Development of a satellite-based hazard rating system for Dendroctonus frontallis (Coleoptera: Scolytidae) in the Ouachita Mountains of Arkansas

The southern pine beetle, Dendroctonus frontalis Zimmermann (Coleoptera: Scolytidae), is the most damaging forest insect pest of pines (Pinus spp.) throughout the southeastern United States. Hazard rating schemes have been developed for D. frontalis, but for these schemes to be accurate and effective, they require extensive on-site measurements of stand attributes such as host density, age, and basal area. We developed a stand hazard-rating scheme for several watersheds in the Ouachita Highlands of Arkansas based upon remotely sensed data and a geographic information system. A hazard model was developed using stand attributes (tree species, stand age and density, pine basal area, and landform information) and was used to establish baseline hazard maps for the watersheds. Landsat 7 ETM+ data were used for developing new hazard maps. Two dates of Landsat imagery were used in the analyses (August 1999 and October 1999). The highest correlations between hazard rating scores and remotely sensed variables from either of the dates included individual Landsat 7 ETM+ bands in the near- and mid-infrared regions as well as variables derived from various bands (i.e., Tasseled cap parameters, principal component parameters, and vegetation indices such as the calculated simple ratio and normalized difference vegetation index). Best subset regression analyses produced models to predict stand hazard to southern pine beetle that consisted of similar variables that resembled but were more detailed than maps produced using inverse distance weighted techniques. Although the models are specific for the study area, with modifications, they should be transferable to geographically similar areas.     

Effects of Season and Successional Stage on Leaf Area Index and Spectral Vegetation Indices in Three Mesoamerican Tropical Dry Forests1

We compared plant area index (PAI) and canopy openness for different successional stages in three tropical dry forest sites: Chamela, Mexico; Santa Rosa, Costa Rica; and Palo Verde, Costa Rica, in the wet and dry seasons. We also compared leaf area index (LAI) for the Costa Rican sites during the wet and dry seasons. In addition, we examined differences in canopy structure to ascertain the most influential factors on PAI/LAI. Subsequently, we explored relationships between spectral vegetation indices derived from Landsat 7 ETM+ satellite imagery and PAI/LAI to create maps of PAI/LAI for the wet season for the three sites. Specific forest structure characteristics with the greatest influence on PAI/LAI varied among the sites and were linked to climatic differences. The differences in PAI/LAI and canopy openness among the sites were explained by both the past land‐use history and forest management practices. For all sites, the best‐fit regression model between the spectral vegetation indices and PAI/LAI was a Lorentzian Cumulative Function. Overall, this study aimed to further research linkages between PAI/LAI and remotely sensed data while exploring unique challenges posed by this ecosystem.     

Object-oriented methods for habitat mapping at multiple scales – Case studies from Northern Germany and Wye Downs, UK

This paper presents an application of object-oriented techniques for habitat classification based on remotely sensed images and ancillary data. The study reports the results of habitat mapping at multiple scales using Earth Observation (EO) data at various spatial resolutions and multi temporal acquisition dates. We investigate the role of object texture and context in classification as well as the value of integrating knowledge from ancillary data sources. Habitat maps were produced at regional and local scales in two case studies; Schleswig-Holstein, Germany and Wye Downs, United Kingdom. At the regional scale, the main task was the development of a consistent object-oriented classification scheme that is transferable to satellite images for other years. This is demonstrated for a time series of Landsat TM/ETM+ scenes. At the local scale, investigations focus on the development of appropriate object-oriented rule networks for the detailed mapping of habitats, e.g. dry grasslands and wetlands using very high resolution satellite and airborne scanner images. The results are evaluated using statistical accuracy assessment and visual comparison with traditional field-based habitat maps. Whereas the application of traditional pixel-based classification result in a pixelised (salt and pepper) representation of land cover, the object-based classification technique result in solid habitat objects allowing easy integration into a vector-GIS for further analysis. The level of detail obtained at the local scale is comparable to that achieved by visual interpretation of aerial photographs or field-based mapping and also retains spatially explicit, fine scale information such as scrub encroachment or ecotone patterns within habitats.     

Secondary Forest Detection in a Neotropical Dry Forest Landscape Using Landsat 7 ETM+ and IKONOS Imagery1

We integrate forest structure and remotely sensed data for four successional stages (pasture, early, intermediate, and late) of a tropical dry forest area located in the Sector Santa Rosa of the Guanacaste Conservation Area in northwestern Costa Rica. We used a combination of spectral vegetation indices derived from Landsat 7 ETM+ medium resolution and IKONOS high‐resolution imagery. The indices (using the red and near‐infrared bands) simple ratio and normalized difference vegetation index separated the successional stages well. Two other indices using mid‐infrared bands did not separate successional stages as well. In a comparison of the successional stages with chronological age, there was no separability in the spectral reflectance among different age classes. Successional stages, in contrast, showed distinct groups with minimal overlap. We also applied a simple validation in another dry forest located in the Palo Verde National Park in the province of Guanacaste, Costa Rica, with reasonably good results.     

Using satellite imagery to assess plant species richness: The role of multispectral systems

Question: The use of variations in the spectral responses of remotely sensed images was recently proposed as an indicator of plant species richness (Spectral Variation Hypothesis, SVH). In this paper we addressed the issue of the potential use of multispectral sensors by testing the hypothesis that only some of the bands recorded in a remotely sensed image contain information related to the variation in species richness. Location: Montepulciano Lake, central Italy. Methods: We assessed how data compression techniques, such as Principal Component Analysis (PCA), influence the relationship between spectral heterogeneity and species richness and evaluated which spectral interval is the most adequate for predicting species richness by means of linear regression analysis. Results: The original multispectral data set and the first two non-standardized principal components can both be used as predictors of plant species richness (R²∼ 0.48; p < 0.001), confirming that PCA is an effective tool for compressing multi-spectral data without loss of information. Using single spectral bands, the near infrared band explained 41% of variance in species richness (p < 0.01), while the visible wavelengths had much lower prediction powers. Conclusions: The potential of satellite data for estimating species richness is likely to be due to the near infrared bands, rather than to the visible bands, which share highly redundant information. Since optimal band selection for image processing is a crucial task and it will assume increasing importance with the growing availability of hyperspectral data, in this paper we suggest a ‘near infrared way’for assessing species richness directly from remotely sensed data.     

多尺度遥感综合监测我国北方典型草原区植被盖度

利用多尺度遥感影像综合进行全球和区域尺度的土地利用/覆盖变化(LUCC)研究是最近全球变化研究的重要方向之一。本文综合利用野外群落样方、数字相机、ETM+影像、NOAA/AVHRR影像,在遥感、GIS和GPS支持下,对我国北方典型草原区植被盖度进行了综合监测、模拟与分析。结果表明:(1) 利用经处理后的数字相机影像测量盖度的结果准确性较高,可以作为植被盖度测量的标准结果,反映真实的覆盖特征,并用以验证利用其它方法测量结果的精度。(2) 利用野外1 m2样方网格法目视估测的植被盖度结果变化较大,不稳定。本次实验中,与数字相机测量结果相比,样方估测的盖度普遍偏高,平均偏差为9.92%;但两者相关性较好(r2=0.89)。(3) 采用Gutman模型ETM+影像、NOAA/AVHRR影像反演植被盖度的结果与数字相机测量结果偏差分别为7.03%、7.83%,ETM+像元分解NOAA像元后得到的植被盖度与数字相机测量结果偏差5.68%。三者与数字相机测量结果的相关系数r2分别为0.78、0.61和0.76。(4)利用野外实测植被盖度数据直接与NOAA-NDVI影像建立统计模型估算植被盖度的精度较低(r2=0.65),而通过空间分辨率介于两者之间的ETM+影像进行转换后,该精度得到一定的提高(r2=0.80)。利用像元分解的方法提高了大尺度植被盖度监测的精度,是利用遥感数据进行尺     

Predicting krill swarm characteristics important for marine predators foraging off East Antarctica

Open ocean predator‐prey interactions are often difficult to interpret because of a lack of information on prey fields at scales relevant to predator behaviour. Hence, there is strong interest in identifying the biological and physical factors influencing the distribution and abundance of prey species, which may be of broad predictive use for conservation planning and evaluating effects of environmental change. This study focuses on a key Southern Ocean prey species, Antarctic krill Euphausia superba, using acoustic observations of individual swarms (aggregations) from a large‐scale survey off East Antarctica. We developed two sets of statistical models describing swarm characteristics, one set using underway survey data for the explanatory variables, and the other using their satellite remotely sensed analogues. While survey data are in situ and contemporaneous with the swarm data, remotely sensed data are all that is available for prediction and inference about prey distribution in other areas or at other times. The fitted models showed that the primary biophysical influences on krill swarm characteristics included daylight (solar elevation/radiation) and proximity to the Antarctic continental slope, but there were also complex relationships with current velocities and gradients. Overall model performance was similar regardless of whether underway or remotely sensed predictors were used. We applied the latter models to generate regional‐scale spatial predictions using a 10‐yr remotely‐sensed time series. This retrospective modelling identified areas off east Antarctica where relatively dense krill swarms were consistently predicted during austral mid‐summers, which may underpin key foraging areas for marine predators. Spatiotemporal predictions along Antarctic predator satellite tracks, from independent studies, illustrate the potential for uptake into further quantitative modelling of predator movements and foraging. The approach is widely applicable to other krill‐dependent ecosystems, and our findings are relevant to similar efforts examining biophysical linkages elsewhere in the Southern Ocean and beyond.     

采用广义加法模型整合数字高程模型和遥感数据进行植被分布预测

为了采用广义加法模型整合数字高程模型和遥感数据进行植被分布的预测, 并探索耦合环境变量和遥感数据作为预测变量是否能够有效地提高植被分布预测的精度, 选择海拔、坡度、至黄河最近距离、至海岸线最近距离, 以及从SPOT5遥感影像中提取的光谱变量作为预测变量, 采用广义加法模型整合环境变量和光谱变量, 建立植被分布预测模型。研究设置3种建模情景(以环境变量作为预测变量, 以光谱变量作为预测变量, 综合使用环境变量与光谱变量作为预测变量)对黄河三角洲的优势植被类型的分布进行了预测, 并对预测结果采用偏差分析、受试者工作特征曲线和野外采样点对比等3种方法进行了验证。结果表明: (1)基于广义加法模型的植被分布预测方法具有一定的实用性, 可以较为准确地预测植被的分布; 盖度较高的植被类型预测精度较高, 盖度较低的植被类型预测精度较低, 植物群落结构的特点是出现这些差异的主要原因; 综合使用环境变量和光谱变量作为预测变量的模型, 预测精度高于单独以环境变量或者光谱变量作为预测变量的模型。(2)环境变量、光谱变量大多被选入模型, 二者均对植被分布预测有重要的作用; 同一预测变量在不同植被类型的预测模型中的贡献不同, 这与植被的光谱、环境特征差异有关; 同一预测变量在不同的建模情景下对模型的贡献不同, 环境变量与光谱变量的耦合效应可能是导致预测变量对模型的贡献出现变化的原因。     

Monitoring toxic cyanobacteria Lyngbya majuscula (Gomont) in Moreton Bay, Australia by integrating satellite image data and field mapping

Large-scale blooms of Lyngbya majuscula (Gomont) have occurred throughout Moreton Bay (south-east Queensland) and have been documented since 1997. L. majuscula is a toxic cyanobacteria which fixes nitrogen and is found attached to: seagrass, algae and coral. The toxic and smothering nature of L. majuscula has affected human and environmental health in sensitive coastal ecosystems. To reduce these impacts, monitoring is an essential component of studying the origins and development of L. majuscula blooms. An accurate and cost effective means to map the extent of a bloom and its biophysical properties is needed. This study presents an operational approach for mapping the extent of L. majuscula blooms in the clear and shallow water regions of Moreton Bay, eastern Australia, from a combination of field and remotely sensed data sets. The ability to discriminate L. majuscula from other substrate types over a range of depths was first examined using detailed field reflectance spectra, measured optical properties of Moreton Bay waters and a radiative transfer model (Hydrolight 4.1). A two-stage process was then used to map L. majuscula. The spatial extent of L. majuscula and other major substrate types was first recorded from a boat-based survey by marine park authorities using point-based GPS measurements. This sampling was timed to coincide with an overpass of the Landsat 7 ETM+ sensor. When the results of the boat-based mapping detected more than 25% L. majuscula cover in the study area, a cloud free Landsat 7 ETM+ image was acquired for that date. In the second stage of mapping, selected field survey data provided the basis for a supervised classification of the ETM+ image data to map L. majuscula. Effort and accuracy assessment of both field and image mapping methods indicated a trade-off between areal coverage and mapping accuracy. The Landsat 7 ETM+ based mapping procedure provided 100% areal coverage with 58% accuracy. In contrast, the boat-based field survey method covered only 0.5% of the study area, but with almost 100% mapping accuracy. The approach outlined in this work has been adopted as a standard operating procedure in Moreton Bay. This study illustrates how remote sensing can be combined with field monitoring, to provide marine park authorities with useful information to understand and manage blooms.     

Leakiness: A new index for monitoring the health of arid and semiarid landscapes using remotely sensed vegetation cover and elevation data

The health of arid and semiarid lands needs to be monitored, particularly if they are used to produce food and fiber, and are prone to loss of vegetation cover and soil. Indicators of landscape health based on remotely sensed data could cost-effectively integrate structural and functional attributes of land surfaces across a range of scales. In this paper, we describe a new index for remotely monitoring changes in the health of land. The new index takes important aspects of landscape structure and function into account by focusing on the potential for landscapes to lose or ‘leak’ (not retain) soil sediments. We combined remotely sensed vegetation patchiness data with digital elevation model (DEM) data to derive a quantitative metric, the landscape leakiness index, LI. This index is strongly linked to landscape function by algorithms that reflect the way in which spatial configuration of vegetation cover and terrain affect soil loss. Linking LI to landscape function is an improvement on existing indicators that are based on qualitatively assessing remotely sensed changes in vegetation cover. Using archived Landsat imagery and Shuttle Radar Topography Mission DEMs, we found for example that LI indicated improvements in the condition or health of a rangeland paddock that was monitored from 1980 to 2002. This paddock is located in central Australia and its improved health is documented by photographs and field data. Although the full applicability of LI remains to be explored, we have demonstrated that it has the potential to serve as a useful ecological indicator for monitoring the health of arid and semiarid landscapes.     

Using remote sensing to model tree species distribution in Peruvian lowland Amazonia

Species distribution models for Amazonian trees have mostly been produced at scales and resolutions that are too broad and coarse for practical use in either conservation or forestry. On the other hand, several studies have shown that elevation and the medium‐resolution remote sensing data available via Landsat imagery can be successfully used to detect differences in plant species composition in Amazonia. Therefore, it seems likely that the same data can also be used to predict geographical distributions of individual taxa. Here we use remotely sensed data and a maximum entropy algorithm (MaxEnt) to generate landscape‐scale distribution models at 30‐m‐resolution for five economically important timber tree genera (Apuleia, Amburana, Crepidospermum, Dipteryx, and Manilkara). Individual Landsat Thematic Mapper bands and normalized difference vegetation index yielded acceptable model performance, and the use of averaging filters (3 × 3 and 5 × 5 pixel low‐pass filters) improved model performance further. Including elevation as a predictor also improved model performance for all the genera. Our results suggest that it is possible to use Landsat bands and elevation as predictors for modeling the potential distribution of tree species in lowland Amazonia at a fine enough resolution to facilitate the practical management of forest resources.     

多尺度遥感综合监测我国北方典型草原区植被盖度

利用多尺度遥感影像综合进行全球和区域尺度的土地利用/覆盖变化(LUCC)研究是最近全球变化研究的重要方向之一.本文综合利用野外群落样方、数字相机、ETM+影像、NOAA/AVHRR影像,在遥感、GIS和GPS支持下,对我国北方典型草原区植被盖度进行了综合监测、模拟与分析.结果表明:(1)利用经处理后的数字相机影像测量盖度的结果准确性较高,可以作为植被盖度测量的标准结果,反映真实的覆盖特征,并用以验证利用其它方法测量结果的精度.(2)利用野外1 m2样方网格法目视估测的植被盖度结果变化较大,不稳定.本次实验中,与数字相机测量结果相比,样方估测的盖度普遍偏高,平均偏差为9.92%;但两者相关性较好(r2=0.89).(3)采用Gutman模型ETM+影像、NOAA/AVHRR影像反演植被盖度的结果与数字相机测量结果偏差分别为7.03%、7.83%,ETM+像元分解NOAA像元后得到的植被盖度与数字相机测量结果偏差5.68%.三者与数字相机测量结果的相关系数r2分别为0.78、0.6l和0.76.(4)利用野外实测植被盖度数据直接与NOAA-NDVI影像建立统计模型估算植被盖度的精度较低(r2=0.65),而通过空间分辨率介于两者之间的ETM+影像进行转换后,该精度得到一定的提高(r2=0.80).利用像元分解的方法提高了大尺度植被盖度监测的精度,是利用遥感数据进行尺度转换研究的重要实践.多尺度遥感影像的综合对地观测对大区域上反演植被盖度有很好的促进作用.     

Integrating vegetation field surveys with remotely sensed data

Summary   This paper explores data compatibility issues arising from the assessment of remnant native vegetation condition using satellite remote sensing and field-based data. Space-borne passive remote sensing is increasingly used as a way of providing a total sample and synoptic overview of the spectral and spatial characteristics of native vegetation canopies at a regional scale. However, integrating field-collected data often not designed for integration with remotely sensed data can lead to data compatibility issues. Subsequent problems associated with the integration of unsuited datasets can contribute to data uncertainty and result in inconclusive findings. It is these types of problems (and potential solutions) that form the basis of this paper. In other words, how can field surveys be designed to support and improve compatibility with remotely sensed total surveys? Key criteria were identified for consideration when designing field-based surveys of native vegetation condition (and other similar applications) with the intent to incorporate remotely sensed data. The criteria include recommendations for the siting of plots, the need for reference location plots, the number of sample sites and plot size and distribution, within a study area. The difficulties associated with successfully integrating these data are illustrated using real examples taken from a study of the vegetation in the Little River Catchment, New South Wales, Australia.     

Diversity, biomass and distribution pattern of Sargassum beds in the South West lagoon of New Caledonia (South Pacific)

Sargassum (Phaeophyceae, Fucales) is a genus of worldwide distribution recently recognised to proliferate in several regions of the South Pacific. In New Caledonia, species of this genus naturally structure one of the major lagoon habitats but their extent, composition and biomass remain largely unknown. To fill these gaps in our knowledge over large areas, we applied a combination of remote sensing and in situ methods applied to the Neo Caledonian South West Lagoon. Space borne high resolution Landsat (30-m resolution) and Quickbird (2.4-m resolution) images of the Neo Caledonian South West lagoon were used to estimate the spatial extent of the different beds of interest. Species composition was determined for 11 Sargassum beds and seasonal variations were investigated for four representative beds over an 18 month period using quadrats and transects. Relationships between surface cover and biomass were estimated from seasonal field data sets. Finally, four methods requiring variable levels of sampling effort were designed to estimate the total biomass at the scale of each bed, considering (or not) the specific composition, and spatial and temporal variations. Seven Sargassaceae species were identified. Mean surface cover (24.4–51.6%) and total biomass [3.4–1,461.9 t dry matter (DM)] varied widely between beds. Overall, biomass temporal variations were not significantly different, but species-level variations seemed to be bed-specific. The extent of the 11 beds was 9 km²; their total biomass was estimated and compared using each of the four methods, and the most precise method provided an estimate of 2,900 t DM. This study demonstrates a way of characterising Sargassum beds, efficiently and on a large scale, using a combination of remotely sensed and in situ data. These methods should be useful for possible future biomonitoring of Sargassum beds in New Caledonia, and in other areas worldwide.     

Regional scale relationships between ecosystem structure and functioning: the case of the Patagonian steppes

Aims 1. To characterize ecosystem functioning by focusing on above‐ground net primary production (ANPP), and 2. to relate the spatial heterogeneity of both functional and structural attributes of vegetation to environmental factors and landscape structure. We discuss the relationship between vegetation structure and functioning found in Patagonia in terms of the capabilities of remote sensing techniques to monitor and assess desertification. Location Western portion of the Patagonian steppes in Argentina (39°30′ S to 45°27′ S). Methods We used remotely‐sensed data from Landsat TM and AVHRR/NOAA sensors to characterize vegetation structure (physiognomic units) and ecosystem functioning (ANPP and its seasonal and interannual variation). We combined the satellite information with floristic relevés and field estimates of ANPP. We built an empirical relationship between the Landsat TM‐derived normalized difference vegetation index (NDVI) and field ANPP. Using stepwise regressions we explored the relationship between ANPP and both environmental variables (precipitation and temperature surrogates) and structural attributes of the landscape (proportion and diversity of different physiognomic classes (PCs)). Results PCs were quite heterogeneous in floristic terms, probably reflecting degradation processes. Regional estimates of ANPP showed differences of one order of magnitude among physiognomic classes. Fifty percent of the spatial variance in ANPP was accounted for by longitude, reflecting the dependency of ANPP on precipitation. The proportion of prairies and semideserts, latitude and, to a lesser extent, the number of PCs within an 8 × 8 km cell accounted for an additional 33% of the ANPP variability. ANPP spatial heterogeneity (calculated from Landsat TM data) within an 8 × 8 km cell was positively associated with the mean AVHRR/NOAA NDVI and with the diversity of physiognomic classes. Main conclusions Our results suggest that the spatial and temporal patterns of ecosystem functioning described from ANPP result not only from water availability and thermal conditions but also from landscape structure (proportion and diversity of different PCs). The structural classification performed using remotely‐sensed data captured the spatial variability in physiognomy. Such capability will allow the use of spectral classifications to monitor desertification.     

新一代Landsat系列卫星：Landsat8遥感影像新增特征及其生态环境意义

美国Landsat 8卫星的成功发射使一度中断的Landsat对地观测得以继续。Landsat 8除了保持Landsat 7卫星的基本特征外,还在波段的数量、波段的光谱范围和影像的辐射分辨率上进行了改进。基于该卫星的首幅影像,针对这些新的特性进行了分析和研究。研究发现:(1)新增的卷云波段有助于区别点云和高反射地物;(2)卷云波段设计的波长范围位于粘土矿物光谱反射的强吸收带,有利于土壤与建筑不透水面信息的区别;(3)新增的深蓝波段有助于水体悬浮物浓度的监测;(4)全色影像波长范围的收窄有利于该影像上植被和非植被的区别;(5)辐射分辨率的提高可避免极亮/极暗区的灰度过饱和现象,这对反射率极低的水体的细微特征识别有很大帮助。显然,Landsat 8这些新增的优点将会对全球生态环境变化的监测产生积极的作用。     

Historical Maps from Modern Images: Using Remote Sensing to Model and Map Century-Long Vegetation Change in a Fire-Prone Region

Understanding the age structure of vegetation is important for effective land management, especially in fire-prone landscapes where the effects of fire can persist for decades and centuries. In many parts of the world, such information is limited due to an inability to map disturbance histories before the availability of satellite images (~1972). Here, we describe a method for creating a spatial model of the age structure of canopy species that established pre-1972. We built predictive neural network models based on remotely sensed data and ecological field survey data. These models determined the relationship between sites of known fire age and remotely sensed data. The predictive model was applied across a 104,000 km² study region in semi-arid Australia to create a spatial model of vegetation age structure, which is primarily the result of stand-replacing fires which occurred before 1972. An assessment of the predictive capacity of the model using independent validation data showed a significant correlation (r_s = 0.64) between predicted and known age at test sites. Application of the model provides valuable insights into the distribution of vegetation age-classes and fire history in the study region. This is a relatively straightforward method which uses widely available data sources that can be applied in other regions to predict age-class distribution beyond the limits imposed by satellite imagery.     

Modeling above‐ground litterfall in eastern Mediterranean conifer forests using fractional tree cover,and remotely sensed and ground data

Question: How can we model above‐ground litterfall in Mediterranean conifer forests using remotely sensed and ground data, and geographic information systems (GIS)? Location: Eastern Mediterranean conifer forest of Turkey. Methods: Above‐ground litterfall from Mediterranean forest stands of Pinus nigra, Cedrus libani, Pinus brutia and Juniperus excelsa and mixed Abies cilicica, C. libani and P. nigra was modeled as a function of fractional tree cover using a regression tree algorithm, based on IKONOS and Landsat TM/ETM+data. Landsat TM/ETM+images for the study area were used to map actual stand patterns, based on a land‐cover map of species stands using a supervised classification. Results: Total amount of annual above‐ground litterfall for the entire study area (12 260 km²) was estimated at 417.2 Mg ha^?1 for P. brutia, 291.1 Mg ha^?1 for the mixed stand, 115.5 Mg ha^?1 for P. nigra, 54.6 Mg ha^?1 for J. excelsa and 45.9 Mg ha^?1 for C. libani. The maps generated indicate the distribution of the seasonal amount of total above‐ground litterfall for different species and the distribution of species stands in the study area. There was an increase in the amount of above‐ground litterfall for P. brutia stand in summer, for J. excelsa in autumn and for C. libani, P. nigra and the mixed stand of A. cilicica, P. nigra and C. libani in winter. Conclusion: Application of this model helps to improve the accuracy of estimated litterfall input to soil organic carbon pools in the Mediterranean conifer forests.     

Fire monitoring in savanna ecosystems using MODIS data: a case study of Kruger National Park, South Africa

The heterogeneity of savanna ecosystems is guaranteed by disturbance events like fires, droughts, floods and browsing and grazing by herbivores. For conservation areas with limited space to preserve biodiversity, fire monitoring is crucial. Long periods of satellite remotely sensed data provide an alternative solution to estimate the distribution of different vegetation types and fire-affected patches over time. This study focusses on the application of MODIS data to detect, identify and delineate fire-affected areas in Kruger National Park (KNP), South Africa, for the period 2001–2003. Fire scars on KNP’s savanna were identified using threshold and supervised classification methods on moderate-resolution imaging spectroradiometer (MODIS) with 500-m resolution and 32-day global composites using a combination of band 1 (red), 2 (NIR, near infrared), 4 (green) and 6 (SWIR, short wave infrared). On identified fire scars, the spectral indexes of albedo, normalised difference infrared index (NDII) and normalised difference vegetation index (NDVI) were extracted. The following four broad habitat types were used for this analysis: riparian woodland, dense woodland, mixed woodland and open-tree savanna. The values of albedo, NDII and NDVI during the dry season (June to October) for different years are lower on fire-affected patches. Mixed woodland is the largest habitat burned with 21%, 43% and 2% of the KNP area affected by fire in 2001, 2002 and 2003, respectively. Riparian woodland is the least affected by fire. The supervised classification method has a greater accuracy for fire scars detection in KNP savannas during the dry season. We conclude that MODIS data can be used successfully for fire monitoring in savanna ecosystems.     

Monitoring Karenia brevis blooms in the Gulf of Mexico using satellite ocean color imagery and other data

Harmful algal blooms (HABs) of Karenia brevis are a recurrent problem in the Gulf of Mexico, with nearly annual occurrences on the Florida southwest coast, and fewer occurrences on the northwest Florida and Texas coasts. Beginning in 1999, the National Oceanic and Atmospheric Administration has issued the Gulf of Mexico HAB Bulletins to support state monitoring and management efforts. These bulletins involve analysis of satellite imagery with field and meteorological station data. The effort involves several components or models: (a) monitoring the movement of an algal bloom that has previously been identified as a HAB (type 1 forecast); (b) detecting new blooms as HAB or non-HAB (type 2); (c) predicting the movement of an identified HAB (type 3); (d) predicting conditions favorable for a HAB to occur where blooms have not yet been observed (type 4). The types 1 and 2 involve methods of bloom detection requiring routine remote sensing, especially satellite ocean color imagery and in situ data. Prediction (types 3 and 4) builds on the monitoring capability by using interpretative and numerical modeling. Successful forecasts cover more than 1000 km of coast and require routine input of remotely sensed and in situ data.The data sources used in this effort include ocean color imagery from the Sea-Viewing Wide Field-of-View Sensor/OrbView-2 satellite and processed using coastal-specific algorithms, wind data from coastal and offshore buoys, field observations of bloom location and intensity provided by state agencies, and forecasts from the National Weather Service. The HAB Bulletins began in coordination with the state of Florida in autumn of 1999 and included K. brevis bloom monitoring (type 1), with limited advisories on transport (type 3) and the detection of blooms in new areas (type 2). In autumn 2000, we improved both the transport forecasts and detection capabilities and began prediction of conditions favorable for bloom development (type 4). The HAB Bulletins have had several successes. The state of Florida was advised of the potential for a bloom to occur at the end of September 2000 (type 4), and the state was alerted to the position of blooms in January 2000 and October 2001 in areas that had not been previously sampled (type 3). These successful communications of HAB activity allowed Florida agencies responsible for shellfish management and public health to respond to a rapidly developing event in a timely, efficient manner.