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).

Effects of soil nutrients and climate factors on belowground biomass in an alpine meadow in the source region of the Yangtze-Yellow rivers, Tibetan Plateau of China

Improving our knowledge of the effects of environmental factors （e.g. soil conditions, precipitation and temperature） on belowground biomass in an alpine grassland is essential for understanding the consequences of carbon storage in this biome. The object of this study is to investigate the relative importance of soil nutrients and climate factors on belowground biomass in an alpine meadow in the source region of the Yangtze and Yellow rivers, Tibetan Plateau. Soil organic carbon （SOC）, total nitrogen （TN） and total phosphorous （TP） contents and belowground biomass were measured at 22 sampling sites across an alpine meadow on the Tibetan Plateau. We analyzed the data by using the redundancy analysis to determine the main environmental factors affecting the belowground biomass and the contribution of each factor. The results showed that SOC, TN and TP were the main factors that influenced belowground biomass, and the contribution of SOC, TN and TP on biomass was in the range of 47.87%-72.06% at soil depths of 0-30 cm. Moreover, the combined contribution of annual mean temperature （AMT） and mean annual precipitation （MAP） on belowground biomass ranged from 0.92% to 4.10%. A potential mechanism for the differences in belowground biomass was caused by the variations in soil nitrogen and phosphorous, which were coupled with SOC. A significant correlation was observed between MAP and soil nutrients （SOC, TN and TP） at the soil depth of 0-10 cm （P〈0.05）. We concluded that precipitation is an important driving force in regulating ecosystem functioning as reflected in variations of soil nutrients （SOC, TN and TP） and dynamics of belowground biomass in alpine grassland ecosystems.

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.

Changes in the relationship between species richness and belowground biomass among grassland types and along environmental gradients in Xinjiang, Northwest China

The association between biodiversity and belowground biomass(BGB) remains a central debate in ecology.In this study, we compared the variations in species richness(SR) and BGB as well as their interaction in the top(0–20 cm), middle(20–50 cm) and deep(50–100 cm) soil depths among 8 grassland types(lowland meadow, temperate desert, temperate desert steppe, temperate steppe desert, temperate steppe, temperate meadow steppe, mountain meadow and alpine steppe) and along environmental gradients(elevation, energy condition(annual mean temperature(AMT) and potential evapotranspiration(PET)), and mean annual precipitation(MAP)) based on a 2011–2013 survey of 379 sites in Xinjiang, Northwest China.The SR and BGB varied among the grassland types.The alpine steppe had a medium level of SR but the highest BGB in the top soil depth, whereas the lowland meadow had the lowest SR but the highest BGB in the middle and deep soil depths.The SR and BGB in the different soil depths were tightly associated with elevation, MAP and energy condition;however, the particular forms of trends in SR and BGB depended on environmental factors and soil depths.The relationship between SR and BGB was unimodal in the top soil depth, but SR was positively related with BGB in the middle soil depth.Although elevation, MAP, energy condition and SR had significant effects on BGB, the variations in BGB in the top soil depth were mostly determined by elevation, and those in the middle and deep soil depths were mainly affected by energy condition.These findings highlight the importance of environmental factors in the regulations of SR and BGB as well as their interaction in the grasslands in Xinjiang.

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.

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.

Above-and belowground biomass in relation to envi- ronmental factors in temperate grasslands, Inner Mongolia

Above- and belowground biomasses of grasslands are important parameters for characterizing re- gional and global carbon cycles in grassland ecosystems. Compared with the relatively detailed in- formation for aboveground biomass (AGB), belowground biomass (BGB) is poorly reported at the re- gional scales. The present study, based on a total of 113 sampling sites in temperate grassland of the Inner Mongolia, investigated regional distribution patterns of AGB, BGB, vertical distribution of roots, and their relationships with environmental factors. AGB and BGB increased from the southwest to the northeast of the study region. The largest biomass occurred in meadow steppe, with mean AGB and BGB of 196.7 and 1385.2 g/m2, respectively; while the lowest biomass occurred in desert steppe, with an AGB of 56.6 g/m2 and a BGB of 301.0 g/m2. In addition, about 47% of root biomass was distributed in the top 10 cm soil. Further statistical analysis indicated that precipitation was the primary determinant factor in shaping these distribution patterns. Vertical distribution of roots was significantly affected by precipitation, while the effects of soil texture and grassland types were weak.

Patterns of above-and belowground biomass allocation in China's grasslands:Evidence from individual-level observations

Above-and belowground biomass allocation not only influences growth of individual plants,but also influences vegetation structures and functions,and consequently impacts soil carbon input as well as terrestrial ecosystem carbon cycling.However,due to sampling difficulties,a considerable amount of uncertainty remains about the root:shoot ratio(R/S),a key parameter for models of terrestrial ecosystem carbon cycling.We investigated biomass allocation patterns across a broad spatial scale.We collected data on individual plant biomass and systematically sampled along a transect across the temperate grasslands in Inner Mongolia as well as in the alpine grasslands on the Tibetan Plateau.Our results indicated that the median of R/S for herbaceous species was 0.78 in China's grasslands as a whole.R/S was significantly higher in temperate grasslands than in alpine grasslands(0.84 vs.0.65).The slope of the allometric relationship between above-and belowground biomass was steeper for temperate grasslands than for alpine.Our results did not support the hypothesis that aboveground biomass scales isometrically with belowground biomass.The R/S in China's grasslands was not significantly correlated with mean annual temperature(MAT) or mean annual precipitation(MAP).Moreover,comparisons of our results with previous findings indicated a large difference between R/S data from individual plants and communities.This might be mainly caused by the underestimation of R/S at the individual level as a result of an inevitable loss of fine roots and the overestimation of R/S in community-level surveys due to grazing and difficulties in identifying dead roots.Our findings suggest that root biomass in grasslands tended to have been overestimated in previous reports of R/S.

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.

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.

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.

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。

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.

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.

Improved strategy for estimating stem volume and forest biomass using moderate resolution remote sensing data and GIS

This study presents the utility of remote sensing （RS）, GIS and field observation data to estimate above ground biomass （AGB） and stem volume over tropical forest environment. Application of those data for the modeling of forest properties is site specific and highly uncertain, thus further study is encouraged. In this study we used 1460 sampling plots collected in 16 transects measuring tree diameter （DBH） and other forest properties which were useful for the biomass assessment. The study was carded out in tropical forest region in East Kalimantan, Indo- nesia. The AGB density was estimated applying an existing DBH - biomass equation. The estimate was superimposed over the modified GIS map of the study area, and the biomass density of each land cover was calculated. The RS approach was performed using a subset of sample data to develop the AGB and stem volume linear equation models. Pearson correlation statistics test was conducted using ETM bands reflectance, vegetation indices, image transform layers, Principal Component Analysis （PCA） bands, Tasseled Cap （TC）, Grey Level Co-Occurrence Matrix （GLCM） texture features and DEM data as the predictors. Two linear models were generated from the significant RS data. To analyze total biomass and stem volume of each land cover, Landsat ETM images from 2000 and 2003 were preprocessed, classified using maximum likelihood method, and filtered with the majority analysis. We found 158±16 m^3.ha^-1 of stem volume and 168±15 t.ha^-1 of AGB estimated from RS approach, whereas the field measurement and GIS estimated 157±92 m^3.ha^-1 and 167±94 t.ha^-1 of stem volume and AGB, respectively. The dynamics of biomass abundance from 2000 to 2003 were assessed from multi temporal ETM data and we found a slightly declining trend of total biomass over these periods. Remote sensing approach estimated lower biomass abundance than did the GIS and field measurement data. The earlier approach predicted 10.5 Gt and 10.3 Gt of total biomasses in 2000 and 2003, while the later estimated 11.9 Gt and 11.6 Gt of total biomasses, respectively. We found that GLCM mean texture features showed markedly strong correlations with stem volume and biomass.

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.

Biomass estimation of Shorea robusta with principal component analysis of satellite data

Spatio-temporal assessment of the above ground biomass （AGB） is a cumbersome task due to the difficulties associated with the measurement of different tree parameters such as girth at breast height and height of trees. The present research was conducted in the campus of Birla Institute of Technology, Mesra, Ranchi, India, which is predomi- nantly covered by Sal （Shorea robusta C. F. Gaertn）. Two methods of regression analysis was employed to determine the potential of remote sensing parameters with the AGB measured in the field such as linear regression analysis between the AGB and the individual bands, principal components （PCs） of the bands, vegetation indices （VI）, and the PCs of the VIs respectively and multiple linear regression （MLR） analysis be- tween the AGB and all the variables in each category of data. From the linear regression analysis, it was found that only the NDVI exhibited regression coefficient value above 0.80 with the remaining parameters showing very low values. On the other hand, the MLR based analysis revealed significantly improved results as evidenced by the occurrence of very high correlation coefficient values of greater than 0.90 determined between the computed AGB from the MLR equations and field-estimated AGB thereby ascertaining their superiority in providing reliable estimates of AGB. The highest correlation coefficient of 0.99 is found with the MLR involving PCs of VIs.

Distribution characteristics of zooplankton biomass in the East China Sea

On the basis of the data of oceanographic survey in the East China Sea in four seasons during 1997-2000 (23°30'~33°00'N, 118°30'-128°E), the variation of total biomass and diet biomass of zooplankton and their spatial-temporal distribution and relationship with the fishing ground of Engraulis japonicus are approached and analyzed. The results show that the average biomass is 65.32 mg/m3 in four seasons, autumn (86.18 mg/m3) being greater than summer (69.18 mg/m3) greater than spring (55.67 mg/m3) greater than winter (50.33 mg/m3). The average value of diet zooplankton biomass is 40.9 mg/m3. The trends of horizontal distribution both in the total biomass and the diet biomass of zooplankton are similar. The high biomass region (250-500 mg/m3) is very limited, only accounting for 1% of the investigation area. Seasonal variation of the biomass is very remarkable in the west and north parts of East China Sea coastal waters ( 29°30'N,125°E). The horizontal distribution of diet zooplankton depends on the abundance distribution of crustacean. The distribution of diet zooplankton is related to the fishing ground of Engraulis japonicus and the high-density area of young fish and larval. In spring, the central fishing ground of Engraulis japonicus (>100 kg/h) and the high-density area of young fish and larval (>100 individuals per net) are located at the same place of high-density (100-250 mg/m3)area of diet zooplankton in the middle-southern part of East China Sea or the edge of its waters.