and sampling methods that used volume or taper models,the equivalence of the sampling methods,and their relative efficiency.We established 65 field plots in planted forests of two coniferous tree species.We estimated stand volume for a range of Basal Area Factors(BAFs).Results showed that CHS produced the most similar mean stand volume across BAFs and tree species with maximum differences between BAFs of 5-18m^(3)·ha^(−1).Horizontal Point Sampling(HPS)using volume models produced very large variability in mean stand volume across BAFs with the differences up to 126m^(3)·ha^(−1).However,CHS was less precise and less efficient than HPS.Furthermore,none of the sampling methods were statistically interchangeable with CHS at an allowable tolerance of≤55m^(3)·ha^(−1).About 72%of critical height measurements were below crown base indicating that critical height was more accessible to measurement than expected.Our study suggests that the consistency in the mean estimates of CHS is a major advantage when planning a forest inventory.When checking against CHS,results hint that HPS estimates might contain potential model bias.These strengths of CHS could outweigh its lower precision.Our study also implies serious implications in financial terms when choosing a sampling method.Lastly,CHS could potentially benefit forest management as an alternate option of estimating stand volume when volume or taper models are lacking or are not reliable.
基金Supported by the National Natural Science Foundation of China(No.61971162,61771186)the Natural Science Foundation of Heilongjiang Province(No.PL2024 F023)+1 种基金the Fundamental Scientific Research Funds of Heilongjiang Province(No.2022-KYYWF-1050)the Open Research Fund of National Mobile Communications Research Laboratory in Southeast University(No.2023D07).
文摘The inherent challenges arising from variations in user-captured viewpoints and object orientation disparities in real-world scenarios pose significant difficulties in establishing robust correspondence relationships between image pairs.Methods based on geometric transformation estimation usually perform affine transformation of the global image for viewpoint correction,which not only increases the time complexity but also generates a large number of redundant features.To solve this problem,this paper proposes an adaptive affine transformation model(AATM)to achieve robust image matching by dividing special regions with pixel information and employing feature extraction algorithms with different granularities.First,the input image is divided into significant and non-significant regions by an adaptive algorithm.Second,for the salient region,the feature point extraction is accelerated by optimizing the longitude angle sampling algorithm and constructing the affine invariant nonlinear scale space,introducing the Hessian integral image and box filter.Then,for the non-significant region of the weak texture scene through the uniform step sampling algorithm,a dense feature description can be obtained in the weak texture scenes,so that more robust features are extracted for both significant and non-significant regions.The results of extensive experiments on two datasets show that the AATM algorithm outperforms similar algorithms in terms of the number of correctly matched pairs,elapsed time,and root mean square error(RMSE),indicating that the AATM can obtain more robust matches in scenes with large angle tilting and scale transformations.
基金supported by a grant from the Ministry of Science,Research and the Arts of Baden-Württemberg(7533-10-5-78)to Jürgen BauhusFelix Storch received additional support through the BBW ForWerts Graduate Program
文摘Background: The importance of structurally diverse forests for the conservation of biodiversity and provision of a wide range of ecosystem services has been widely recognised. However, tools to quantify structural diversity of forests in an objective and quantitative way across many forest types and sites are still needed, for example to support biodiversity monitoring. The existing approaches to quantify forest structural diversity are based on small geographical regions or single forest types, typically using only small data sets.Results: Here we developed an index of structural diversity based on National Forest Inventory(NFI) data of BadenWurttemberg, Germany, a state with 1.3 million ha of diverse forest types in different ownerships. Based on a literature review, 11 aspects of structural diversity were identified a priori as crucially important to describe structural diversity. An initial comprehensive list of 52 variables derived from National Forest Inventory(NFI) data related to structural diversity was reduced by applying five selection criteria to arrive at one variable for each aspect of structural diversity. These variables comprise 1) quadratic mean diameter at breast height(DBH), 2) standard deviation of DBH, 3) standard deviation of stand height, 4) number of decay classes, 5) bark-diversity index, 6) trees with DBH ≥ 40 cm, 7) diversity of flowering and fructification, 8) average mean diameter of downed deadwood, 9) mean DBH of standing deadwood, 10) tree species richness and 11) tree species richness in the regeneration layer. These variables were combined into a simple,additive index to quantify the level of structural diversity, which assumes values between 0 and 1. We applied this index in an exemplary way to broad forest categories and ownerships to assess its feasibility to analyse structural diversity in large-scale forest inventories.Conclusions: The forest structure index presented here can be derived in a similar way from standard inventory variables for most other large-scale forest inventories to provide important information about biodiversity relevant forest conditions and thus provide an evidence-base for forest management and planning as well as reporting.
