Subjects: Geosciences >> Geography submitted time 2024-03-01 Cooperative journals: 《干旱区研究》
Abstract： As the largest carbon reservoir on land, forests play a crucial role in human life and development.Understanding the dynamic changes in forest resources and modernizing their sustainable development iscurrently a significant research focus. This study focuses on natural Picea forests in the Tianshan Mountains anduses ground measurement data, helicopter airborne LiDAR point cloud data, and Global Ecosystem DynamicsInvestigation (GEDI) data to construct a multisource fusion data framework. By utilizing deep learningalgorithms within the AutoKeras framework, the study aims to predict the regression model of multiple relativeheight quantiles of GEDI data and their aboveground biomass in the study area, thereby validating the feasibilityof GEDI data for large-scale aboveground biomass retrieval. The main conclusions are as follows: (1) GEDI dataare highly feasible for estimating forest aboveground biomass. Through automated deep learning algorithms andtraining and verification sets, the overall data achieve a coefficient of determination (R2) of 0.69, 0.63, and 0.67,respectively, along with a mean absolute error of 3.73 mg·hm−2, 4.22 mg·hm−2, and 3.89 mg·hm−2, demonstratinghigh prediction accuracy. (2) Helicopter LiDAR, an intermediate technology for estimating aboveground biomassusing GEDI data, exhibits a single tree recognition accuracy of over 0.75 across the study area. The studysuccessfully utilizes multimodal data fusion to quantitatively describe the structural parameters of the single treefoundation in the study area while verifying the potential of GEDI data for obtaining forest aboveground biomass.Moreover, the study provides a theoretical basis for estimating carbon sources and sinks, biomass, stock, forestmanagement, biodiversity protection, and other projects in similar areas, offering essential guidance, andfundamental data support.
Subjects: Environmental Sciences, Resource Sciences >> Basic Disciplines of Environmental Science and Technology submitted time 2019-08-02 Cooperative journals: 《干旱区地理》
Abstract：干旱是全球范围内影响最为广泛的自然灾害之一，其所导致的土壤沙漠化、荒漠化和盐碱化给生态环境造成不可逆的危害。通过对MODIS数据进行投影转换、去云等预处理的基础上，利用地形校正对[WTBX]TVDI模型进行改进，构建了改进型的温度植被干旱指数（mTVDI）用于新疆干旱区旱情监测。利用土壤实测数据对mTVDI及传统的TVDI模型进行对比验证。研究结果表明：（1） 利用EVI与校正后的LST构建的mTVDIE对干旱区旱情的敏感度最高，与实测土壤水分数据的相关性R2为0.74。（2） 从空间上看，新疆2015年旱情分布以塔里木盆地和准噶尔盆地为两个干旱中心，旱情状况由严重逐步向周围山区递减至湿润状态。从时间上看，新疆6月、7月和8月旱情最为严重。（3） 研究利用TRMM降水数据对基于mTVDIE反演的新疆旱情时空分布特征进行对比分析，结果表明二者所表现出的旱情时空分布较为一致，不同时间段内的降水量与mTVDIE之间具有一定的相关性，且均通过了P<0.01显著性检验。综上，基于TVDI所提出的mTVDIE 能够有效开展新疆干旱区旱情监测，且精度较高，从而为今后定量化开展大区域尺度旱情监测研究提供参考。
Subjects: Biology >> Botany >> Plant ecology, plant geography submitted time 2016-05-03
Abstract： Aims Accurate estimation of carbon density and storage is among the key challenges in evaluating ecosystem carbon sink potentials for reducing atmospheric CO2 concentration. It is also important for developing future conservation strategies and sustainable practices. Our objectives were to estimate the ecosystem carbon density and storage of Picea schrenkiana forests in Tianshan region of Xinjiang, and to analyze the spatial distribution and influencing factors. Methods Based on field measurements, the forest resource inventories, and laboratory analyses, we studied the carbon storage, its spatial distribution, and the potential influencing factors in Picea schrenkiana forest of Tianshan. Field surveys of 70 sites, with 800 m2 (28.3 m × 28.3 m) for plot size, was conducted in 2011 for quantifying arbor biomass (leaf, branch, trunk and root), grass and litterfall biomass, soil bulk density, and other laboratory analyses of vegetation carbon content, soil organic carbon content, etc. Important findings The carbon content of the leaf, branch, trunk and root of Picea schrenkianais varied from 46.56% to –52.22%. The vegetation carbon content of arbor and the herbatious/litterfall layer was 49% and 42%, respectively. The forest biomass of Picea schrenkiana was 187.98 Mg?hm–2, with 98.93% found in the arbor layer. The biomass in all layers was in the order of trunk (109.81 Mg?hm–2) > root (39.79 Mg?hm–2) > branch (23.62 Mg?hm–2) > leaf (12.76 Mg?hm–2). From the age-group point of view, the highest and the lowest biomass was found at the mature forest (48.70 Mg?hm–2) and young forest (30.72 Mg?hm–2), respectively. The carbon density and storage were 544.57 Mg?hm–2 and 290.84 Tg C, with vegetation portion of 92.57 Mg?hm–2 and 53.14 Tg C, and soil portion of 452.00 Mg?hm–2 and 237.70 Tg C, respectively. The spatial distribution of carbon density and storage appeared higher in the western areas than those in the eastern regions. In the western Tianshan Mountains (e.g., Yili), carbon density was the highest, whereas the central Tianshan Mountains (e.g., Manasi, Fukang, Qitai) also had high carbon density. In the eastern Tianshan Mountains (e.g., Hami), it was low. This distribution seemed consistent with the changes in environmental conditions. The primary causes of carbon density difference might be a combined effects of multiple environmental factors such as terrain, precipitation, temperature, and soil.
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