Quantification of Above-Ground Biomass and Carbon Sequestration Potential of Roadside Trees in the Plateau Department of Benin Republic

Roadside trees are effective natural solutions for mitigating climate change. Despite the usefulness of trees to carbon sequestration, there is a dearth of information on the estimation of biomass and carbon stock for roadside trees in the study area. This study aimed to estimate the carbon stock and carbon dioxide equivalent of roadside trees. A complete enumeration of trees was carried out in Kétou, Pobè and Sakété within the communes of the Plateau Department, Bénin Republic. Total height and diameter at breast height were measured from trees along the roads while individual wood density value was obtained from wood density database. The allometric method of biomass estimation was adopted for the research. The results showed that the total estimations for above-ground biomass, carbon stock and carbon equivalent from all the enumerated roadside trees were 154.53 mt, 72.63 mt and 266.55 mt, respectively. The results imply that the roadside trees contain a substantial amount of carbon stock that can contribute to climate change mitigation through carbon sequestration.

Estimation of above-ground biomass and carbon stock of an invasive woody shrub in the subtropical deciduous forests of Doon Valley,western Himalaya,India

This study describes the different parameters used to derive the allometric equation for calculating the biomass of an invasive woody shrub Lantana camara L.from the subtropical conditions of western Himalaya.It identifies the most accurate and convenient method for biomass calculation by comparing destructive with nondestructive methodology.Different parameters were measured on a wide range of Lantana from different community levels for the non-destructive calculation of total aboveground biomass.Different explanatory variables were identified and measured such as basal diameter either as a single independent variable or in combination with plant height.The other suitable combinations of available independent variables include crown length,crown width,crown area,crown volume and coverage of the plant.Amongst the wide range of allometric equations used with different variables,the equation with D2 H as a variable was found to be the most suitable estimator of biomass calculation for Lantana.Sahastradhara,being the most disturbed area due to its high tourist activity round the year,showed maximum coverage（58.57 % ha-1）,highest biomass（13,559.60 kg ha-1） and carbon density（6,373.01 kg ha-1）of Lantana.The degree of Lantana’s invasiveness in subtropical conditions was also calculated on the basis of importance value index（IVI）.The maximum IVI（22.77）and mean coverage（26.8 % ha-1） was obtained from the areas near Jolly Grant airport,indicating that physically disturbed areas are more suitable for the growth of Lantana,which may significantly increase shrub biomass.The importance of incorporating allometric equations in calculation of shrub biomass,and its role in atmospheric carbon assimilation has thus been highlighted through the findings of this study.

Contrasting changes in above- and below-ground biomass allocation across treeline ecotones in southeast Tibet

Under conditions of a warmer climate,the advance of the alpine treeline into alpine tundra has implications for carbon dynamics in mountain ecosystems.However,the above- and below-ground live biomass allocations among different vegetation types within the treeline ecotones are not well investigated.To determine the altitudinal patterns of above-/below-ground carbon allocation,we measured the root biomass and estimated the above-ground biomass(AGB) in a subalpine forest,treeline forest,alpine shrub,and alpine grassland along two elevational transects towards the alpine tundra in southeast Tibet.The AGB strongly declined with increasing elevation,which was associated with a decrease in the leaf area index and a consequent reduction in carbon gain.The fine root biomass(FRB) increased significantly more in the alpine shrub and grassland than in the treeline forest,whereas the coarse root biomass changed little with increasing altitudes,which led to a stable below-ground biomass(BGB) value across altitudes.Warm and infertile soil conditions might explain the large amount of FRB in alpine shrub and grassland.Consequently,the root toshoot biomass ratio increased sharply with altitude,which suggested a remarkable shift of biomass allocation to root systems near the alpine tundra.Our findings demonstrate contrasting changes in AGB and BGB allocations across treeline ecotones,which should be considered when estimating carbon dynamics with shifting treelines.

Biomass and dominance of conservative species drive above-ground biomass productivity in a mediterranean-type forest of Chile

Background: Forest productivity has a pivotal role in human well-being. Vegetation quantity, niche complementarity, mass-ratio, and soil resources are alternative/complementary ecological mechanisms driving productivity. One challenge in current forest management depends on identifying and manipulating these mechanisms to enhance productivity. This study assessed the extent to which these mechanisms control aboveground biomass productivity(AGBP) of a Chilean mediterranean-type matorral. AGBP measured as tree aboveground biomass changes over a 7-years period, was estimated for twelve 25 m × 25 m plots across a wide range of matorral compositions and structures. Variables related to canopy structure, species and functional diversity, species and functional dominance, soil texture, soil water and soil nitrogen content were measured as surrogates of the four mechanisms proposed. Linear regression models were used to test the hypotheses. A multimodel inference based on the Akaike’s information criterion was used to select the best models explaining AGBP and for identifying the relative importance of each mechanism.Results: Vegetation quantity(tree density) and mass-ratio(relative biomass of Cryptocarya alba, a conservative species) were the strongest drivers increasing AGBP, while niche complementarity(richness species) and soil resources(sand, %) had a smaller effect either decreasing or increasing AGBP, respectively. This study provides the first assessment of alternative mechanisms driving AGBP in mediterranean forests of Chile. There is strong evidence suggesting that the vegetation quantity and mass-ratio mechanisms are key drivers of AGBP, such as in other tropical and temperate forests. However, in contrast with other studies from mediterranean-type forests, our results show a negative effect of species diversity and a small effect of soil resources on AGBP.Conclusion: AGBP in the Chilean matorral depends mainly on the vegetation quantity and mass-ratio mechanisms.The findings of this study have implications for matorral restoration and management for the production of timber and non-timber products and carbon sequestration.

Estimating above-ground biomass by fusion of LiDAR and multispectral data in subtropical woody plant communities in topographically complex terrain in North-eastern Australia

We investigated a strategy to improve predicting capacity of plot-scale above-ground biomass （AGB） by fusion of LiDAR and Land- sat5 TM derived biophysical variables for subtropical rainforest and eucalypts dominated forest in topographically complex landscapes in North-eastern Australia. Investigation was carried out in two study areas separately and in combination. From each plot of both study areas, LiDAR derived structural parameters of vegetation and reflectance of all Landsat bands, vegetation indices were employed. The regression analysis was carded out separately for LiDAR and Landsat derived variables indi- vidually and in combination. Strong relationships were found with LiDAR alone for eucalypts dominated forest and combined sites compared to the accuracy of AGB estimates by Landsat data. Fusing LiDAR with Landsat5 TM derived variables increased overall performance for the eucalypt forest and combined sites data by describing extra variation （3% for eucalypt forest and 2% combined sites） of field estimated plot-scale above-ground biomass. In contrast, separate LiDAR and imagery data, andfusion of LiDAR and Landsat data performed poorly across structurally complex closed canopy subtropical minforest. These findings reinforced that obtaining accurate estimates of above ground biomass using remotely sensed data is a function of the complexity of horizontal and vertical structural diversity of vegetation.

Estimating canopy closure density and above-ground tree biomass using partial least square methods in Chinese boreal forests

Boreal forests play an important role in global environment systems. Understanding boreal forest ecosystem structure and function requires accurate monitoring and estimating of forest canopy and biomass. We used partial least square regression （PLSR） models to relate forest parameters, i.e. canopy closure density and above ground tree biomass, to Landsat ETM＋ data. The established models were optimized according to the variable importance for projection （VIP） criterion and the bootstrap method, and their performance was compared using several statistical indices. All variables selected by the VIP criterion passed the bootstrap test （p〈0.05）. The simplified models without insignificant variables （VIP 〈1） performed as well as the full model but with less computation time. The relative root mean square error （RMSE%） was 29% for canopy closure density, and 58% for above ground tree biomass. We conclude that PLSR can be an effective method for estimating canopy closure density and above ground biomass.

Fixed-Bed Column Adsorption Modeling of MnO4- Ions from Acidic Aqueous Solutions on Activated Carbons Prepared with the Biomass

Activated carbons calcined at 400˚C and 600˚C (AC-400 and AC-600), prepared using palm nuts, collected in the town of Franceville in Gabon, were used to study the dynamic adsorption of MnO4- ions in acidic media on fixed bed column and on the kinetic modeling of experimental data of breakthrough curves of MnO4- ions obtained. Results on the adsorption of MnO4- ions in fixed-bed dynamics obtained on AC-400 and AC-600 adsorbents beds indicated that the AC-400 bed appears to be the most efficient in removing MnO4- ions in acidic media. Indeed, the adsorbed amounts, the adsorbed capacities at saturation and the elimination percentage of MnO4- ions obtained with AC-400 (31.24 mg;52.06 mg·g-1 and 41.65% respectively) were higher compared to those obtained with AC-600 (9.87 mg;16.45 mg·g-1 and 17.79% respectively). The breakthrough curves kinetic modeling revealed that the Thomas model and the pseudo-first-order kinetic model were the most suitable models to describe the adsorption of MnO4- ions on adsorbents studied in our experimental conditions. The results of the intraparticle diffusion model showed that intraparticle diffusion was involved in the adsorption mechanism of MnO4- ions on investigated adsorbents and was not the limiting step and the only process controlling MnO4- ions adsorption. In contrast to AC-400, the intraparticle diffusion on AC-600 bed plays an important role in the adsorption mechanism of MnO4- ions.

Model-based estimation of above-ground biomass in the miombo ecoregion of Zambia

Background：Information on above-ground biomass（AGB） is important for managing forest resource use at local levels,land management planning at regional levels,and carbon emissions reporting at national and international levels.In many tropical developing countries,this information may be unreliable or at a scale too coarse for use at local levels.There is a vital need to provide estimates of AGB with quantifiable uncertainty that can facilitate land use management and policy development improvements.Model-based methods provide an efficient framework to estimate AGB.Methods：Using National Forest Inventory（NFI） data for a^1,000,000 ha study area in the miombo ecoregion,Zambia,we estimated AGB using predicted canopy cover,environmental data,disturbance data,and Landsat 8 OLI satellite imagery.We assessed different combinations of these datasets using three models,a semiparametric generalized additive model（GAM） and two nonlinear models（sigmoidal and exponential）,employing a genetic algorithm for variable selection that minimized root mean square prediction error（RMSPE）,calculated through cross-validation.We compared model fit statistics to a null model as a baseline estimation method.Using bootstrap resampling methods,we calculated 95% confidence intervals for each model and compared results to a simple estimate of mean AGB from the NFI ground plot data.Results：Canopy cover,soil moisture,and vegetation indices were consistently selected as predictor variables.The sigmoidal model and the GAM performed similarly;for both models the RMSPE was -36.8 tonnes per hectare（i.e.,57% of the mean）.However,the sigmoidal model was approximately 30% more efficient than the GAM,assessed using bootstrapped variance estimates relative to a null model.After selecting the sigmoidal model,we estimated total AGB for the study area at 64,526,209 tonnes（＋/- 477,730）,with a confidence interval 20 times more precise than a simple designbased estimate.Conclusions：Our findings demonstrate that NFI data may be combined with freely available satellite imagery and soils data to estimate total AGB with quantifiable uncertainty,while also providing spatially explicit AGB maps useful for management,planning,and reporting purposes.

Assessment of Land Use Land Cover Change Drivers and Its Impacts on above Ground Biomass and Regenerations of Woody Plants: A Case Study at Dire Dawa Administration, Ethiopia

Understanding land use land cover (LULC) change drivers at local scale is vital for development of management strategies to tackle further decline of natural resources. In connection to this, a study was conducted in Dire Dawa administration, Ethiopia to investigate the drivers for change in land use land cover and its impact on above ground biomass and regenerations of woody plants. A total of 160 respondents were selected randomly to collect data on drivers of LULC change. A multistage stratified cluster sampling was used for above ground biomass assessment. Nine sample plots of 10 m × 10 m size in each cluster and a total of 36 sample plots in all clusters were randomly established. In all sample plots, woody plants having >5 cm diameter were measured for their diameter at breast height (DBH), and biomass estimated using allometric equation. The study revealed that, cutting of woody plants for fuel wood and making charcoal, population growth, expansion of cultivated land, drought, settlement areas and livestock ranching are the major six important drivers of LULC change. The study also revealed that, the mean above ground biomass of woody plants in Dire Dawa Administration was 4.94 ton/ha, with maximum and minimum above ground biomass of 6.27 ton/ha and 3.90 ton/ha, respectively. The number of regenerants of tree species was low and only 36% of the plots had tree regenerants. Thus, proper woodland management strategies implementation, land use planning, afforestation and reforestation activities are recommended to minimize unprecedented LULC change in the study area.

Above Ground Biomass Assessment from Combined Optical and SAR Remote Sensing Data in Surat Thani Province, Thailand

Today the carbon content in the atmosphere is predominantly increasing due to greenhouse gas emission and deforestation. Forest plays a key role in absorbing carbon dioxide from atmosphere by process of sequestration through photosynthesis and stores in form of wood biomass which contains nearly 70% - 80% of global carbon. Different forms of biomass in the environment include agricultural products, wood, renewable energy and solid waste. Therefore, it is essential to estimate the biomass content in the environment. In olden days, biomass is estimated by forest inventory techniques which consume lot of time and cost. The spatial distribution of biomass cannot be obtained by traditional inventory forest techniques so the application of remote sensing in biomass assessment is introduced to solve the problem. Overall accuracy of classified map indicates that land features of Surat Thani on map show an accuracy of 91.13% with different land features on ground. Both optical (LANDSAT-8) and synthetic aperture radar (ALOS-2) remote sensing data are used for above ground biomass (AGB) assessment. Biomass that stores in branch and stem of tree is called as above ground biomass. Twenty ground sample plots of 30 m × 30 m utilized for biomass calculation from allometric equations. Optical remote sensing calculates the biomass based on the spectral indices of Soil Adjusted Vegetation Index (SAVI) and Ratio Vegetation Index (RVI) by regression analysis (R2 = 0.813). Synthetic aperture radar (SAR) is an emerging technique that uses high frequency wavelengths for biomass estimation. HV backscattering of ALOS-2 shows good relation (R2 = 0.74) with field calculated biomass compared to HH (R2 = 0.43) utilizes for biomass model generation by linear regression analysis. Combination of both optical spectral indices (SAVI, RVI) and HV (ALOS-2) SAR backscattering increases the plantation biomass accuracy to (R2 = 0.859) compared to optical (R2 = 0.788) and SAR (R2 = 0.742).

Estimation of Above Ground Biomass in Forests Using Alos Palsar Data in Kericho and Aberdare Ranges

Above Ground Biomass is one of the six pools identified in the inventory of forest resources and estimation of greenhouse gas emissions and sinks from the forestry sector. The pool varies by management practices in different agro-ecological or agro-climatic zones in forests. The quantification of above ground biomass (AGB) hence carbon sequestration in forests has been very difficult due to the immense costs required. This research was done to estimate AGB using ALOS PALSAR L band data (HH, HV polarisation) acquired in 2009 in relation with ground measurements data in Kericho and Aberdares ranges in Kenya. Tree data information was obtained from ground measurement of DBH and tree heights in 100 circular plots of 15 m radius, by use of random sampling technique. ALOS PALSAR image is advantageous for its active microwave sensor using L-band frequency to achieve cloud free imageries, and the ability of long wavelength cross-polarization to estimate AGB accurately for tropical forests. The variations result between Natural and plantation forest for measured and estimated biomass in Kericho HV band regression value was 0.880 and HH band was 0.520. In Aberdare ranges HV regression value of 0.708 and HH band regression value of 0.511 for measured and estimated biomass respectively. The variations can be explained by the influence of different management regimes induced human disturbances, forest stand age, density, species composition, and trees diameter distribution. However, further research is required to investigate how strong these factors affect relationship between AGB and Alos Palsar backscatters.

川西高山-亚高山草地群落特征及稳定性对增温的响应

川西高山-亚高山区域作为中国长江、黄河上游的重要生态屏障,拥有丰富的生物多样性。川西高山-亚高山草地生态系统对增温变化敏感,但对其草地生态系统群落及其稳定性对增温的响应研究仍存在不足。由于指标选取单一、研究尺度小等研究方法问题,已有研究结论的代表性不足。为了深入了解这一生态系统在不同海拔高度上对气候变暖的响应特征,本研究在贡嘎山东北坡雅家埂峡谷地带,沿海拔3000~4130 m设置4个高山亚高山草地样地,采用两种常用的模拟增温模式(OTC增温和带草皮下移增温)模拟环境温度升高,研究2012—2017年期间,不同海拔梯度上草地物种丰富度、群落高度和盖度、地上净初级生产力对增温的响应特征,以及这些特征变化与群落生物量时间稳定性的关系。结果表明:(1)OTC增温仅在高海拔寒冷样地降低了物种丰富度,不影响中低海拔样地的物种丰富度;下移增温增加了高海拔寒冷样地的物种丰富度,却降低了低海拔温暖样地的物种丰富度;(2)OTC增温仅增加了高海拔寒冷样地的群落高度,下移增温显著增加了所有海拔梯度上的植被高度;(3)在两个中间海拔梯度样地,两种增温模式都降低了杂草类植物盖度,却增加了莎草与禾草的盖度;(4)仅有下移增温显著增加了高海拔寒冷样地的地上净初级生产力,各功能群地上净初级生产力的变化存在海拔差异;(5)位于灌草交错带3850 m样地的群落生物量时间稳定性最高,而位于4130 m的高海拔寒冷样地的群落生物量时间稳定性最弱。研究表明,高海拔寒冷生境的草地群落组成结构和地上净初级生产力易受到气候变暖的影响,趋于不稳定。本研究可为川西高山草地生态系统应对未来气候变暖的生态保护管理与政策制定提供科学依据。

GF-1和Landsat 8 OLI遥感影像在草地资源调查中的应用——以甘南州天然草地为例

以甘南州天然草地为研究对象,利用高空间分辨率遥感影像(GF-1)数据,提取了甘南州天然草地空间分布数据;结合地形数据和行政区划单元数据分析了不同类型天然草地的空间分布特征;利用Landsat 8 OLI数据和草地盖度、草地地上生物量实测数据构建了甘南州草地盖度、草地地上生物量遥感反演模型,并分析了2019年甘南州草地的生长状况。结果表明:①全州天然草地面积为1995740.04 hm^(2),草地地上生物量为2250.28 kg/hm^(2),草地盖度为93.11%。②高寒草甸和山地草甸分别占63.69%和28.31%,二者分别集中分布于海拔3500~5000 m,和3000~3500 m。研究结果为甘南州草地调查、日常监管提供了数据与理论支持。

基于SPOT NDVI的2010-2018年青海省草地资源动态监测研究

【目的】动态监测草地资源对草地资源的合理利用及生态建设具有重要意义,可实现对青海省草地资源的动态监测,为草原管理提供决策依据。【方法】选择植被覆盖度和地上生物量作为研究指标,利用2010-2018年青海省8月NDVI、年降水量、年均气温和2015-2017年实测地上生物量数据,反演植被覆盖度(FVC)时空变化趋势,分析植被覆盖度与气候因子的相关性,获得地上生物量反演模型并分析其时空变化趋势。【结果】1)近9 a青海省平均FVC呈极显著下降趋势(P<0.05),平均地上生物量呈增长趋势;2)近9 a青海省16.90%区域的FVC呈极显著降低(P<0.01),15.42%区域显著降低(P<0.05);极显著好转(P<0.01)面积占0.94%,显著好转(P<0.05)面积占1.43%;3)年降水量和年均气温在近9 a均呈起伏上升趋势,年际间呈负协同现象;4)FVC与年降水量、年均气温呈弱显著相关区域占20%左右(P<0.1);5)指数模型y=496.21e2.4497x为青海省地上生物量反演的最优模型(R2=0.42,P<0.01)。6)地上生物量减少趋势最大为276.71 kg/hm^(2),增加趋势最大为544.68 kg/hm^(2),占比为69.18%、30.82%。【结论】青海省草地资源在逐步恢复,且其变化与人类活动有重要关系。

Biomass Prediction Equation for Colophospermum mopane (Mopane) in Botswana

This study aimed to develop a biomass equation for estimating the total above-ground biomass for Colophospermum mopane (mopane) based on the pooled data from three study sites. The mopane woodlands in Botswana represent 14.6% of Botswana’s total area. The woodlands directly or indirectly support the livelihood of the majority of the rural population by providing wood and non-wood products. However, there is limited information on the pattern, trends and distribution of woody biomass production and their primary, environmental, and climatic determinants in different parts of Botswana. All the data were collected by destructive sampling from three study sites in Botswana. Stratified random sampling was based on the stem diameter at breast height (1.3 m from the ground or Diameter at Breast Height (DBH)). A total of 30 sample trees at each study site were measured, felled and weighed. The data from the three sites were pooled together, and the study employed regression analysis to examine the nature of relationships between total above-ground biomass (dependent variable) and five independent variables: 1) total tree height;2) crown diameter;3) stem diameters at 0.15 m;1.3 m (DBH) and 3 m from the ground respectively. There were significant relationships between all the independent variables and the dependent variable. However, DBH emerged as the strongest predictor of total tree above-ground biomass for mopane. The equation lnBiomass=-1.163+2.190lnDBH was adopted for use in the indirect estimation of total tree above-ground biomass for mopane in Botswana.

联合运用多光谱和激光雷达技术构建的林分生物量估算模型

以广东省广州市从化石门国家森林公园为研究区域,选择4种不同林分类型(针阔混交林、阔叶林、针叶林、竹林),各林分类型选择3个20 m×20 m地块作为样方;结合激光雷达、多光谱图像、实测数据,构建多元非线性反演模型和多元线性回归模型,估算森林地上生物量,并选择最佳模型进行精度评价。结果表明:(1)依据多源数据建立的4种不同林分类型的多元非线性地上生物量反演模型的精度最高,针阔混交林样地地上生物量预测值为42.79 t·hm^(-2)、阔叶林样地地上生物量预测值为60.46 t·hm^(-2)、针叶林样地地上生物量预测值为32.99 t·hm^(-2)、竹林样地地上生物量预测值为1.92 t·hm^(-2)。(2)研究区中4种不同林分类型的多元非线性地上生物量反演模型的拟合精度,由大到小依次为竹林(决定系数为0.919)、阔叶林(决定系数为0.813)、针叶林(决定系数为0.786)、针阔混交林(决定系数为0.713),均符合精度要求。采用多光谱和激光雷达数据结合的方式,能够较精准地提取林分地上生物量信息,可准确估算针阔混交林、阔叶林、针叶林、竹林的地上生物量。

雅鲁藏布江上游高寒草甸地上生物量与多样性沿海拔梯度的变化特征

【目的】雅鲁藏布江是青藏高原最重要的河流,研究雅鲁藏布江地上生物量和物种多样性对了解该区域的草地资源和生态保护具有重要意义。【方法】根据海拔梯度对雅鲁藏布江上游高寒草甸植被特征进行调查,探讨不同海拔梯度下地上生物量和物种多样性的分布差异、地上生物量与物种多样性之间的关系,以及相关环境因子对地上生物量和物种多样性的影响。【结果】(1)雅鲁藏布江上游高寒草甸地上生物量与海拔、气温和降水均无显著相关关系(P>0.05);(2)多物种样性指数与海拔呈显著负相关关系(P<0.05),Shannon-Weiner指数(H)、Patrick指数(R)随海拔升高表现为下降趋势,而物种多样性与气温和降水呈显著正相关关系(P<0.05);(3)地上生物量与物种多样性之间表现为显著负相关关系,Shannon-Weiner指数(H)对地上生物量的解释达到70%(P<0.01)。【结论】雅鲁藏布江上游高寒草甸的海拔与地上生物量无显著相关性,而与物种多样性呈显著负相关关系,这为雅鲁藏布江上游流域草地资源合理利用和物种多样性保护提供依据。

基于PSO-LSSVM的森林地上生物量估测模型

为提高森林地上生物量估测精度,从建模因子和建模方法出发,提出了一种综合考虑影像纹理特征、地形特征、光谱特征的粒子群优化最小二乘支持向量机生物量估测方法。以松山自然保护区为研究区域,以资源三号遥感卫星数据为数据源,配合194块调查样地实测数据、森林资源二类调查数据、数字高程模型数据,通过分析46个特征变量与森林地上生物量间的Pearson相关性,进行特征变量优化提取,建立PSO-LSSVM模型并在Matlab 2014a上编程实现。以决定系数R2和均方根误差RMSE为指标,对比分析了PSO-LSSVM和多元线性回归地上生物量模型精度。研究结果表明:PSO-LSSVM模型在针叶林、阔叶林、灌木林3种类型中预测决定系数分别为0.867、0.853、0.842,比多元线性回归模型分别提高了23.15%、19.13%、14.40%。PSO-LSSVM地上生物量模型具有良好的自学能力和自适应能力,它取代了传统的遍历优化方法,在全局优化及收敛速度方面具有较大优势,预测精度较高。

基于机载P-波段全极化SAR数据的复杂地形森林地上生物量估测方法

【目的】利用国产合成孔径雷达(SAR)系统(CASMSAR)获取的机载P-波段全极化SAR(PolSAR)数据,分析SAR对森林地上生物量(AGB)的响应与地形的关系,建立融合地形因子的高精度多项式模型,以提高森林AGB的估测精度。【方法】首先以基于机载激光雷达(Li DAR)数据得到的研究区坡度分布图与结合实测样地AGB数据得到的森林AGB分布图作为参考数据进行系统抽样,分析森林AGB与P-波段PolSAR后向散射强度的关系以及不同坡度下二者的相关性变化;然后利用Li DAR得到的高精度数字高程模型(DEM)结合机载P-波段的轨道数据计算当地入射角,进而建立以后向散射强度、当地入射角以及雷达视角为输入特征的多项式统计模型,同时将以上系统抽样得到的样本一部分作为模型训练样本,一部分作为精度检验样本。为避免样本尺度引起的偶然性,检验了20 m×20 m至100 m×100 m不同样地尺度下的估测精度。【结果】以90 m×90 m样本为例,当坡度为0°~5°时,引入当地入射角(第2组特征)的估测精度与未引入当地入射角(第1组特征)的估测精度分别为:决定系数(R^2)为0.634和0.634,均方根误差(RMSE)为12.07和12.08 t·hm^(-2),总精度(Acc.)为78.91%和78.89%;当坡度为5°~10°时,第2组特征与第1组特征的估测精度分别为:R2为0.524和0.523,RMSE为13.52和13.97 t·hm^(-2),Acc.为80.57%和80.52%;当坡度大于10°时,第2组特征与第1组特征的估测精度分别为:R^2为0.628和0.519,RMSE为13.16和15.70 t·hm^(-2),Acc.为81.05%和78.55%。随着样地尺度增大,2组特征的估测精度均增大,且第2组特征的估测精度大于第1组。【结论】当坡度小于10°时,地形对森林的后向散射强度几乎无影响;当坡度大于10°时,地形的影响显著,在不同尺度下,引入当地入射角的估测模型均可以有效提高估测精度,充分说明模型的有效性和稳定性。此外,随着尺度增大,无论采用的模型是否考虑了地形影响,其估测精度都逐渐提高并趋于稳定,揭示出对复杂地形下森林AGB估测模型效果的评价必须考虑尺度的影响,且参考样地要足够大,否则难以得到客观的结论。

高寒草甸阳坡-阴坡梯度上环境因子特征及其与地上生物量和物种丰富度的关系

以甘南高寒草甸为对象,研究了坡向梯度上环境因子的变化特点及其与地上生物量和物种丰富度的关系.结果表明:0～20 cm与20～40 cm的土壤含水量均是自阳坡到阴坡呈递增的变化趋势,且20～40 cm土壤的含水量高于0～20 cm的;两层土壤的全磷质量分数和w(C):w(N)与含水量的变化相同,土壤全氮、有机质、速效磷及速效氮质量分数的变化均呈先减小后增大的趋势,偏阳坡最小.土壤养分质量分数均是0～20 cm的大于20～40 cm的,表现出表层土壤养分富集的现象,土壤pH值沿阳坡到阴坡呈递减趋势.地上生物量和物种丰富度的变化特点均为阴坡>偏阴坡>偏阳坡>阳坡,且地上生物量与物种丰富度显著正相关.同归分析表明:土壤含水量与土壤全磷,有机质质量分数是影响地上生物量和物种丰富度的主要因子,其中土壤含水量是关键限制性因子.