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.

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.

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).

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.

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.

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.

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.

A scalable cyberinfrastructure and cloud computing platform for forest aboveground biomass estimation based on the Google Earth Engine

Earth observation(EO)data,such as high-resolution satellite imagery or LiDAR,has become one primary source for forests Aboveground Biomass(AGB)mapping and estimation.However,managing and analyzing the large amount of globally or locally available EO data remains a great challenge.The Google Earth Engine(GEE),which leverages cloud-computing services to provide powerful capabilities on the management and rapid analysis of various types of EO data,has appeared as an inestimable tool to address this challenge.In this paper,we present a scalable cyberinfrastructure for on-the-fly AGB estimation,statistics,and visualization over a large spatial extent.This cyberinfrastructure integrates state-of-the-art cloud computing applications,including GEE,Fusion Tables,and the Google Cloud Platform(GCP),to establish a scalable,highly extendable,and highperformance analysis environment.Two experiments were designed to demonstrate its superiority in performance over the traditional desktop environment and its scalability in processing complex workflows.In addition,a web portal was developed to integrate the cyberinfrastructure with some visualization tools(e.g.Google Maps,Highcharts)to provide a Graphical User Interfaces(GUI)and online visualization for both general public and geospatial researchers.

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.

Remote sensing based shrub above-ground biomass and carbon storage mapping in Mu Us desert,China

The estimation of above-ground biomass(AGB) and carbon storage is very important for arid and semi-arid ecosystems.HJ-1A/B satellite data combined with field measurement data was used for the estimation of shrub AGB and carbon storage in the Mu Us desert,China.The correlations of shrub AGB and spectral reflectance of four bands as well as their combined vegetation indexes were respectively analyzed and stepwise regression analysis was employed to establish AGB prediction equation.The prediction equation based on ratio vegetation index(RVI)was proved to be more suitable for shrub AGB estimation in the Mu Us desert than others.Shrub AGB and carbon storage were mapped using the RVI based prediction model in final.The statistics showed the western Mu Us desert has relatively high AGB and carbon storage,and that the gross shrub carton storage in Mu Us desert reaches 16 799 200 t,which has greatly contributed to the carbon fixation in northern China.

Forest above ground biomass estimation methodology based on polarization coherence tomography

基于微波的后向散射系数估计森林地上生物量(AGB)易受后向散射系数饱和的影响,而利用森林高度,根据生长方程估计AGB,却没有考虑和AGB密切相关的林分密度、树种组成、林层垂直分布等空间结构特征的作用,针对这些问题,提出一种基于极化相干层析(Polarization Coherence Tomography,PCT)技术的AGB估计方法。基于德国宇航局(DLR)机载SAR系统(ESAR)获取的特劳斯坦(Traunstein)试验区L-波段极化干涉SAR(PolInSAR)数据,通过对具有不同AGB水平的典型林分的相对反射率函数曲线的分析,定义了9个与AGB具有相关性的特征参数。然后基于20个林分的实测AGB数据,以林分尺度上这9个特征参数的平均值为自变量,以实测林分平均AGB为因变量,采用逐步回归分析法构建了AGB估测模型,并对该模型进行评价,对影响模型估计精度的因素进行分析,结果表明,由PCT提取的相对反射率函数特征参数对AGB很敏感,充分利用相对反射率函数信息可提高AGB估计精度。

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.

Comparative Assessment of the Relationship of Satellite Data with the Above Ground Biomass of Sal Trees(Shorea robusta) Determined from Phenologically Different Time Periods

The Above Ground Biomass(AGB) estimates of vegetation comprise both the bole biomass determined through a volumetric equation and litter biomass collected from the ground.For mature trees,the AGB estimated in phenologically different time periods is directly affected by the litter biomass since the Diameter at Breast Height(DBH) and height(H) of such trees that are used in the estimation of bole biomass would remain unchanged over a reasonable time period.In the present study,we have determined the AGB of Sal trees(Shorea robusta) in two contrasting seasons:the peak green period in October being devoid of lit-ter on the ground and the leaf shedding period in February with abundant amount of litter present on the ground.Estimation of AGB for the month of February included the litter biomass.In contrast,the AGB for October represented only the bole biomass.AGB was estimated for ten different plots selected in the study area.The AGB estimated from ten sampling plots for each time period was re-gressed with the individual tree parameters such as the average DBH and height of trees measured from the corresponding plots.The regression analysis exhibited a significantly stronger relationship between the AGB and DBH for the month of October as compared to February.Furthermore,the correlation between the remotely sensed derived data and AGB was also found to be significantly higher for the month of October than February.This observation indicates that inclusion of the litter biomass in AGB will tend to decrease the re-gression relationship between AGB and DBH and also between the remotely sensed data and AGB.Therefore,these conclusions invite careful consideration while estimating AGB from satellite data in phenologically different time periods.

Biomass Components and Environmental Controls in Ningxia Grasslands

Grassland plays an important role in the global carbon cycle and climate regulation. However, there are still large uncertainties in grassland carbon pool and also its role in global carbon cycle due to the lack of measured grassland biomass at regional scale or global scale with a unified survey method, particular for below-ground biomass. The present study, based on a total of 44 grassland sampling plots with 220 quadrats across Ningxia, investigated the characteristics of above-ground biomass （AGB）, below-ground biomass （BGB）, litter biomass （LB）, total biomass （TB） and root：shoot ratios （R：S） for six predominantly grassland types, and their relationships with climatic factors. AGB, BGB, LB and TB varied markedly across different grassland types, the median value ranging from 28.2-692.6 g m-2 for AGB, 130.4-2 036.6 g m-： for BGB, 9.2-82.3 g m2 for LB, and 168.0-2 681.3 g m-： for TB. R：S showed less variation with median values from 3.2 to 5.3 （excluding marshy meadow）. The different grassland types showed similar patterns of biomass allocation, with more than 70% BGB for all types. There is evidence of strong positive effects associated with mean annual precipitation （MAP） and negative effects associated with mean annual temperature （MAT） on AGB, BGB, and LB, although both factors have the opposite effect on R：S.

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。

大气CO_(2)浓度升高背景下冬小麦冠层光谱特征和地上生物量估算

本研究旨在探究大气CO_(2)浓度升高对冬小麦全生育时期冠层光谱特征的影响,并基于筛选的敏感波段建立地上生物量(AGB)与光谱参数的定量关系。为此,在2021—2022年的冬小麦生长季,利用开放式CO_(2)富集系统(Mini-FACE),设定大气CO_(2)浓度(ACO_(2),(420±20)μL L^(–1))和高CO_(2)浓度(ECO_(2),(550±20)μL L^(–1))两个处理水平,分析了高CO_(2)浓度下光谱特征变化,基于连续投影算法(SPA)、逐步多元线性回归(SMLR)和偏最小二乘法回归(PLSR)筛选AGB敏感波段并构建估算模型。结果表明:CO_(2)浓度升高使冬小麦拔节期和开花期AGB显著增加。红边和近红边反射率及红边面积在拔节期增加,在开花期和灌浆期降低,蓝边、黄边和红边位置在不同生育时期均发生移动;AGB的敏感光谱波段主要分布在红边和近红边区域,CO_(2)浓度升高缩小了AGB敏感波段范围,但不影响AGB的估算;AGB的SMLR和PLSR模型均取得了较高的估算精度(R^(2)>0.8),其中SMLR模型中的R_(799′)、D_(y)、SD_(y)和PRI等特征参数与AGB显著相关,R^(2)为0.866。PLSR模型(R^(2)>0.9)在估算精度和稳定性上优于SMLR模型。本研究可为未来高CO_(2)浓度下冬小麦生长发育的遥感监测提供理论基础和技术方法。

不同基质处理对蒜头果幼苗生长的影响

为提高蒜头果(Malaniaoleifera)幼苗品质和造林成活率,以泥炭土、轻基质、黄心土、松针、蛭石和珍珠岩为原料,设计9个不同基质处理,测定蒜头果幼苗生长和生物量指标,并分析其与基质理化性质的相关性,结合主成分分析对不同基质处理进行综合评价。结果表明,在不同基质处理下,蒜头果幼苗生长指标均存在差异。T5处理的苗高和地径总生长量均最高,分别为69.39 cm和8.31 mm。T1处理的地上部分鲜质量总重、全株鲜质量、地上部分干质量总重和全株干质量均最高,分别为36.13、69.97、15.57和25.94 g。T8处理的地下部分鲜质量和地下部分干质量均最高,分别为37.03和11.13 g。苗高与土壤密度呈显著负相关,与速效磷含量呈显著正相关,与速效氮和速效钾含量均呈极显著正相关。地径与土壤密度呈极显著负相关,与田间持水量和总孔隙度均呈显著正相关,与速效氮和速效钾含量均呈极显著正相关。幼苗地上部分鲜质量、全株鲜质量和地上部分干质量均与总孔隙度呈显著正相关。全株鲜质量与速效氮含量呈显著正相关。T1处理的叶生物量和全株生物量均最高,综合得分最高;T5处理的苗高和地径均表现最好,叶生物量低于T1处理;T8处理的根系生长最好,最有利于幼苗地下部分生长;这3个基质配方均适用于蒜头果幼苗培育。

基于局部回归模型的森林生物量动态变化分析

基于丰林县地区4期Landsat影像和对应气象站点数据,结合该地区248块固定样地数据,利用全局回归模型(多元线性模型)和2种局部回归模型(地理加权回归模型、时空地理加权回归模型)建立研究区乔木地上生物量和遥感因子之间的关系,选出最优模型来研究丰林县乔木地上生物量时空变化。结果表明:根据3种模型的模拟结果数据与实测值的分析对比可以发现,局部回归模型的拟合效果要优于全局模型,加入时间特征的时空地理加权回归模型的拟合效果最好,模型评价指标与地理加权回归模型相比更为理想。统计得到研究区4个时期内总的乔木地上生物量分别为1.63×10^(7)、2.05×10^(7)、2.32×10^(7)、3.37×10^(7)t,4个时期的平均乔木地上生物量分别为54.82、68.98、77.87、113.46 t/hm2,乔木地上生物量呈现出逐期增加的趋势。利用遥感因子估测丰林地区的地上生物量,为未来该地区生物量分布的估计提供了依据。

Using nonparametric modeling approaches and remote sensing imagery to estimate ecological welfare forest biomass

The spatial distribution of forest biomass is closely related with carbon cycle, climate change, forest productivity, and biodiversity. Efficient quantification of biomass provides important information about forest quality and health. With the rising awareness of sustainable development, the ecological benefits of forest biomass attract more attention compared to traditional wood supply function. In this study, two nonparametric modeling approaches, random forest（RF） and support vector machine were adopted to estimate above ground biomass（AGB） using widely used Landsat imagery in the region,especially within the ecological forest of Fuyang District in Zhejiang Province, China. Correlation analysis was accomplished and model parameters were optimized during the modeling process. As a result, the best performance modeling method RF was implemented to produce an AGB estimation map. The predicted map of AGB in the study area showed obvious spatial variability and demonstrated that within the current ecological forest zone, as well as the protected areas, the average of AGB were higher than the ordinary forest. The quantification of AGB was proven to have a close relationship with the local forest policy and management pattern, which indicated that combining remote-sensing imagery and forest biophysical property would provide considerable guidance for making beneficial decisions.