中国生物多样性核心监测指标遥感产品体系构建与思考

生物多样性的稳定维持关乎人类生存发展与地球健康。生物多样性核心监测指标(Essential Biodiversity Variables,EBVs)旨在结合地面调查与遥感技术,为大尺度、长时间序列的生物多样性监测提供新的解决方案。然而,目前学界仍然缺乏一套国家尺度标准化EBVs遥感监测产品数据集,以进行生物多样性评估。本研究旨在对中国生物多样性核心监测指标遥感产品进行体系构建与思考,首先综述了目前EBVs的遥感研究概况,并根据EBVs研究文献的数量进行调研分析;同时,本文在已有遥感生物多样性产品优先标准的基础上,添加了“可重复性”的新标准,并据此构建了中国EBVs遥感产品体系与监测数据集的指标清单,最终对中国EBVs遥感研究存在的问题进行思考与讨论。本研究可为中国的生物多样性遥感监测提供科学依据,有望为中国生物多样性政策的制定提供支撑。

Patterns and ecological determinants of woody plant height in eastern Eurasia and its relation to primary productivity

Aims Plant height is a key functional trait related to aboveground bio-mass,leaf photosynthesis and plant fitness.However,large-scale geographical patterns in community-average plant height(cAPH)of woody species and drivers of these patterns across different life forms remain hotly debated.Moreover,whether cAPH could be used as a predictor of ecosystem primary productivity is unknown.Methods We compiled mature height and distributions of 11422 woody spe-cies in eastern Eurasia,and estimated geographic patterns in cAPH for different taxonomic groups and life forms.then we evaluated the effects of environmental(including current climate and historical climate change since the Last Glacial Maximum(LGM))and evolutionary factors on cAPH.Lastly,we compared the predictive power of cAPH on primary productivity with that of LiDAR-derived canopy-height data from a global survey.Important Findings Geographic patterns of cAPH and their drivers differed among taxonomic groups and life forms.the strongest predictor for cAPH of all woody species combined,angiosperms,all dicots and deciduous dicots was actual evapotranspiration,while temperature was the strongest pre-dictor for cAPH of monocots and tree,shrub and evergreen dicots,and water availability for gymnosperms.Historical climate change since the LGM had only weak effects on cAPH.No phylogenetic signal was detected in family-wise average height,which was also unrelated to the tested environmental factors.Finally,we found a strong correlation between cAPH and ecosystem primary productivity.Primary productivity showed a weaker relationship with cAPH of the tallest species within a grid cell and no relationship with LiDAR-derived canopy height reported in the global survey.Our findings suggest that current climate rather than historical climate change and evolutionary history determine the geographical patterns in cAPH.However,the relative effects of climatic factors representing environmental energy and water availability on spatial variations of cAPH vary among plant life forms.Moreover,our results also suggest that cAPH can be used as a good predictor of ecosystem primary productivity.

Quantifying individual tree growth and tree competition using bi-temporal airborne laser scanning data:a case study in the Sierra Nevada Mountains,California

Improved monitoring and understanding of tree growth and its responses to controlling factors are important for tree growth modeling.Airborne Laser Scanning(ALS)can be used to enhance the efficiency and accuracy of large-scale forest surveys in delineating three-dimensional forest structures and under-canopy terrains.This study proposed an ALSbased framework to quantify tree growth and competition.Bi-temporal ALS data were used to quantify tree growth in height(ΔH),crown area(ΔA),crown volume(ΔV),and tree competition for 114,000 individual trees in two conifer-dominant Sierra Nevada forests.We analyzed the correlations between tree growth attributes and controlling factors(i.e.tree sizes,competition,forest structure,and topographic parameters)at multiple levels.At the individual tree level,ΔH had no consistent correlations with controlling factors,ΔA andΔV were positively related to original tree sizes(R>0.3)and negatively related to competition indices(R<−0.3).At the forest-stand level,ΔH andΔA were highly correlated to topographic wetness index(|R|>0.7),ΔV was positively related to original tree sizes(|R|>0.8).Multivariate regression models were simulated at individual tree level forΔH,ΔA,andΔV with the R2 ranged from 0.1 to 0.43.The ALS-based tree height estimation and growth analysis results were consistent with field measurements.

Nonscalability of Fractal Dimension to Quantify Canopy Structural Complexity from Individual Trees to Forest Stands

Canopy structural complexity is a critical emergent forest attribute,and light detection and ranging(lidar)-based fractal dimension has been recognized as its powerful measure at the individual tree level.However,the current lidar-based estimation method is highly sensitive to data characteristics,and its scalability from individual trees to forest stands remains unclear.This study proposed an improved method to estimate fractal dimension from lidar data by considering Shannon entropy,and evaluated its scalability from individual trees to forest stands through mathematical derivations.Moreover,a total of 280 forest stand scenes simulated from the terrestrial lidar data of 115 trees spanning large variability in canopy structural complexity were used to evaluate the robustness of the proposed method and the scalability of fractal dimension.The results show that the proposed method can significantly improve the robustness of lidar-derived fractal dimensions.Both mathematical derivations and experimental analyses demonstrate that the fractal dimension of a forest stand is equal to that of the tree with the largest fractal dimension in it,manifesting its nonscalability from individual trees to forest stands.The nonscalability of fractal dimension reveals its limited capability in canopy structural complexity quantification and indicates that the power-law scaling theory of a forest stand underlying fractal geometry is determined by its dominant tree instead of the entire community.Nevertheless,we believe that fractal dimension is still a useful indicator of canopy structural complexity at the individual tree level and might be used along with other stand-level indexes to reflect the“tree-to-stand”correlation of canopy structural complexity.