测量的区域土地覆盖格局研基于多尺度遥感究

 利用1km、4km和8km 3种空间分辨率的NOAA/AVHRR数字影像,对中国NECT样带西部地区进行了土地覆盖分类及其景观特征的比较研究。重点比较了几种空间分辨率遥感数据分类结果边界的一致性和空间差异,以及影像所记录的景观格局的差异。为进一步在不同尺度上研究景观变化过程以及尺度转换研究奠定了基础。研究表明：3种空间分辨率的遥感影像所反映的区域土地覆盖的宏观空间格局是一致的,但类型的边界、每一类型斑块的形状和数量均产生较大的差异;经过对反映景观空间结构的4种指标（分维数、破碎度、多样性、优势度）的比较显示出随着遥感影像空间分辨率的变化,影像所反映的景观结构发生了较大的变化。其中,各覆盖类型的分维数表现出最大差异,表征着空间分辨率的变化对斑块复杂程度的影响最大。     

测量的区域土地覆盖格局研基于多尺度遥感究

利用1km、4km和8km 3种空间分辨率的NOAA/AVHRR数字影像,对中国NECT样带西部地区进行了土地覆盖分类及其景观特征的比较研究。重点比较了几种空间分辨率遥感数据分类结果边界的一致性和空间差异,以及影像所记录的景观格局的差异。为进一步在不同尺度上研究景观变化过程以及尺度转换研究奠定了基础。研究表明：3种空间分辨率的遥感影像所反映的区域土地覆盖的宏观空间格局是一致的,但类型的边界、每一类型斑块的形状和数量均产生较大的差异;经过对反映景观空间结构的4种指标（分维数、破碎度、多样性、优势度）的比较显示出随着遥感影像空间分辨率的变化,影像所反映的景观结构发生了较大的变化。其中,各覆盖类型的分维数表现出最大差异,表征着空间分辨率的变化对斑块复杂程度的影响最大。     

基于植被信息季节变换的植被覆盖度变化——以福建省连江县为例

基于植被覆盖度的植被信息遥感变化检测已成为研究植被及其相关生态系统变化的主要途径,但由于云覆盖等天气条件的影响,很难获得不同年份同一季节覆盖整个研究区的光学遥感影像来进行植被变化检测,而采用季节差异的影像必然会影响植被变化检测的结果.为此,本研究利用中高分辨率遥感数据的空间分辨率优势和MODIS遥感数据的时间分辨率优势,基于二者关系的拟合,提出一种植被信息季节变换的方法,将不同季节影像的植被覆盖度变换到研究所需的季节上.结果表明: 将该方法应用到福建敖江流域连江片区发现,植被信息变换的效果较好,经过将覆盖研究区的2007年冬季和2013年春季的中高分辨率影像的植被信息统一变换到夏季后,2007年的植被覆盖度由66.5%上升到79.7%,2013年由58.6%上升到77.9%,有效消除了因季节差异而对植被覆盖度估算产生的误差,提高了结果的准确性.     

Land surface phenology as an indicator of biodiversity patterns

With the rapid decline in biodiversity worldwide it is imperative to develop procedures for assessing changes in biodiversity across space. The synoptic view provided by imaging remote sensors constitutes a suitable approach for analyzing biodiversity from local to regional scales. A procedure based on the close relationship between floristic similarity and the similarity in land surface phenology was recently developed and successfully applied to assess diversity patterns using time series imagery acquired by the Moderate Resolution Imaging Spectro-radiometer (MODIS). However, as it depends on high temporal resolution remotely sensed data (e.g., MODIS), the procedure is constrained by the coarse spatial resolution characterizing these high temporal resolution data. Using an optimized technique for image fusion, we combined high temporal resolution data acquired by the MODIS sensor system with moderate spatial resolution data acquired by the Landsat TM/ETM+ sensor systems. Our results show that the MODIS/Landsat data fusion allows the characterization of land surface phenology at higher spatial resolutions, which better corresponded with information acquired within vegetation survey plots established in temperate montane forests located in Wolong Nature Reserve, Sichuan Province, China. As such, the procedure is useful for capturing changes in biodiversity induced by disturbances operating at large spatial scales and constitutes a suitable tool for monitoring and managing biodiversity.     

黄淮海多熟种植农业区作物历遥感检测与时空特征

多熟种植是高强度农业土地利用的重要特征,但由于缺乏在空间和时间上清晰描述农业多熟种植和作物种植历时空分布的数据,使得区域尺度农田生态系统碳动态估计、农田生产力监测与模拟等有很大的不确定性。黄淮海农业区是以冬小麦-夏玉米二熟制为主的我国粮食主产区,冬小麦和夏玉米分别为光合作用途径为C3和C4的作物,已有研究证明如果在估算生态系统生产力时不考虑一年两季作物及其光能利用率的差异则会导致生产力估算结果过低。研究结合农业气象站点地面作物物候观测数据和空间分辨率500m、8d合成的MOD IS时间序列数据,分析研究区二熟制作物的生长过程、物候特征和作物历的空间差异,发展基于EVI和LSWI时间序列曲线检测多熟区各季作物种植历的方法,获取黄淮海农业区空间表述清晰的熟制和各季作物的生长开始与结束时间数据,并应用农业气象站点数据对方法和所获取的作物历数据进行了比较验证。论述的方法和提取的各季作物的作物历时空数据将能够应用于区域尺度农田生产力估算、生物地球化学循环模拟和农业生态系统监测。     

Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data

Background

Remotely-sensed environmental data from earth-orbiting satellites are increasingly used to model the distribution and abundance of both plant and animal species, especially those of economic or conservation importance. Time series of data from the MODerate-resolution Imaging Spectroradiometer (MODIS) sensors on-board NASA''s Terra and Aqua satellites offer the potential to capture environmental thermal and vegetation seasonality, through temporal Fourier analysis, more accurately than was previously possible using the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor data. MODIS data are composited over 8- or 16-day time intervals that pose unique problems for temporal Fourier analysis. Applying standard techniques to MODIS data can introduce errors of up to 30% in the estimation of the amplitudes and phases of the Fourier harmonics.

Methodology/Principal Findings

We present a novel spline-based algorithm that overcomes the processing problems of composited MODIS data. The algorithm is tested on artificial data generated using randomly selected values of both amplitudes and phases, and provides an accurate estimate of the input variables under all conditions. The algorithm was then applied to produce layers that capture the seasonality in MODIS data for the period from 2001 to 2005.

Conclusions/Significance

Global temporal Fourier processed images of 1 km MODIS data for Middle Infrared Reflectance, day- and night-time Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) are presented for ecological and epidemiological applications. The finer spatial and temporal resolution, combined with the greater geolocational and spectral accuracy of the MODIS instruments, compared with previous multi-temporal data sets, mean that these data may be used with greater confidence in species'' distribution modelling.     

The role of land cover in bioclimatic models depends on spatial resolution

Aim We explored the importance of climate and land cover in bird species distribution models on multiple spatial scales. In particular, we tested whether the integration of land cover data improves the performance of pure bioclimatic models. Location Finland, northern Europe. Methods The data of the bird atlas survey carried out in 1986–89 using a 10 × 10 km uniform grid system in Finland were employed in the analyses. Land cover and climatic variables were compiled using the same grid system. The dependent and explanatory variables were resampled to 20‐km, 40‐km and 80‐km resolutions. Generalized additive models (GAM) were constructed for each of the 88 land bird species studied in order to estimate the probability of occurrence as a function of (1) climate and (2) climate and land cover variables. Model accuracy was measured by a cross‐validation approach using the area under the curve (AUC) of a receiver operating characteristic (ROC) plot. Results In general, the accuracies of the 88 bird–climate models were good at all studied resolutions. However, the inclusion of land cover increased the performance of 79 and 78 of the 88 bioclimatic models at 10‐km and 20‐km resolutions, respectively. There was no significant improvement at the 40‐km resolution. In contrast to the finer resolutions, the inclusion of land cover variables decreased the modelling accuracy at 80km resolution. Main conclusions Our results suggest that the determinants of bird species distributions are hierarchically structured: climatic variables are large‐scale determinants, followed by land cover at finer resolutions. The majority of the land bird species in Finland are rather clearly correlated with climate, and bioclimate envelope models can provide useful tools for identifying the relationships between these species and the environment at resolutions ranging from 10 km to 80 km. However, the notable contribution of land cover to the accuracy of bioclimatic models at 10–20‐km resolutions indicates that the integration of climate and land cover information can improve our understanding and model predictions of biogeographical patterns under global change.     

Topographic spatial characterisation of grey seal Halichoerus grypus breeding habitat at a sub-seal size spatial grain

Expansion within breeding colonies may critically depend upon the availability of suitable breeding habitat. Here we use topographic modelling in a GIS to characterise suitable pupping habitat and accurately predict the pattern of colonisation in an expanding grey seal breeding colony – the Isle of May (Scotland). We use high resolution images from large format aerial photographs of the colony to generate sub‐metre accurate Digital Terrain Models (DTMs). GIS modelling with these DTMs provides topographic measures of elevation, slope and ease of access to sea and freshwater pools at a 2 m grid cell size. Seal locations during the 1994 breeding season, with sex‐age class, were also digitised from the same images. We examine how the physical attributes of cells (locations) with and without pups differ and identify areas suitable for pupping but remaining unoccupied during 1994. We predict patterns of future colonisation by characterising areas differentiated by the densities of pups within 5 m grid cells and identifying areas, both occupied or unoccupied, with a potential for increased future pupping densities. Our predictions were tested by examining pup distributions observed in the 1998 breeding season. Occupied sites were significantly closer to freshwater pools and access to the sea (p<0.001) than unoccupied sites suggesting that proximity to water may restrict colony expansion before all areas of suitably flat terrain are occupied. All pup density classes occurred in sites with similar slope values and distance to pools. However, higher pupping densities occurred closer to access points (p=0.014). Pup densities observed in 1998 revealed that our 1994 predictions were accurate (p<0.0001). Only 12% of 466 grid cells had higher densities in 1998 than predicted, of which 88% differed by only 1 pup. These incorrectly classified cells occurred at the expanding edge of the colony (in a more topographically homogenous area) and at the main access points from the sea (major traffic zones). These results demonstrate the value of the accurate quantification of topographic parameters at the appropriate spatial grain (in this case below the size of the individual) for use in habitat classification and predictions of habitat utilisation.     

上海市城市土地利用景观的空间尺度效应

在RS与GIS技术支持下,基于2002年上海市5m分辨率的SPOT遥感影像和上海城市土地利用的景观类型,定量分析了几种常用景观格局指数随尺度的变化规律．结果表明：上海市土地利用景观格局指数对粒度和幅度变化都很敏感,景观格局具有明显的尺度依赖性,不同指数对尺度的响应特征不同;40m分辨率是上海城市土地利用景观的本征观测尺度,小于这个尺度范围往往表现出随机性特征;24km的幅度是一个特征操作尺度,与上海市建成区与非建成区边界的范围相吻合,说明对于上海城市景观而言,距离城市中心12km的幅度可能是一个本征的操作尺度;上海市城市结构的复杂性和城市空间扩展的不对称性,说明上海城市景观格局的本征操作尺度并不是一个规则的形状,24km的正方形范围仅是一个近似的操作尺度．     

基于植被/温度特征的黄土高原地表水分季节变化

地表水分是监测土地退化的一个重要指标,是气候、水文、生态、农业等领域的主要参数。选择陕北黄土高原地区作为研究区域,首先采用基于植被覆盖特征的植被状态指数（Vegetation Condition Index,简称VCI）和基于地表温度特征（Land Surface Temperature,简称Ts）的温度状态指数（Temperature Condition Index,简称TCI）分别评价了区域地表水分状况的季节变化。在此基础上分析了植被指数与地表温度特征线性关系的季节变化规律,计算了基于两者经验关系的地表干燥度指数（Temperature Vegetation Dryness Index,TVDI）。该指数对Ts／NDVI特征空间的生态特征的解释,对土壤和作物的水分含量具有综合的指示意义。文中利用该指数综合评价了研究区域地表水分状况的时空分布差异,进一步对VCI、TCI与TVDI相关关系的季节变化进行比较分析,并结合气候因子进行了相关验证,从而对不同指数的应用范围做出判定。研究结果表明,单独采用TCI或VCI表征地表水分会受到明显的季节影响,而TVDI能在不同季节综合体现植被覆盖和地表温度特征对地表水分的影响,从而能较好的反映区域地表水分状况的时空变化特征。各区域的TVDI值季节分布上皆为4—7月份高于10-翌年1月份,但各区TVDI值的季节变化则存在显著不同,而各流域内部TVDI值的空间变异性也存在季节差异,其中在10月份较为显著。     

Mapping soil organic matter in low-relief areas based on land surface diurnal temperature difference and a vegetation index

Accurate estimates of the spatial variability of soil organic matter (SOM) are necessary to properly evaluate soil fertility and soil carbon sequestration potential. In plains and gently undulating terrains, soil spatial variability is not closely related to relief, and thus digital soil mapping (DSM) methods based on soil–landscape relationships often fail in these areas. Therefore, different predictors are needed for DSM in the plains. Time-series remotely sensed data, including thermal imagery and vegetation indices provide possibilities for mapping SOM in such areas. Two low-relief agricultural areas (Peixian County, 28 km × 28 km and Jiangyan County, 38 km × 50 km) in northwest and middle Jiangsu Province, east China, were chosen as case study areas. Land surface diurnal temperature difference (DTD) extracted from moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST), and soil-adjusted vegetation index (SAVI) at the peak of growing season calculated from Landsat ETM+ image were used as predictors. Regression kriging (RK) with a mixed linear model fitted by residual maximum likelihood (REML) and residuals interpolated by simple kriging (SK) were used to model and map SOM spatial distribution; ordinary kriging (OK) was used as a baseline comparison. The root mean squared error, mean error and mean absolute error calculated from leave-one-out cross-validation were used to assess prediction accuracy. Results showed that the proposed covariates provided added value to the observations. SAVI aggregated to MODIS resolution was able to identify local highs and lows not apparent from the DTD imagery alone. Despite the apparent similarity of the two areas, the spatial structure of residuals from the linear mixed models were quite different; ranges on the order of 3 km in Jiangyan but 16 km in Peixian, and accuracy of best models differed by a factor of two (3.3 g/kg and 6.3 g/kg SOM, respectively). This suggests that time-series remotely sensed data can provide useful auxiliary variable for mapping SOM in low-relief agricultural areas, with three important cautions: (1) image dates must be carefully chosen; (2) vegetation indices should supplement diurnal temperature differences, (3) model structure must be calibrated for each area.     

Monitoring temporal dynamics of Great Artesian Basin wetland vegetation,Australia, using MODIS NDVI

The Great Artesian Basin springs (Australia) are unique groundwater dependent wetland ecosystems of great significance, but are endangered by anthropogenic water extraction from the underlying aquifers. Relationships have been established between the wetland area associated with individual springs and their discharge, providing a potential means of monitoring groundwater flow using measurements of vegetated wetland area. Previous attempts to use this relationship to monitor GAB springs have used aerial photography or high resolution satellite images and gave sporadic temporal information. These “snapshot” studies need to be placed within a longer and more regular context to better assess changes in response to aquifer draw-downs. In this study we test the potential of 8 years of Moderate Resolution Imaging Spectroradiometer Normalised Difference Vegetation Index data as a long-term tracer of the temporal dynamics of wetland vegetation at the Dalhousie Springs Complex of the Great Artesian Basin. NDVI time series were extracted from MODIS images and phenologies of the main wetland vegetation species defined. Photosynthetic activity within wetlands could be discriminated from surrounding land responses in this medium resolution imagery. The study showed good correlation between wetland vegetated area and groundwater flow over the 2002–2010 period, but also the important influence of natural species phenologies, rainfall, and anthropogenic activity on the observed seasonal and inter-annual vegetation dynamics. Declining trends in the extent (km²) of vegetated wetland areas were observed between 2002 and 2009 followed by a return of wetland vegetation since 2010. This study underlines the need to continue long-term medium resolution satellite studies of the GAB to fully understand variability and trends in the spring-fed wetlands. The MODIS record allows a good understanding of variability within the wetlands, and gives a high temporal-frequency context for less frequent higher spatial resolution studies, therefore providing a strong baseline for assessment of future changes.     

Constraining rooting depths in tropical rainforests using satellite data and ecosystem modeling for accurate simulation of gross primary production seasonality

Accurate parameterization of rooting depth is difficult but important for capturing the spatio-temporal dynamics of carbon, water and energy cycles in tropical forests. In this study, we adopted a new approach to constrain rooting depth in terrestrial ecosystem models over the Amazon using satellite data [moderate resolution imaging spectroradiometer (MODIS) enhanced vegetation index (EVI)] and Biome-BGC terrestrial ecosystem model. We simulated seasonal variations in gross primary production (GPP) using different rooting depths (1, 3, 5, and 10 m) at point and spatial scales to investigate how rooting depth affects modeled seasonal GPP variations and to determine which rooting depth simulates GPP consistent with satellite-based observations. First, we confirmed that rooting depth strongly controls modeled GPP seasonal variations and that only deep rooting systems can successfully track flux-based GPP seasonality at the Tapajos km67 flux site. Second, spatial analysis showed that the model can reproduce the seasonal variations in satellite-based EVI seasonality, however, with required rooting depths strongly dependent on precipitation and the dry season length. For example, a shallow rooting depth (1–3 m) is sufficient in regions with a short dry season (e.g. 0–2 months), and deeper roots are required in regions with a longer dry season (e.g. 3–5 and 5–10 m for the regions with 3–4 and 5–6 months dry season, respectively). Our analysis suggests that setting of proper rooting depths is important to simulating GPP seasonality in tropical forests, and the use of satellite data can help to constrain the spatial variability of rooting depth.     

A method to derive vegetation distribution maps for pollen dispersion models using birch as an example

Detailed knowledge of the spatial distribution of sources is a crucial prerequisite for the application of pollen dispersion models such as, for example, COSMO-ART (COnsortium for Small-scale MOdeling-Aerosols and Reactive Trace gases). However, this input is not available for the allergy-relevant species such as hazel, alder, birch, grass or ragweed. Hence, plant distribution datasets need to be derived from suitable sources. We present an approach to produce such a dataset from existing sources using birch as an example. The basic idea is to construct a birch dataset using a region with good data coverage for calibration and then to extrapolate this relationship to a larger area by using land use classes. We use the Swiss forest inventory (1 km resolution) in combination with a 74-category land use dataset that covers the non-forested areas of Switzerland as well (resolution 100 m). Then we assign birch density categories of 0%, 0.1%, 0.5% and 2.5% to each of the 74 land use categories. The combination of this derived dataset with the birch distribution from the forest inventory yields a fairly accurate birch distribution encompassing entire Switzerland. The land use categories of the Global Land Cover 2000 (GLC2000; Global Land Cover 2000 database, 2003, European Commission, Joint Research Centre; resolution 1 km) are then calibrated with the Swiss dataset in order to derive a Europe-wide birch distribution dataset and aggregated onto the 7 km COSMO-ART grid. This procedure thus assumes that a certain GLC2000 land use category has the same birch density wherever it may occur in Europe. In order to reduce the strict application of this crucial assumption, the birch density distribution as obtained from the previous steps is weighted using the mean Seasonal Pollen Index (SPI; yearly sums of daily pollen concentrations). For future improvement, region-specific birch densities for the GLC2000 categories could be integrated into the mapping procedure.     

发展NECT土地覆盖特征数据集的原理、方法和应用

着重探讨了建立中国东北样带 (NortheastChinatransect, NECT) 土地覆盖特征数据集的原理、方法及其在全球变化研究方面的重要应用。NECT土地覆盖特征数据集是以多时相的 1km分辨率的NOAA/AVHRR归一化植被指数NDVI (Normalizeddifferencevegetationindex) 数字影像为基础, 同时采用高程、气候、土壤、植被、土地利用、土地资源、生态区域、行政边界、经济、社会等多源数据作为数据源, 并经过标准化处理 (如数字化、空间插值、几何配准、投影转换 ) 集成而成。在土地覆盖特征数据集的主要应用方面, 如 :1) 利用多时相、1km分辨率的NOAA/AVHRR影像完成了中国东北样带土地覆盖分类图。一级分类系统包括森林、草原、荒漠和沙地、灌丛、农田、混合覆盖 类型、城镇和水体等 8类, 二级分类体系包括 12类。经过地面采样进行精度检验, 分类精度达到 81.6 1%。 2 ) 对主要植被类型的植物生长季变化进行的研究。利用多时相的遥感影像构造了能够反映植被年际、季节生长变化的遥感植被指数ND VImax、NDVI变幅xam以及NDVI的标准偏差x′s 等, 分析这 3个参数 1983~ 1999年的 17年中的变化情况。该数据集的建立是研究该样带土地覆盖特征及其变化规律的基础, 对基于样带的全球变化研究有重要的意义。     

The spatial distribution of cereal bioenergy potential in China

Biomass energy that exists in crop residues can be used for electricity generation and fuel production. However, its spatial distribution has formed a bottleneck in its utilization. This study introduces a data fusion method that uses the Net Primary Productivity (NPP) product of the Moderate‐resolution Imaging Spectroradiometer (MODIS) data as a weighting factor to downscale crop statistics from a county scale to a 1 km² spatial resolution using GIS to accurately map the spatial distribution of cereal bioenergy potential in China. The study demonstrates that the combination of remote sensing and statistical methods improves both spatial resolution and accuracy of the results, and resolves errors and uncertainties stemming from remote sensing processes. The results of the study will allow better decision making for siting biomass power plants, which will in turn reduce the cost of transportation of materials and increase the use of bioenergy.     

陕北黄土高原景观破碎化的时空动态研究

景观破碎化受到自然环境背景及社会经济活动的影响,并限制区域发展.为揭示景观破碎化的时空变化特征,整合了MODIS(中分辨率成像光谱仪)影像逐旬数据与多时段景观类型数据,并对其进行了分析.比较1987～2002年景观破碎化的年际差异表明,陕北黄土高原的景观破碎化过程较缓慢,但具有区域差异,且与景观类型组成息息相关.结果表明,林地的破碎化水平变化显著,而农、草地的破碎化水平变化相对缓慢.年际变化分析有助于说明区域景观破碎化的趋势,而年内季节变化则能帮助认识景观破碎化过程的周期规律.     

Spatiotemporal variability of grassland vegetation cover in a catchment in Inner Mongolia, China, derived from MODIS data products

Conditions, distribution and development of vegetation in semi arid regions are highly variable. In this study we detected the temporal and spatial variability of vegetation in the Xilin river catchment from 2000 to 2008 by analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) data products of that period. The study is based on LAI (Leaf area index) and supported by NDVI (Normalized difference vegetation index) and LST (Land surface temperature) data with a spatial resolution of 1 km. The mean LAI of the catchment from 2000 to 2008 is 0.59. Precipitation data of the study period governs the conditions and distribution of vegetation in the catchment. In dry years, e.g. 2001 and 2005, LAI was clearly lower (0.52) compared to 2003 (LAI?=?0.72) which was a wet year. As precipitation generally decreases from south-east to north-west, LAI values decrease according to this gradient. The influence of heavy grazing in the vicinity of the Xilin river is obvious as LAI is low (0.4) in these areas. The high temporal variability of the LAI is displayed by its high mean CV (coefficient of variation) which is 48% for the observed years. The analysis of sample areas illustrates temporal and spatial differences in vegetation development within the catchment and shows a generally delayed growth start in the north-west of the catchment.     

Development and implementation of a spatial unit non-overlapping water stress index for water scarcity evaluation with a moderate spatial resolution

Water scarcity is a serious global problem, and accurate estimations are urgently needed. The Water stress index (WSI) is one of the most commonly used methods for global or large scale water scarcity evaluation, but this method lacks of consideration of water demand and water supply positions non-overlapped spatial distribution, tends to overestimate water stress when applied to a moderate resolution grid (e.g., 1 km). In this study, we used a non-overlapping water supply-and-demand unit approach to improve the calculation scheme and constructed a spatial unit non-overlapping WSI model (Sun-WSI model). We then applied the new model to the Yarlung Tsangpo-Brahmaputra River (TBR) and estimated monthly water stress from 2006 to 2012 with a 1 km spatial resolution. The results showed that the determination coefficient (R²) between the normalized Drought Index (DI) and water stress was mainly in the range of 0.2–0.7, accounting for 77.4% of the study area. The spatial pattern of water stress estimated by the Sun-WSI model was consistent with the DI. Further analysis showed that both overall and grid water stress estimated by the Sun-WSI model were close to the results from existing studies; however the Sun-WSI model had a higher spatial resolution. With a 1 km resolution, the Sun-WSI model performed better than the conventional WSI with respect to both overall results and spatial details. This suggests that the Sun-WSI model is suitable for evaluating regional or moderate-resolution grid water scarcity.     

Differentiating geological fertility derived vegetation zones in Kruger National Park,South Africa,using Landsat and MODIS imagery

Spatial technologies present possibilities for producing frequently updated and accurate habitat maps, which are important in biodiversity conservation. Assemblages of vegetation are equivalent to habitats. This study examined the use of satellite imagery in vegetation differentiation in South Africa's Kruger National Park (KNP). A vegetation classification scheme based on dominant tree species but also related to the park's geology was tested, the geology generally consisting of high and low fertility lithology. Currently available multispectral satellite imagery is broadly either of high spatial but low temporal resolution or low spatial but high temporal resolution. Landsat TM/ETM+ and MODIS images were used to represent these broad categories. Rain season dates were selected as the period when discrimination between key habitats in KNP is most likely to be successful. Principal Component Analysis enhanced vegetated areas on the Landsat images, while NDVI vegetation enhancement was employed on the MODIS image. The images were classified into six field sampling derived classes depicting a vegetation density and phenology gradient, with high (about 89%) indicative classification accuracy. The results indicate that, using image processing procedures that enhance vegetation density, image classification can be used to map the park's vegetation at the high versus low geological fertility zone level, to accuracies above 80% on high spatial resolution imagery and slightly lower accuracy on lower spatial resolution imagery. Rainfall just prior to the image date influences herbaceous vegetation and, therefore, success at image scene vegetation mapping, while cloud cover limits image availability. Small scale habitat differentiation using multispectral satellite imagery for large protected savanna areas appears feasible, indicating the potential for use of remote sensing in savanna habitat monitoring. However, factors affecting successful habitat mapping need to be considered. Therefore, adoption of remote sensing in vegetation mapping and monitoring for large protected savanna areas merits consideration by conservation agencies.