Estimate Vegetation Biomass Research Articles

Airborne laser scanning for terrain modeling in the Amazon forest

ABSTRACT Very few studies have been devoted to understanding the digital terrain model (DTM) creation for Amazon forests. DTM has a special and important role when airborne laser scanning is used to estimate vegetation biomass. We examined the influence of pulse density, spatial resolution, filter algorithms, vegetation density and slope on the DTM quality. Three Amazonian forested areas were surveyed with airborne laser scanning, and each original point cloud was reduced targeting to 20, 15, 10, 8, 6, 4, 2, 1, 0.75, 0.5 and 0.25 pulses per square meter based on a random resampling process. The DTM from resampled clouds was compared with the reference DTM produced from the original LiDAR data by calculating the deviation pixel by pixel and summarizing it through the root mean square error (RMSE). The DTM from resampled clouds were also evaluated considering the level of agreement with the reference DTM. Our study showed a clear trade-off between the return density and the horizontal resolution. Higher forest canopy density demanded higher return density or lower DTM resolution.

Unmixing Polarimetric Radar Images Based on Land Cover Type Identified by Higher Resolution Optical Data Before Target Decomposition: Application to Forest and Bare Soil

Extracting information from a polarimetric radar representation usually consists in decomposing it with target decomposition algorithms. This first step can be seen as a geometric analysis of the polarimetric information: the identification of physical radar scattering mechanisms. The problem is that average physical parameters are estimated. As a consequence, these parameters might not describe correctly any of the land cover types that can be mixed together into the radar resolution cell. Therefore, using the polarimetric parameters for land cover classification is challenging. The novelty of the method is to propose a thematic analysis of the polarimetric information preceding the geometric one. The objective is to assess if splitting off polarimetric information on a land cover type basis before applying usual target decomposition algorithms can produce more consistent radar scattering mechanisms when land cover classes are mixed inside the radar resolution cell. A cooperative fusion framework in which very high-resolution optical images are used to unmix physical radar scattering mechanisms is proposed. For bare soil and forests, we point out that a linear unmixing model applied to the covariance matrix is able to split off polarimetric information on a land cover type basis. The assessment of the unmixed radar matrices is carried out with polarimetric radar images from the Radarsat-2 satellite. It was found that despite speckle, the reconstructed radar information after the unmixing process is statistically relevant with the observations. The question whether the unmixed radar images contain relevant thematic information is more challenging, but results tend to validate this property. This method could be used to have a better estimation of vegetation biomass in the context of open forested areas.

Non-photosynthetic vegetation biomass estimation in semiarid Canadian mixed grasslands using ground hyperspectral data, Landsat 8 OLI, and Sentinel-2 images

ABSTRACTResearch on quantifying non-photosynthetic vegetation (NPV) with optical remote-sensing approaches has been focusing on optically distinguishing NPV from green vegetation and bare soil. With a very similar spectral response curve to NPV, dry moss is a significant component in semiarid mixed grasslands and plays a large role in NPV estimation. However, limited attention has been paid to this role. We investigated the potential of optical remote sensing to distinguish NPV biomass in semiarid grasslands characterized by NPV, biological soil crust dominated by moss and lichen, and bare soil. First, hyperspectral spectral indices were examined to determine the most useful spectral wavelength regions for NPV biomass estimation. Second, multispectral red-edge indices and shortwave infrared (SWIR) indices were simulated based on Landsat 8 Operational Land Imager (OLI) and Sentinel-2A MultiSpectral Instrument band reflectance, respectively, to determine the most suitable multispectral indices for NPV estimation. The potential multispectral indices were then applied to Landsat 8 OLI images and Sentinel-2A images acquired in early, middle, peak, and early senescence growing seasons to investigate the potential of satellite images for quantifying NPV biomass. Our results indicated that hyperspectral red-edge indices, modified simple ratio, modified red-edge normalized difference vegetation index (mNDVI705), and normalized difference vegetation index (NDVI705) are better than the SWIR hyperspectral indices, including cellulose absorption index for quantifying NPV biomass. The simulated multispectral red-edge spectral indices (NDVIred-edge and mNDVIred-edge) demonstrate good and comparable performance on quantifying NPV biomass with SWIR multispectral indices (normalized difference index [NDI5 and NDI7] and soil-adjusted corn residue index). Nevertheless, the multispectral indices derived from Landsat 8 OLI and Sentinel-2 images have limited potential for NPV biomass estimation.

Estimating vegetation biomass and cover across large plots in shrub and grass dominated drylands using terrestrial lidar and machine learning

Terrestrial laser scanning (TLS) has been shown to enable an efficient, precise, and non-destructive inventory of vegetation structure at ranges up to hundreds of meters. We developed a method that leverages TLS collections with machine learning techniques to model and map canopy cover and biomass of several classes of short-stature vegetation across large plots. We collected high-definition TLS scans of 26 1-ha plots in desert grasslands and big sagebrush shrublands in southwest Idaho, USA. We used the Random Forests machine learning algorithm to develop decision tree models predicting the biomass and canopy cover of several vegetation classes from statistical descriptors of the aboveground heights of TLS points. Manual measurements of vegetation characteristics collected within each plot served as training and validation data. Models based on five or fewer TLS descriptors of vegetation heights were developed to predict the canopy cover fraction of shrubs (R2=0.77, RMSE=7%), annual grasses (R2=0.70, RMSE=21%), perennial grasses (R2=0.36, RMSE=12%), forbs (R2=0.52, RMSE=6%), bare earth or litter (R2=0.49, RMSE=19%), and the biomass of shrubs (R2=0.71, RMSE=175g) and herbaceous vegetation (R2=0.61, RMSE=99g) (all values reported are out-of-bag). Our models explained much of the variability between predictions and manual measurements, and yet we expect that future applications could produce even better results by reducing some of the methodological sources of error that we encountered. Our work demonstrates how TLS can be used efficiently to extend manual measurement of vegetation characteristics from small to large plots in grasslands and shrublands, with potential application to other similarly structured ecosystems. Our method shows that vegetation structural characteristics can be modeled without classifying and delineating individual plants, a challenging and time-consuming step common in previous methods applying TLS to vegetation inventory. Improving application of TLS to studies of shrub-steppe ecosystems will serve immediate management needs by enhancing vegetation inventories, environmental modeling studies, and the ability to train broader datasets collected from air and space.

Mapping Spartina alterniflora Biomass Using LiDAR and Hyperspectral Data

Large-scale coastal reclamation has caused significant changes in Spartina alterniflora (S. alterniflora) distribution in coastal regions of China. However, few studies have focused on estimation of the wetland vegetation biomass, especially of S. alterniflora, in coastal regions using LiDAR and hyperspectral data. In this study, the applicability of LiDAR and hypersectral data for estimating S. alterniflora biomass and mapping its distribution in coastal regions of China was explored to attempt problems of wetland vegetation biomass estimation caused by different vegetation types and different canopy height. Results showed that the highest correlation coefficient with S. alterniflora biomass was vegetation canopy height (0.817), followed by Normalized Difference Vegetation Index (NDVI) (0.635), Atmospherically Resistant Vegetation Index (ARVI) (0.631), Visible Atmospherically Resistant Index (VARI) (0.599), and Ratio Vegetation Index (RVI) (0.520). A multivariate linear estimation model of S. alterniflora biomass using a variable backward elimination method was developed with R squared coefficient of 0.902 and the residual predictive deviation (RPD) of 2.62. The model accuracy of S. alterniflora biomass was higher than that of wetland vegetation for mixed vegetation types because it improved the estimation accuracy caused by differences in spectral features and canopy heights of different kinds of wetland vegetation. The result indicated that estimated S. alterniflora biomass was in agreement with the field survey result. Owing to its basis in the fusion of LiDAR data and hyperspectral data, the proposed method provides an advantage for S. alterniflora mapping. The integration of high spatial resolution hyperspectral imagery and LiDAR data derived canopy height had significantly improved the accuracy of mapping S. alterniflora biomass.

Vegetation biomass estimation with remote sensing: focus on forest and other wooded land over the Mediterranean ecosystem

ABSTRACTCarbon sequestration service of Mediterranean forest and other wooded land is threatened by their fragile, complex, and highly evolving nature, due to both human disturbances and climate change. Remote-sensing methods for forest biomass estimation have gained increased attention, and substantial research has been conducted worldwide over the past four decades. Yet, the literature body focused on Mediterranean forests is rather limited as a result of their small extent compared to other biomes. We discuss the remote-sensing studies over the Mediterranean forest and other wooded land, discriminating research based on the primary data source used, such as optical imagery, datasets from active sensors, and combination of multisource data. The review indicates that there is a significant research gap in terms of the studies, as well as a need for a reduction of the errors and uncertainty of estimates, which are associated with both the sensors’ characteristics and the Mediterranean forest and other wooded land structure. Biomass estimates based on optical data were generally less accurate (R2 close to 0.70, where R2 is the coefficient of determination), however, when data from active sensors were involved, accuracy of estimations was considerably greater (usually R2 greater than 0.80). With respect to scale, most of the local scale studies established relationships with R2 over 0.70 and as high as 0.98, while the few regional scale studies exhibited R2 close to 0.80. Further, in-depth analysis can provide more efficient data fusion, classification methods, and procedures for operational regional and national assessment of forest biomass over such Mediterranean areas.

Aboveground biomass estimates of sagebrush using terrestrial and airborne LiDAR data in a dryland ecosystem

Vegetation biomass estimates across drylands at regional scales are critical for ecological modeling, yet the low-lying and sparse plant communities characterizing these ecosystems are challenging to accurately quantify and measure their variability using spectral-based aerial and satellite remote sensing. To overcome these challenges, multi-scale data including field-measured biomass, terrestrial laser scanning (TLS) and airborne laser scanning (ALS) data, were combined in a hierarchical modeling framework. Data derived at each scale were used to validate an increasingly broader index of sagebrush (Artemisia tridentata) aboveground biomass. First, two automatic crown delineation methods were used to delineate individual shrubs across the TLS plots. Second, three models to derive shrub volumes were utilized with TLS data and regressed against destructively-sampled individual shrub biomass measurements. Third, TLS-derived biomass estimates at 5m were used to calibrate a biomass prediction model with a linear regression of ALS-derived percent vegetation cover (adjusted R2=0.87, p<0.001, RMSE=3.59kg). The ALS prediction model was applied to the study watershed and evaluated with independent TLS plots (adjusted R2=0.55, RMSE=4.01kg, normalized RMSE=35%). The biomass estimates at the scale of 5m is sufficient for capturing the variability of biomass needed to initialize models to estimate ecosystem fluxes, and the contiguous estimates across the watershed support analyzing patterns and connectivity of these dynamics. Our model is currently optimized for the sagebrush-steppe environment at the watershed scale and may be readily applied to other shrub-dominated drylands, and especially the Great Basin, U.S., which extends across five western states. Improved derived metrics from ALS data and collection of additional TLS data to refine the relationship between TLS-derived biomass estimates and ALS-derived models of vegetation structure, will strengthen the predictive power of our model and extend its range to similar shrubland ecosystems.

Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites.

AimThe accurate mapping of forest carbon stocks is essential for understanding the global carbon cycle, for assessing emissions from deforestation, and for rational land-use planning. Remote sensing (RS) is currently the key tool for this purpose, but RS does not estimate vegetation biomass directly, and thus may miss significant spatial variations in forest structure. We test the stated accuracy of pantropical carbon maps using a large independent field dataset.LocationTropical forests of the Amazon basin. The permanent archive of the field plot data can be accessed at: http://dx.doi.org/10.5521/FORESTPLOTS.NET/2014_1MethodsTwo recent pantropical RS maps of vegetation carbon are compared to a unique ground-plot dataset, involving tree measurements in 413 large inventory plots located in nine countries. The RS maps were compared directly to field plots, and kriging of the field data was used to allow area-based comparisons.ResultsThe two RS carbon maps fail to capture the main gradient in Amazon forest carbon detected using 413 ground plots, from the densely wooded tall forests of the north-east, to the light-wooded, shorter forests of the south-west. The differences between plots and RS maps far exceed the uncertainties given in these studies, with whole regions over- or under-estimated by > 25%, whereas regional uncertainties for the maps were reported to be < 5%.Main conclusionsPantropical biomass maps are widely used by governments and by projects aiming to reduce deforestation using carbon offsets, but may have significant regional biases. Carbon-mapping techniques must be revised to account for the known ecological variation in tree wood density and allometry to create maps suitable for carbon accounting. The use of single relationships between tree canopy height and above-ground biomass inevitably yields large, spatially correlated errors. This presents a significant challenge to both the forest conservation and remote sensing communities, because neither wood density nor species assemblages can be reliably mapped from space.

Integration method to estimate above-ground biomass in arid prairie regions using active and passive remote sensing data

The use of microwave remote sensing for estimating vegetation biomass is limited in arid grassland regions because of the heterogeneous distribution of vegetation, sparse vegetation cover, and the strong influence from soil. To minimize the problem, a synergistic method of active and passive remote sensing data for retrieval of above-ground biomass (AGB) was developed in this paper. Vegetation coverage, which can be easily estimated from optical data, was combined in the scattering model. The total backscattering was divided into the amount attributed to areas covered with vegetation and that attributed to areas of bare soil. Backscattering coefficients were simulated using the established scattering model. A look-up table was established using the relationship between the vegetation water content and the backscattering coefficient for water content retrieval. Then, AGB was estimated using the relationship between the vegetation water content and the AGB. The method was applied to estimate the AGB of the Wutumeiren prairie. Finally, the accuracy and sources of error in this innovative AGB retrieval method were evaluated. The results showed that the predicted AGB correlated with the measured AGB ( R 2 = 0.8414 , RMSE = 0.1953 kg / m 2 ). Thus, the method has operational potential for the estimation of the AGB of herbaceous vegetation in arid regions.

红树林生态系统遥感监测研究进展

PDF HTML阅读 XML下载 导出引用 引用提醒 红树林生态系统遥感监测研究进展 DOI: 10.5846/stxb201205150715 作者: 作者单位: 国家海洋环境监测中心 海洋遥感室,国家海洋环境监测中心 海洋遥感室,奥胡斯大学生命科学系,奥胡斯 C丹麦,国家海洋局南海环境监测中心,国家海洋环境监测中心 海洋遥感室,国家海洋环境监测中心 海洋遥感室; 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金资助项目(41201179);海洋公益性行业科研专项(DOME(MEA)-01-03) A review on the application of remote sensing in mangrove ecosystem monitoring Author: Affiliation: National Marine Environmental Monitoring Center,National Marine Environmental Monitoring Center,Department of Bioscience,Plant Biology,Aarhus University Ole Worms Allé , Aarhus C,Denmark,,National Marine Environmental Monitoring Center,National Marine Environmental Monitoring Center;China;Department of Bioscience,Plant Biology,Aarhus University Ole Worms Allé , Aarhus C,Denmark Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:随着现代遥感技术的迅速发展,遥感监测已经成为红树林生态系统变化监测的重要手段和方法。从遥感技术在生态系统变化监测应用领域入手,综述了国内外红树林遥感监测的发展历程,系统总结了遥感技术在红树林湿地动态、种间分类、群落结构(叶面积指数、冠幅、树高等)、生物量、灾害灾情(病虫害、风暴潮等)、景观格局动态、驱动力、红树林湿地保护与管理等领域应用现状,归纳了不同应用领域遥感监测的理论、方法及研究现状。指出我国在红树林遥感监测中存在的不足。提出红树林遥感监测应在分类标准体系规范化、分类精度提升、红树林生态学特征参数(物种多样性、优势度等)、生态系统环境空间演变过程及遥感监测的尺度效应方面加大研究力度。充分发挥区域综合监测模型在红树林生态系统变化遥感监测中的作用。 Abstract:Mangrove trees form a community of woody plants in intertidal areas periodically immersed by sea water, and are located in tropical and sub-tropical zones. Mangrove forests are complicated ecological systems with characteristics of both land and sea, and form coastal ecologically critical areas (ECA). Monitoring ecological parameters of mangrove ecological systems has gained increased attention from governments and scholars. Ground-based investigation can lead to comprehensive understanding of the structure and functions of the mangrove ecosystem. However, because of spatial and temporal limitations, information on ecological changes of mangrove trees over long periods and large regions cannot be obtained in this way. Advancements in modern remote sensing technology, modeling and simulation, and landscape pattern analysis have provided important technological means for discerning spatiotemporal ecological ecosystem changes. In particular, remote sensing has become an important tool for obtaining temporal and spatial dimensions of ecological parameters of the mangrove forest ecological system. Statistics show that by May 2012, a total of 233 academic papers had been published outside China on remote sensing for monitoring mangrove forests, a number which is increasing yearly. This research mainly focuses on dynamic monitoring, inter-species classification, and structural monitoring of those forests. Especially since 2000, structural monitoring of mangrove communities and investigation of their driving forces and other aspects (sea level changes and comprehensive investigations) have become primary research topics. The application of remote sensing to mangrove forest ecosystem monitoring in China began in the 1990s and has been increasing remarkably in recent years, particularly during the period 2008-2011. This paper summarizes the status of such application and current problems. Specifically, the work expounds on the following aspects: 1) Theories and methods of dynamic monitoring of mangrove-covered wetlands. 2) Theories and methods of inter-species classification technology, as well as requirements of image data. In particular, classification of mangrove trees does not rely only on spectral characteristics, but also requires consideration of structural information that helps enhance classification precision. 3) Theories and methods of remote monitoring of structural parameters of mangrove communities (LAI, crown diameter, tree height and others). In addition, investigations of the relationship between the radar backscattering coefficient and crown diameter and vertical structure of mangrove trees, through establishing a model of their quantitative relationships; this facilitates remote monitoring of mangrove forest growth by applying C-band, L-band, P-band, C-VV and C-HH bands of NASA/JPL. 4) Theories and methods of remote monitoring and inversion of primary production of mangrove forests. Comparative analysis shows that the radar backscattering coefficient is more precise than the NDVI model in estimating vegetation biomass. 5) The status of disasters affecting mangrove forests (diseases, insect infestation and storm surges) and of monitoring theories. 6) Remote dynamic monitoring of and comments on the mangrove-covered region and inter-species landscape patterns. 7) Remote sensing of and comments on driving mechanisms of dynamic evolution of mangrove-covered wetlands. 7) Status and methods of application of remote sensing technology in protection and management of mangrove-covered wetlands. This paper points out existing deficiencies and challenges in remote monitoring of elements of the mangrove forest ecosystem, and emphasizes the need for more research into standardization of classification systems and enhancement of classification precision. Also needed is research into parameters of the ecological characteristics of mangrove forests (diversity and species dominance), spatial evolution of the ecological system, and dimensional effects of remote monitoring. Remote monitoring of the mangrove forest ecosystem lags behind in China, so more such studies should be undertaken in the country. 参考文献 相似文献 引证文献

Review on Correlation Analysis of Independent Variables in Estimation Models of Vegetation Biomass Based on Remote Sensing

Applying remote sensing to estimate vegetation biomass has been paid more attention.When using the remote sensing information parameters and other parameters to construct models,researchers often need choose some parameters as independent variables which have obvious influence on biomass.Therefore,identifying the number of independent variables and choosing independent variables play an important role in the reliability and precision of estimation models.From single-variable models,multi-variables models and nonparametric models,the article reviews on correlation analysis of independent variables in estimation models of vegetation biomass based on remote sensing,which offers some referenced value for choosing the independent variables in estimation models of vegetation biomass.

Field Test of Digital Photography Biomass Estimation Technique in Tallgrass Prairie

Fuel loading information is important for prescribed fire planning, evaluating wildfire risk, and understanding fire effects in grassland. Yet fuel loads in grasslands often go unmeasured because of the time required to clip plots and process samples, as well as limited access or proximity to a drying oven. We tested the digital photography biomass estimation technique for measuring fuel load in grasslands in two national parks in the eastern Great Plains. The method consists of using percentage image obstruction, as determined by digital photography, to estimate vegetation biomass based on a linear transformation (i.e., regressing dry clipped weights against percent digital obstruction). We used the technique with some modification and measured digital obstruction at two sites at Wilson’s Creek National Battlefield, Missouri (WICR), and three sites at Tallgrass Prairie National Preserve, Kansas (TAPR). The method did not result in strong correlations at either of the two sites at WICR (Site 1: r 2 5 0.02; Site 2: r 2 5 0.32), but performed relatively well at TAPR (Site 1 [, 1 yr since burn]: r 2 5 0.82; Site 2 [2 yr since burn]: r 2 5 0.57; Site 3 [1 yr since burn]: r 2 5 0.88). Linear regressions for the three sites at TAPR did not differ in slop e( P . 0.05). In general, the denser the vegetation, the weaker the relationship between the vegetation biomass of clip plots and the percentage image obstruction of digital images. The digital photography technique may not be useful for estimating fuel loads in grasslands with relatively high biomass (. 80 g ? 0.1 m 22 ) or digital image obstruction . 50%. Large amounts of litter may also potentially reduce the accuracy of the technique.

The ICESat-2 Laser Altimetry Mission

Satellite and aircraft observations have revealed that remarkable changes in the Earth's polar ice cover have occurred in the last decade. The impacts of these changes, which include dramatic ice loss from ice sheets and rapid declines in Arctic sea ice, could be quite large in terms of sea level rise and global climate. NASA's Ice, Cloud and Land Elevation Satellite-2 (ICESat-2), currently planned for launch in 2015, is specifically intended to quantify the amount of change in ice sheets and sea ice and provide key insights into their behavior. It will achieve these objectives through the use of precise laser measurements of surface elevation, building on the groundbreaking capabilities of its predecessor, the Ice Cloud and Land Elevation Satellite (ICESat). In particular, ICESat-2 will measure the temporal and spatial character of ice sheet elevation change to enable assessment of ice sheet mass balance and examination of the underlying mechanisms that control it. The precision of ICESat-2's elevation measurement will also allow for accurate measurements of sea ice freeboard height, from which sea ice thickness and its temporal changes can be estimated. ICESat-2 will provide important information on other components of the Earth System as well, most notably large-scale vegetation biomass estimates through the measurement of vegetation canopy height. When combined with the original ICESat observations, ICESat-2 will provide ice change measurements across more than a 15-year time span. Its significantly improved laser system will also provide observations with much greater spatial resolution, temporal resolution, and accuracy than has ever been possible before.

Remote Sensing of Vegetation Structure Using Computer Vision

High spatial resolution measurements of vegetation structure in three-dimensions (3D) are essential for accurate estimation of vegetation biomass, carbon accounting, forestry, fire hazard evaluation and other land management and scientific applications. Light Detection and Ranging (LiDAR) is the current standard for these measurements but requires bulky instruments mounted on commercial aircraft. Here we demonstrate that high spatial resolution 3D measurements of vegetation structure and spectral characteristics can be produced by applying open-source computer vision algorithms to ordinary digital photographs acquired using inexpensive hobbyist aerial platforms. Digital photographs were acquired using a kite aerial platform across two 2.25 ha test sites in Baltimore, MD, USA. An open-source computer vision algorithm generated 3D point cloud datasets with RGB spectral attributes from the photographs and these were geocorrected to a horizontal precision of 0.82) than computer vision (R2 &gt; 0.64), primarily because of difficulties observing terrain under closed canopy forest. Results confirm that computer vision can support ultra-low-cost, user-deployed high spatial resolution 3D remote sensing of vegetation structure.

Soil and vegetation carbon stocks in Brazilian Western Amazonia: relationships and ecological implications for natural landscapes

The relationships between soils attributes, soil carbon stocks and vegetation carbon stocks are poorly know in Amazonia, even at regional scale. In this paper, we used the large and reliable soil database from Western Amazonia obtained from the RADAMBRASIL project and recent estimates of vegetation biomass to investigate some environmental relationships, quantifying C stocks of intact ecosystem in Western Amazonia. The results allowed separating the western Amazonia into 6 sectors, called pedo-zones: Roraima, Rio Negro Basin, Tertiary Plateaux of the Amazon, Javari-Juruá-Purus lowland, Acre Basin and Rondonia uplands. The highest C stock for the whole soil is observed in the Acre and in the Rio Negro sectors. In the former, this is due to the high nutrient status and high clay activity, whereas in the latter, it is attributed to a downward carbon movement attributed to widespread podzolization and arenization, forming spodic horizons. The youthful nature of shallow soils of the Javari-Juruá-Purus lowlands, associated with high Al, results in a high phytomass C/soil C ratio. A similar trend was observed for the shallow soils from the Roraima and Rondonia highlands. A consistent east-west decline in biomass carbon in the Rio Negro Basin sector is associated with increasing rainfall and higher sand amounts. It is related to lesser C protection and greater C loss of sandy soils, subjected to active chemical leaching and widespread podzolization. Also, these soils possess lower cation exchangeable capacity and lower water retention capacity. Zones where deeply weathered Latosols dominate have a overall pattern of high C sequestration, and greater than the shallower soils from the upper Amazon, west of Madeira and Negro rivers. This was attributed to deeper incorporation of carbon in these clayey and highly pedo-bioturbated soils. The results highlight the urgent need for refining soil data at an appropriate scale for C stocks calculations purposes in Amazonia. There is a risk of misinterpreting C stocks in Amazonia when such great pedological variability is not taken into account.

Exploring TM image texture and its relationships with biomass estimation in Rondônia, Brazilian Amazon

Many texture measures have been developed and used for improving land-cover classification accuracy, but rarely has research examined the role of textures in improving the performance of aboveground biomass estimations. The relationship between texture and biomass is poorly understood. This paper used Landsat Thematic Mapper (TM) data to explore relationships between TM image textures and aboveground biomass in Rondônia, Brazilian Amazon. Eight grey level co-occurrence matrix (GLCM) based texture measures (i.e., mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation), associated with seven different window sizes (5x5, 7x7, 9x9, 11x11, 15x15, 19x19, and 25x25), and five TM bands (TM 2, 3, 4, 5, and 7) were analyzed. Pearson's correlation coefficient was used to analyze texture and biomass relationships. This research indicates that most textures are weakly correlated with successional vegetation biomass, but some textures are significantly correlated with mature forest biomass. In contrast, TM spectral signatures are significantly correlated with successional vegetation biomass, but weakly correlated with mature forest biomass. Our findings imply that textures may be critical in improving mature forest biomass estimation, but relatively less important for successional vegetation biomass estimation.

Analysis of vegetation indices for monitoring vegetation degradation in semi‐arid and arid areas of Mongolia

An attempt has been made in this study to delineate the characteristics of spectral signatures of the vegetation in terms of various vegetation indices (VIs), particularly the Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index2 (MSAVI2) and Enhanced Vegetation Index (EVI) to manifest their ability to estimate vegetation biomass over a large area and to monitor vegetation degradation in arid and semi‐arid area of Mongolia. Multi‐temporal SPOT‐4 VEGETATION data from 1998 to 2001 have been used for the analysis. The correlations between the vegetation indices observed at various degrees of vegetation coverage during different stages of growth were examined. The results showed that in Mongolian desert steppe and Gobi desert zone MSAVI2 is the best, while in mountain steppe zone EVI is found to estimate biomass well. Generally, it was found that total biomass was decreased by 50.7% and 31.4% of rangeland is very severe degraded in the case study area.

Seasonal diet selection of cattle grazing a montane riparian community

Abstract Cattle-grazing in riparian areas has become increasingly controversial. More information is needed about cattle use of these areas to develop Best Management Practices. This study was designed to determine seasonal changes in diet selection of cattle in a montane riparian community in northern Colorado. Previous cattle diet studies in riparian zones have not separated the interaction between seasonal preference and biomass utilization. The experiment was conducted within large exclosures that had not been grazed by livestock since 1956. Vegetation biomass estimates and diet samples from 5 esophageally fistulated steers were taken during 4 grazing periods (spring, early-summer, late- summer, and fall) in 1995. Vegetation measurements and diet samples were also collected during the late summer and fall of 1994. One of the 3 paddocks in each grazing period of 1995 had been grazed in 1994. Steer diets in 1995 were found to contain 15% more Carex spp. from paddocks that had been ungrazed until 1995 as...

Seasonal diet selection of cattle grazing a montane riparian community

Cattle-grazing in riparian areas has become increasingly controversial. More information is needed about cattle use of these areas to develop Best Management Practices. This study was designed to determine seasonal changes in diet selection of cattle in a montane riparian community in northern Colorado. Previous cattle diet studies in riparian zones have not separated the interaction between seasonal preference and biomass utilization. The experiment was conducted within large exclosures that had not been grazed by livestock since 1956. Vegetation biomass estimates and diet samples from 5 esophageally fistulated steers were taken during 4 grazing periods (spring, early-summer, late- summer, and fall) in 1995. Vegetation measurements and diet samples were also collected during the late summer and fall of 1994. One of the 3 paddocks in each grazing period of 1995 had been grazed in 1994. Steer diets in 1995 were found to contain 15% more Carex spp. from paddocks that had been ungrazed until 1995 as compared with paddocks previously grazed in 1994. Kentucky bluegrass (Poa pratensis L.) and forbs were consumed about 13 times and 5 times greater, respectively, in previously grazed paddocks as compared with ungrazed paddocks. Carex spp. were the most preferred component of cattle diets throughout the study. Willows (Salix spp.) were not preferred in any grazing period of either year, and consisted of less than 4% of cattle diets in any season. However, a trend towards increased consumption of willows was found from spring to fall, 1995. Information obtained in this study should help managers develop grazing systems that allow for a desirable combination of herbaceous and woody species in a riparian community while still affording grazeable forage.

Wetland vegetation biomass estimation and mapping from Landsat ETM data: a case study of poyang lake

Poyang Lake is the largest freshwater lake in China. This paper conducted a digital and rapid investigation of the lake’s wetland vegetation biomass using Landsat ETM data acquired on April 16, 2000. First, utilizing the false color composite derived from the ETM data as one of the main references, the authors designed a reasonable sampling route for field measurement of the biomass, and carried it out on April 18–28, 2000. Then after both the sampling data and the ETM data were geometrically corrected to an equal-area projection of Albers, linear relationships among the sampling data and some transformed data derived from the ETM data and the ETM 4 were calculated. The results show that the sampling data is best relative to the band 4 data with a high correlation coefficient of 0.86, followed by the DVI and NDVI data with 0.83 and 0.80 respectively. Therefore, a linear regression model, which was based on the field data and band 4 data, was used to estimate the total biomass of entire Poyang Lake, and then the map of the biomass distribution was compiled.