Above ground biomass functions with vegetation indices for multiple use systems of two evergreen oaks

Remote sensing is a promising approach for above ground biomass estimation, as forest parameters can be obtained indirectly. The analysis in space and time is quite straight forward due to the flexibility of the method to determine forest crown parameters with remote sensing. It can be used to evaluate and monitoring for example the development of a forest area in time and the impact of disturbances, such as silvicultural practices or deforestation. The vegetation indices, which condense data in a quantitative numeric manner, have been used to estimate several forest parameters, such as the volume, basal area and above ground biomass. The objective of this study was the development of allometric functions to estimate above ground biomass using vegetation indices as independent variables. The vegetation indices used were the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Simple Ratio (SR) and Soil-Adjusted Vegetation Index (SAVI). QuickBird satellite data, with 0.70 m of spatial resolution, was orthorectified, geometrically and atmospheric corrected, and the digital number were converted to top of atmosphere reflectance (ToA). Forest inventory data and published allometric functions at tree level were used to estimate above ground biomass per plot. Linear functions were fitted for the monospecies and multispecies stands of two evergreen oaks (Quercus suber and Quercus rotundifolia) in multiple use systems, montados. The allometric above ground biomass functions were fitted considering the mean and the median of each vegetation index per grid as independent variable. Species composition as a dummy variable was also considered as an independent variable. The linear functions with better performance are those with mean NDVI or mean SR as independent variable. Noteworthy is that the two better functions for monospecies cork oak stands have median NDVI or median SR as independent variable. When species composition dummy variables are included in the function (with stepwise regression) the best model has median NDVI as independent variable. The vegetation indices with the worse model performance were EVI and SAVI.

Ecological and social characterization of key resource areas in Kenyan rangelands

Key resource areas (KRAs), defined as dry season foraging zones for herbivores, were studied relative to the more extensive outlying rangeland areas (non-KRAs) in Kenya. Field surveys with pastoralists, ranchers, scientists and government officials delineated KRAs on the ground. Identified KRAs were mapped based on global positioning and local experts' information on KRAs accessibility and ecological attributes. Using the map of known KRAs and non-KRAs, we examined characteristics of soils, climate, topography, land use/cover attributes at KRAs relative to non-KRAs. How and why do some areas (KRAs) support herbivores during droughts when forage is scarce in other areas of the landscape? We hypothesized that KRAs have fundamental ecological and socially determined attributes that enable them to provide forage during critical times and we sought to characterize some of those attributes in this study. At the landscape level, KRAs took different forms based on forage availability during the dry season but generally occurred in locations of the landscape with aseasonal water availability and/or difficult to access areas during wet season forage abundance. Greenness trends for KRAs versus non-KRAs were evaluated with a 22-year dataset of Normalized Difference Vegetation Index (NDVI). Field surveys of KRAs provided qualitative information on KRAs as dry season foraging zones. At the scale of the study, soil attributes did not significantly differ for KRAs compared to non-KRAs. Slopes of KRA were generally steeper compared to non-KRAs and elevation was higher at KRAs. Field survey respondents indicated that animals and humans generally avoid difficult to access hilly areas using them only when all other easily accessible rangeland is depleted of forage during droughts. Understanding the nature of KRAs will support identification, protection and restoration of critical forage hotspots for herbivores by strengthening rangeland inventory, monitoring, policy formulation, and conservation efforts to improve habitats and human welfare. (c) 2007 Elsevier Ltd. All rights reserved.

Assessing the relationship between shire winter crop yield and seasonal variability of the MODIS NDVI and EVI images

Australian researchers have been developing robust yield estimation models, based mainly on the crop growth response to water availability during the crop season. However, knowledge of spatial distribution of yields within and across the production regions can be improved by the use of remote sensing techniques. Images of Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, available since 1999, have the potential to contribute to crop yield estimation. The objective of this study was to analyse the relationship between winter crop yields and the spectral information available in MODIS vegetation index images at the shire level. The study was carried out in the Jondaryan and Pittsworth shires, Queensland , Australia . Five years (2000 to 2004) of 250m resolution, 16-day composite of MODIS Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images were used during the winter crop season (April to November). Seasonal variability of the profiles of the vegetation index images for each crop season using different regions of interest (cropping mask) were displayed and analysed. Correlation analysis between wheat and barley yield data and MODIS image values were also conducted. The results showed high seasonal variability in the NDVI and EVI profiles, and the EVI values were consistently lower than those of the NDVI. The highest image values were observed in 2003 (in contrast to 2004), and were associated with rainfall amount and distribution. The seasonal variability of the profiles was similar in both shires, with minimum values in June and maximum values at the end of August. NDVI and EVI images showed sensitivity to seasonal variability of the vegetation and exhibited good association (e.g. r = 0.84, r = 0.77) with winter crop yields.

Remote sensing of nitrogen and water stress in wheat

Nitrogen (N) is the largest agricultural input in many Australian cropping systems and applying the right amount of N in the right place at the right physiological stage is a significant challenge for wheat growers. Optimizing N uptake could reduce input costs and minimize potential off-site movement. Since N uptake is dependent on soil and plant water status, ideally, N should be applied only to areas within paddocks with sufficient plant available water. To quantify N and water stress, spectral and thermal crop stress detection methods were explored using hyperspectral, multispectral and thermal remote sensing data collected at a research field site in Victoria, Australia. Wheat was grown over two seasons with two levels of water inputs (rainfall/irrigation) and either four levels (in 2004; 0, 17, 39 and 163 kg/ha) or two levels (in 2005; 0 and 39 kg/ha N) of nitrogen. The Canopy Chlorophyll Content Index (CCCI) and modified Spectral Ratio planar index (mSRpi), two indices designed to measure canopy-level N, were calculated from canopy-level hyperspectral data in 2005. They accounted for 76% and 74% of the variability of crop N status, respectively, just prior to stem elongation (Zadoks 24). The Normalised Difference Red Edge (NDRE) index and CCCI, calculated from airborne multispectral imagery, accounted for 41% and 37% of variability in crop N status, respectively. Greater scatter in the airborne data was attributable to the difference in scale of the ground and aerial measurements (i.e., small area plant samples against whole-plot means from imagery). Nevertheless, the analysis demonstrated that canopy-level theory can be transferred to airborne data, which could ultimately be of more use to growers. Thermal imagery showed that mean plot temperatures of rainfed treatments were 2.7 °C warmer than irrigated treatments (P < 0.001) at full cover. For partially vegetated fields, the two-Dimensional Crop Water Stress Index (2D CWSI) was calculated using the Vegetation Index-Temperature (VIT) trapezoid method to reduce the contribution of soil background to image temperature. Results showed rainfed plots were consistently more stressed than irrigated plots. Future work is needed to improve the ability of the CCCI and VIT methods to detect N and water stress and apply both indices simultaneously at the paddock scale to test whether N can be targeted based on water status. Use of these technologies has significant potential for maximising the spatial and temporal efficiency of N applications for wheat growers. ‘Ground–breaking Stuff’- Proceedings of the 13th Australian Society of Agronomy Conference, 10-14 September 2006, Perth, Western Australia.

Remote sensing applications for cotton.

This paper compares classified normalized difference vegetation index images of cotton crops derived from both low and high resolution satellite imagery to determine the most accurate and feasible option for Australian cotton growers. It also demonstrates a rapid automated processing and internet delivery system for distributing satellite SPOT-2 imagery. Also provided is the profile of two case studies conducted in the Darling Towns demonstrating the potential benefit of adopting this technology for improving in-season agronomic crop assessments and therefore enable improved management decisions to be made.

Reassessing spatial and temporal dynamics of kangaroo populations

Long-running datasets from aerial surveys of kangaroos (Macropus giganteus, Macropus [uliginosus, Macropus robustus and Macropus rufus) across Queensland, New South Wales and South Australia have been analysed, seeking better predictors of rates of increase which would allow aerial surveys to be undertaken less frequently than annually. Early models of changes in kangaroo numbers in response to rainfall had shown great promise, but much variability. We used normalised difference vegetation index (NDVI) instead, reasoning that changes in pasture condition would provide a better predictor than rainfall. However, except at a fine scale, NDVI proved no better; although two linked periods of rainfall proved useful predictors of rates of increase, this was only in some areas for some species. The good correlations reported in earlier studies were a consequence of data dominated by large droughtinduced adult mortality, whereas over a longer time frame and where changes between years are less dramatic, juvenile survival has the strongest influence on dynamics. Further, harvesting, density dependence and competition with domestic stock are additional and important influences and it is now clear that kangaroo movement has a greater influence on population dynamics than had been assumed. Accordingly, previous conclusions about kangaroo populations as simple systems driven by rainfall need to be reassessed. Examination of this large dataset has permitted descriptions of shifts in distribution of three species across eastern Australia, changes in dispersion in response to rainfall, and an evaluation of using harvest statistics as an index of density and harvest rate. These results have been combined into a risk assessment and decision theory framework to identify optimal monitoring strategies.

Estimating winter crop area across seasons and regions using time-sequential MODIS imagery.

The wheat grain industry is Australia's second largest agricultural export commodity. There is an increasing demand for accurate, objective and near real-time crop production information by industry. The advent of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite platform has augmented the capability of satellite-based applications to capture reflectance over large areas at acceptable pixel scale, cost and accuracy. The use of multi-temporal MODIS-enhanced vegetation index (EVI) imagery to determine crop area was investigated in this article. Here the rigour of the harmonic analysis of time-series (HANTS) and early-season metric approaches was assessed when extrapolating over the entire Queensland (QLD) cropping region for the 2005 and 2006 seasons. Early-season crop area estimates, at least 4 months before harvest, produced high accuracy at pixel and regional scales with percent errors of -8.6% and -26% for the 2005 and 2006 seasons, respectively. In discriminating among crops at pixel and regional scale, the HANTS approach showed high accuracy. The errors for specific area estimates for wheat, barley and chickpea were 9.9%, -5.2% and 10.9% (for 2005) and -2.8%, -78% and 64% (for 2006), respectively. Area estimates of total winter crop, wheat, barley and chickpea resulted in coefficient of determination (R(2)) values of 0.92, 0.89, 0.82 and 0.52, when contrasted against the actual shire-scale data. A significantly high coefficient of determination (0.87) was achieved for total winter crop area estimates in August across all shires for the 2006 season. Furthermore, the HANTS approach showed high accuracy in discriminating cropping area from non-cropping area and highlighted the need for accurate and up-to-date land use maps. The extrapolability of these approaches to determine total and specific winter crop area estimates, well before flowering, showed good utility across larger areas and seasons. Hence, it is envisaged that this technology might be transferable to different regions across Australia.

Comparison of compost and a sandy loam as turf underlay materials on salt-affected parkland

Turfgrasses range from extremely salt sensitive to highly salt tolerant. However, the selection of a salt tolerant turf is not a 'silver bullet' solution to successful turf growth on salt-affected parklands. Interactions between factors such as cultivar, construction practices, establishment, and maintenance can be complex and should not be considered in isolation of one another. Taking this holistic approach, a study investigating cultivar evaluation for salt-affected sites also included a comparison of topsoil materials as turf underlay, as well as pre-treatment of the sod. The turf species and cultivars used in the study were: Cynodon dactylon, cultivar 'Oz Tuff (I) '; Paspalum vaginatum, cultivars 'Sea Isle 1 (I) ' and 'Velvetene (I) '; Zoysia matrella cultivar 'A-1 (I) '; and Zoysia japonica, cultivar 'Empire (I) '. The two underlay materials were compost (100%) or a sandy clay topsoil each applied above a coastal sand profile to a depth of 10 cm. Rooting depth or root dry weight did not significantly differ among turf cultivars. Compost profile treatment had significantly greater root mass than the topsoil among all turf cultivars. This higher root production was reflected by improved quality of all turf at the final evaluation. Turfgrass grown on compost had a higher normalised difference vegetation index (NDVI), regardless of whether full sod or bare-rooted turfgrass was used. The use of a quality underlay was paramount to the successful growth of the turf cultivars investigated. While each cultivar had superior performance in sub-optimal conditions, the key to success was the selection of the right species and cultivar for each situation combined with proper establishment and maintenance of each turf grass.

Männyn neulaskadon arviointi laserkeilauksella

Leaf and needle biomasses are key factors in forest health. Insects that feed on needles cause growth losses and tree mortality. Insect outbreaks in Finnish forests have increased rapidly during the last decade and due to climate change the damages are expected to become more serious. There is a need for cost-efficient methods for inventorying these outbreaks. Remote sensing is a promising means for estimating forests and damages. The purpose of this study is to investigate the usability of airborne laser scanning in estimating Scots pine defoliation caused by the common pine sawfly (Diprion pini L.). The study area is situated in Ilomantsi district, eastern Finland. Study materials included high-pulse airborne laser scannings from July and October 2008. Reference data consisted of 90 circular field plots measured in May-June 2009. Defoliation percentage on these field plots was estimated visually. The study was made on plot-level and methods used were linear regression, unsupervised classification, Maximum likelihood method, and stepwise linear regression. Field plots were divided in defoliation classes in two different ways: When divided in two classes the defoliation percentages used were 0–20 % and 20–100 % and when divided in four classes 0–10 %, 10–20 %, 20–30 % and 30–100 %. The results varied depending on method and laser scanning. In the first laser scanning the best results were obtained with stepwise linear regression. The kappa value was 0,47 when using two classes and 0,37 when divided in four classes. In the second laser scanning the best results were obtained with Maximum likelihood. The kappa values were 0,42 and 0,37, correspondingly. The feature that explained defoliation best was vegetation index (pulses reflected from height > 2m / all pulses). There was no significant difference in the results between the two laser scannings so the seasonal change in defoliation could not be detected in this study.

Random forest algorithm with derived geographical layers for improved classification of remote sensing data

Effective conservation and management of natural resources requires up-to-date information of the land cover (LC) types and their dynamics. The LC dynamics are being captured using multi-resolution remote sensing (RS) data with appropriate classification strategies. RS data with important environmental layers (either remotely acquired or derived from ground measurements) would however be more effective in addressing LC dynamics and associated changes. These ancillary layers provide additional information for delineating LC classes' decision boundaries compared to the conventional classification techniques. This communication ascertains the possibility of improved classification accuracy of RS data with ancillary and derived geographical layers such as vegetation index, temperature, digital elevation model (DEM), aspect, slope and texture. This has been implemented in three terrains of varying topography. The study would help in the selection of appropriate ancillary data depending on the terrain for better classified information.

A simple model for spatial disaggregation of evaporative fraction: Comparative study with thermal sharpened land surface temperature data over India

[1] Evaporative fraction (EF) is a measure of the amount of available energy at the earth surface that is partitioned into latent heat flux. The currently operational thermal sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) on satellite platforms provide data only at 1000 m, which constraints the spatial resolution of EF estimates. A simple model (disaggregation of evaporative fraction (DEFrac)) based on the observed relationship between EF and the normalized difference vegetation index is proposed to spatially disaggregate EF. The DEFrac model was tested with EF estimated from the triangle method using 113 clear sky data sets from the MODIS sensor aboard Terra and Aqua satellites. Validation was done using the data at four micrometeorological tower sites across varied agro-climatic zones possessing different land cover conditions in India using Bowen ratio energy balance method. The root-mean-square error (RMSE) of EF estimated at 1000 m resolution using the triangle method was 0.09 for all the four sites put together. The RMSE of DEFrac disaggregated EF was 0.09 for 250 m resolution. Two models of input disaggregation were also tried with thermal data sharpened using two thermal sharpening models DisTrad and TsHARP. The RMSE of disaggregated EF was 0.14 for both the input disaggregation models for 250 m resolution. Moreover, spatial analysis of disaggregation was performed using Landsat-7 (Enhanced Thematic Mapper) ETM+ data over four grids in India for contrasted seasons. It was observed that the DEFrac model performed better than the input disaggregation models under cropped conditions while they were marginally similar under non-cropped conditions.

鄂尔多斯油蒿生物量遥感动态监测研究

本研究通过对油蒿灌丛及研究地区土壤连续5个月的光谱观测，指出干旱半干旱地区土壤光谱反射的特点，油蒿灌丛的光谱反射将无可避免受土壤背景的影响．分析了油蒿灌丛光谱反射的季节动态变化，指出油蒿灌丛光谱反射不仅取决于叶片，枝条也是重要的反射构件．引入植被指数(Vegetation Index)，采用常规方法与遥感技术相结合估测油蓠单丛生物量，获得较单纯常规方法更高的估测精度，表明在干旱半干旱地区，应用植被指数估测灌丛生物量是可行的．对油蒿单优群落现存生物量动态监测，表明油蒿群落生物量8月份达到最高，是适应生态气候因子，特别是水因子的结果．观测不同深度土壤光谱反射，显示了土壤持水的稳定性．通过对研究地区土壤光谱观测，得到一条适合本地区的土壤线(8011line)．通过比较几种植被指数消除土壤背景影响和估测生物量的效果，选取综合效果最佳的植被指数- SAVI( Soil-Adjusted Vegetation Index)．利用植被指数区分典型地块，提取油蒿地块面积．

全球变化下中国东北样带植物多样性梯度分析及气候-NDVI-植被的关系研究

中国东北样带（NorthEast China Transect, NECT）是位于中纬度温带以降水量作为主要驱动因素的陆地样带。本文的工作以此作为研究平台，利用生态信息系统（Ecological Information System, EIS）以及Microsoft Excel 7.0软件包建立了样带的地理数据库和植物多样性数据库，包括气候数据库、植被数据库、遥感数据库和内蒙内C_4植物数据库以及样带内生态系统特征数据库。在此基础上，主要研究了以下四个方面的内容： 1. 利用Holdridge的生命地带方法对NECT内的生物群区进行了划分。 主要是确定了生物群区间过渡带的位置与宽度，并预测了在全球变化三种模式下NECT内生物群区，尤其是过渡带的变化图景。湿度升高2 ℃后，过渡带的面积都呈扩大化的趋势。森林区对于降水量的变化反应很敏感。荒漠灌丛（即荒漠草原类型）由于其水热条件处于样带内较极端类型，因而对于全球气候变化反应也比较敏感。 2. 研究了NECT内的α、β多样性以及包括生活型、水分生态型、区系地理成分等在内的植物群落特征多样性的梯度变化规律。 研究了样带内的多样性梯度，提出了在样带内存在的α多样性测度问题以及β多样性沿样带的变化规律：样带内由东到西，β多样性逐渐升高，群落内物种被替代的速率变慢;两种植被类型边界上的两个样地之间的相似程度由东到西呈上升趋势;同一类型群落之间的物种周转率比不同类型群落间的物种周转率相对要低。同时将各个环境因子与α、β多样性作了回归分析，找出样带内决定α、β多样性的主要环境因子指标。 样带内沿43.5°N一线附近植物群落的生活型共有17类，水分生态型8类，区系地理成分包括17类，以此为基础分析了群落特征沿样带的变化规律。并探讨了生活型分布的历史地理原因。 3. 对样带气候－NDVI间的关系以及植被－NDVI的关系进行了探讨。 利用来自气象卫星的遥感数据一归一化植被指数（NDVI），和数值化后的样带1:100万植被图进行叠加，找到NECT内每种植被类型对应的NDVI值。样带内共有植被类型147种，反映在NDVI变化上的植被类型有106类。其中，自然植被101种。 影响年均NDVI分布的因子主要有经度、辐射日照百分率及7月温度，与经度呈正相关，与辐射日照时数及7月温度呈负相关。回归方程如下： NDVI = -220.426 + 3.273Lon - 80.338Ratio - 1.962T_7 (R~2 = 0.9714, F = 521.52, p < 0.001) 4. 研究了NECT内的光合功能型。 主要包括内蒙古地区的C_4植物及其生态地理特性。揭标C_4植物的分类群特性、生活型、水分生态型与区系地理成分等生态学特性。C_4植物分布的科属极其集中。C_4光合型为维管植物某些分类群（科、属、种）的特性，为它们固有的遗传特性。推断C_4起源于草本的某些科属。C_4植物为喜热、耐旱的类群。世界种、泛热带种、泛地中海种C_4植物较集中。 样带内的C_3、C_4功能型及其与环境因子的相关性。样带内C_4和C_3光合型植物组成比例由东到西表现出两高两低的趋势。分布主要与年均温和降水量呈显著相关。 提出了一种新的C_3、C_4鉴别方法。即根据野外测定的光合数据建立了C_3、C_4的判别模型： f_1(x) = -1.5493 + 0.1427Pn + 0.1035Tr + 0.3768ΔT + 0.1000Gs f_2(x) = -15.6142 + 1.0542Pn - 0.2503Tr - 0.2957ΔT + 0.6491Gs 最后，综合7个GCMs模型（GFDL，GISS，LLNL，MPI，OSU，UKMOH，UKMOL）的输出结果，利用此结果和本文建立的回归模型，模拟了样带内生物多样性的窨分布格局，并预测了末来全球变化下归一化植被指数NDVI的空间分布格局的变化。

探索一种监测典型草原草场退化的新方法--结合群落样方调查资料和卫星遥感数据（TM）

大范围、实时、准确地监测典型草原地区草场退化或健康状况对于草原生态系统的保育、农牧业的可持续性发展具有非常重要的意义。比起传统的群落学研究方法，遥感技术对于监测大尺度的植被状况具有无可争议的优越性，并且已经被广泛引入监测植被覆盖变化的研究中。本项研究系统地分析、综述了过去用非遥感手段对放牧和草场退化的关注和研究，介绍了遥感技术应用于植被研究的理论基础、主要途径（植被指数）、有关领域的研究进展。特别是本文提出草场退化状况或整体健康状况可以由基干相互独立的层面表示，而过去监测植被变化主要依赖的NDVI等植被指数只能监测草原植被的个别层面（总量层）。 本文以草场放牧退化比较典型的内蒙古锡林河流域为研究对象，在进行了大量的野外样方调查的基础上，提出一种结合群落样方调查和遥感技术的监测草场健康状况的新方法。本文引入主成份分析方法（PCA），从包含12个反映群落各方面信息的变量中提取出3个有特定生态学意义的主成份，并进一步对其进行分析组合，得出一个能比较敏感、全面反映群落健康状况的新指标-草场健康指数（GHI）。 从6波段的植被光谱反射数据中比较理想地提取出2个主成份：可见光因子和红外光因子。表征群落总量、放牧退化的主成份和GHI与样方光谱反射值有相当的相关性，由此得到GHI与可光、红外光因子的回归模型。 应用此模型到卫星遥感数据（TM），得到GHI影像，并与同一数据的NDVI影像作对比研究，发现GHI在反映放牧等人为干扰对草原植被的影响效应方面比NDVI有明显的优点。此外，GHI影像对植被分布格局，特别是斑块结构有更好的显示效果。应用GHI到历史TM数据，对所研究地域的植被覆盖变化、农牧业的变迁模式等进行了定性研究。研究还发现有较长放牧史的过度放牧区的植被类型没有沿牧压梯度的规律性分布，而是呈随机斑块分布模式。

人工神经网络在植被遥感影像分类中的应用研究

比起传统的统计方法，人工神经网络具有很好的非线性处理和并行计算能力，在植被遥感信息处理中得到广泛的应用。本研究系统地介绍了人工神经网络理论及其在植被遥感信息处理中的应用现状。并就如何提高人工神经网络的相干被遥感影像的分类能力进行了详细研究。首次提出了结合植被指数和组成分分析的神经网络分类方法。过去这方面的研究工作大都集中在通过选择一个合适的神经网络模型来提高植被分类精度，而我们认为：根据植被遥感自身的规律，结合统计方法，确定合适的网络输入模式的特征变量，也可以提高分类精度。 研究结果表明，尽管一般的神经网络分类器不需要对输入的模式做明显的特征提取，网络的隐层就具有特征提取的功能。但对TM影像七个波段和常用的五个植被指数（PVI、NDVI、WDVI、PVI、MSAVI2），分别做主成分分析，从而获得人工神经网络输入的特征变量，使用这样一种结合VI、PCA的神经网络对遥感TM多波段影像进行植被分类，能大大提高分类的精度。