Spatial scaling between leaf area index maps of different resolutions

We developed algorithms for spatial scaling of leaf area index (LAI) using sub-pixel information. The study area is located near Liping County, Guizhou Province, in China. Methods for LAI spatial scaling were investigated on LAI images with 960 m resolution derived in two ways. LAI from distributed calculation (LAID) was derived using Landsat ETM+ data (30 m), and LAI from lumped calculation (LAIL) was obtained from the coarse (960 m) resolution data derived through resampling the ETM+ data. We found that lumped calculations can be considerably biased compared to the distributed (ETM+) case, suggesting that global and regional LAI maps can be biased if surface heterogeneity within the mapping resolution is ignored. Based on these results, we developed algorithms for removing the biases in lumped LAI maps using sub-pixel land cover-type information, and applied these to correct one coarse resolution LAI product which greatly improved its accuracy.     

Landsat ETM+ Images in the Estimation of Seasonal Lake Water Quality in Boreal River Basins

We investigated the use of Landsat ETM+ images in the monitoring of turbidity, colored dissolved organic matter (CDOM), and Secchi disk transparency (Z(SD)) in lakes of two river basins located in southern Finland. The ETM+ images were acquired in May, June, and September 2002 and were corrected for atmospheric disturbance using the simplified method of atmospheric correction (SMAC) model. The in situ measurements consisted of water sampling in the largest lake of the region, routine monitoring results for the whole study area, and Z(SD) observations made by volunteers. The ranges of the water quality variables in the dataset were as follows: turbidity, 0.6-25 FNU; absorption coefficient of CDOM at 400 nm, 1.0-12.2 m(-1); Z(SD), 0.5-5.5 m; and chlorophyll a concentration, 2.4-80 mug L(-1). The estimation accuracies of the image-specific empirical algorithms expressed as relative errors were 23.0% for turbidity, 17.4% for CDOM, and 21.1% for Z(SD). If concurrent in situ measurements had not been used for algorithm training, the average error would have been about 37%. The atmospheric correction improved the estimation accuracy only slightly compared with the use of top-of-atmospheric reflectances. The accuracy of the water quality estimates without concurrent in situ measurements could have been improved if in-image atmospheric parameters had been available. The underwater reflectance simulations of the ETM+ channel wavelengths using water quality typical for Finnish lakes (data from 1113 lakes) indicated that region-specific algorithms may be needed in other parts of the country, particularly in the case of Z(SD). Despite the limitations in the spectral and radiometric resolutions, ETM+ imagery can be an effective aid, particularly in the monitoring and management of small lakes (<1 km(2)), which are often not included in routine monitoring programs.     

Measurements and simulation of forest leaf area index and net primary productivity in Northern China

Large scale process-based modeling is a useful approach to estimate distributions of global net primary productivity (NPP). In this paper, in order to validate an existing NPP model with observed data at site level, field experiments were conducted at three sites in northern China. One site is located in Qilian Mountain in Gansu Province, and the other two sites are in Changbaishan Natural Reserve and Dunhua County in Jilin Province. Detailed field experiments are discussed and field data are used to validate the simulated NPP. Remotely sensed images including Landsat Enhanced Thematic Mapper plus (ETM+, 30 m spatial resolution in visible and near infrared bands) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER, 15m spatial resolution in visible and near infrared bands) are used to derive maps of land cover, leaf area index, and biomass. Based on these maps, field measured data, soil texture and daily meteorological data, NPP of these sites are simulated for year 2001 with the boreal ecosystem productivity simulator (BEPS). The NPP in these sites ranges from 80 to 800 gCm(-2)a(-1). The observed NPP agrees well with the modeled NPP. This study suggests that BEPS can be used to estimate NPP in northern China if remotely sensed images of high spatial resolution are available.     

ACCURACY OF LAKE AND STREAM TEMPERATURES ESTIMATED FROM THERMAL INFRARED IMAGES1

Emitted thermal infrared radiation (TIR, λ= 8 to 14 μm) can be used to measure surface water temperatures (top approximately 100 μm). This study evaluates the accuracy of stream (50 to 500 m wide) and lake (300 to 5,000 m wide) radiant temperatures (15 to 22°C) derived from airborne (MASTER, 5 to 15 m) and satellite (ASTER 90 m, Landsat ETM+ 60 m) TIR images. Applied atmospheric compensations changed water temperatures by ?0.2 to +2.0°C. Atmospheric compensation depended primarily on atmospheric water vapor and temperature, sensor viewing geometry, and water temperature. Agreement between multiple TIR bands (MASTER ‐ 10 bands, ASTER ‐ 5 bands) provided an independent check on recovered temperatures. Compensations improved agreement between image and in situ surface temperatures (from 2.0 to 1.1°C average deviation); however, compensations did not improve agreement between river image temperatures and loggers installed at the stream bed (from 0.6 to 1.6°C average deviation). Analysis of field temperatures suggests that vertical thermal stratification may have caused a systematic difference between instream gage temperatures and corrected image temperatures. As a result, agreement between image temperatures and instream temperatures did not imply that accurate TIR temperatures were recovered. Based on these analyses, practical accuracies for corrected TIR lake and stream surface temperatures are around 1°C.     

Spectrally Driven Classification of High Spatial Resolution,Hyperspectral Imagery: A Tool for Mapping In-Stream Habitat

Streams represent an essential component of functional ecosystems and serve as sensitive indicators of disturbance. Accurate mapping and monitoring of these features is therefore critical, and this study explored the potential to characterize aquatic habitat with remotely sensed data. High spatial resolution, hyperspectral imagery of the Lamar River, Wyoming, USA, was used to examine the relationship between spectrally defined classes and field-mapped habitats. Advantages of this approach included enhanced depiction of fine-scale heterogeneity and improved portrayal of gradational zones between adjacent features. Certain habitat types delineated in the field were strongly associated with specific image classes, but most included areas of diverse spectral character; spatially buffering the field map polygons strengthened this association. Canonical discriminant analysis (CDA) indicated that the ratio of the variability among groups to that within a group was an order of magnitude greater for spectrally defined image classes (20.84) than for field-mapped habitat types (1.82), suggesting that unsupervised image classification might more effectively categorize the fluvial environment. CDA results also suggested that shortwave-infrared wavelengths were valuable for distinguishing various in-stream habitats. Although hyperspectral stream classification seemed capable of identifying more features than previously recognized, the technique also suggested that the intrinsic complexity of the Lamar River would preclude its subdivision into a discrete number of classes. Establishing physically based linkages between observed spectral patterns and ecologically relevant channel characteristics will require additional research, but hyperspectral stream classification could provide novel insight into fluvial systems while emerging as a potentially powerful tool for resource management.     

A multi-scale remote sensing approach for monitoring northern peatland hydrology: present possibilities and future challenges

Remote sensing has the potential to provide quantitative spatially explicit hydrological information across northern peatland complexes. This paper details a multi-scale remote sensing approach for assessing the use of Sphagnum mosses as proxy indicators of near-surface hydrology. Several spectral indices developed from the near infra-red (NIR) and shortwave infra-red (SWIR) liquid water absorption bands, as well as a biophysical index can be correlated with measures of near-surface moisture in the laboratory, in the field and from airborne imagery. Data from all platforms revealed similar patterns in the spectral indices in relation to changes in moisture although the strength of correlations was reduced as the spatial scale increased. The rapid collection of temporally and spatially explicit hydrological data means that the technique has potential practical application for environmental managers and peatland scientists at the local scale. The task of up-scaling the technique for use in operational peatland hydrological monitoring to the global scale is challenging but achievable, and requires further investigation into the heterogeneity of near-surface moisture across Sphagnum patches and the application of novel image processing techniques to improve the spatial resolution of currently available satellite imagery.     

Application of remote and in situ information to the management of wetlands in Poland

The protection and regeneration of wetlands has been of crucial importance as a goal in ecological research and in nature conservation for some time and is more important than ever now. Knowledge about the biophysical properties of wetlands' vegetation retrieved from satellite images enables us to improve the monitoring of these unique areas, which are otherwise very often impenetrable and therefore difficult to examine, analyze and assess by means of site visits. The Biebrza Wetlands are situated in the North-East part of Poland and are one of the largest areas made up of marshes and swamps in the entire EU. This is still one of the wildest areas and one of the least destroyed, damaged or changed by human impact. However, in the recent decades there have been attempts made to intensify and overexploit the natural resources of the region and implement new agriculture practices in the area. In this period, drainage canals have been built, and a good deal of the area has been drained. The area of this precious ecosystem covers 25 494 ha. This valuable area of peat with unique vegetation species and with very special birds is one of the most valuable areas in Europe and in 1995 was added to the list of Ramsar sites. The investigation of wetlands in the Biebrza River Valley has been carried out at ground level by taking measurements of soil moisture, evapotranspiration, Leaf Area Index, wet and dry biomass and the levels of ground water and meteorological parameters. Also examined were radiative temperature, detailed vegetation mapping, and APAR. For some years the deterioration of peat lands has been noticed due to the drying out of the area and the frequent outbreak of fires. The consequence is the succession of new vegetation and the appearance of new ecosystems. The Remote Sensing Centre in the Institute of Geodesy and Cartography has undertaken the investigation by applying ERS-2.SAR and ENVISAT ASAR of IS2 and IS4 and VV, HH, HV polarization for the purpose of modeling soil moisture and humidity changes of the area under investigation. The investigation also aimed at finding the best biophysical properties of wetlands' vegetation to characterize marshland habitats and its changes. At the same time as registering the microwave data, the optical data from Landsat ETM+, SPOT VEGETATION, ERS-2.ATSR, ENVISAT MERIS, and NOAA/AVHRR have been registered and information about the biomass and heat fluxes as sensible and latent heat has also been calculated. The vegetation indices are calculated from EO satellite data taking into account jointly the features of vegetation responsible for reflection in various bands and combining this information from several spectral bands. Also, the changes in the humidity of the area have been examined by extracting the backscattering coefficients from two SAR images that were taken at a similar period of the year but with a gap of 5 years. The information about soil moisture as retention, soil moisture changes, heat fluxes and evapotranspiration are all very important for estimates of CO(2) sequestration. The ENVISAT images have been obtained for the ESA AO-ID122 project. Also the SMOS and ALOS data will be applied for the Biebrza Wetlands in the future.     

Mapping Locust Habitats in the Amudarya River Delta,Uzbekistan with Multi-Temporal MODIS Imagery

Reed beds of Phragmites australis in the River Amudarya delta near the Aral Sea constitute permanent breeding areas of the Asian Migratory locust, Locusta migratoria migratoria. Every year, thousands of hectares are treated with broad-spectrum insecticides to prevent locust swarms from damaging crops in adjacent areas. To devise efficient locust monitoring and management plans, accurate and updated information about the spatial distribution of reeds is necessary. Given the vast geographic extent of the delta, traditional, ground survey methods are inadequate. Remotely sensed data collected by the MODIS sensor aboard the TERRA satellite provide a useful tool to characterize the spatial distribution of reeds. Multi-temporal MODIS data, collected at different times of the growing season, were used to generate spectral-temporal signatures for reeds and other land cover classes. These spectral-temporal signatures were matched with reed phenology. MODIS information was digitally classified to generate a land cover map with an overall accuracy of 74%. MODIS data captured 87% of the ground-verified reed locations. Estimates derived from MODIS data indicate that 18% of the study area was covered by reeds. However, high commission error resulted from misclassification of reeds mixed with shrubs class and shrubs class as reeds. This could have resulted in overprediction of the area covered by reeds. Additional research is needed to minimize the overlap between reeds and other vegetation classes (shrubs, and reed and shrub mix). Nevertheless, despite its relatively low spatial resolution (250 m), multi-temporal MODIS data were able to adequately capture the distribution of reeds. Instead of blanketing the fragile wetland ecosystem of the Amudarya delta with chemical anti-locust treatments, plant protection specialists can use this information to devise ecologically sound pest management plans aimed at reducing the adverse environmental impact in the zone of the Aral Sea ecological catastrophe. MODIS methodology to identify reed stands can be applicable to the Migratory locust habitats in other geographic areas.     

Comparing the utility of image algebra operations for characterizing landscape changes: the case of the Mediterranean coast

The aim of this study is to compare various image algebra procedures for their efficiency in locating and identifying different types of landscape changes on the margin of a Mediterranean coastal plain, Cukurova, Turkey. Image differencing and ratioing were applied to the reflective bands of Landsat TM datasets acquired in 1984 and 2006. Normalized Difference Vegetation index (NDVI) and Principal Component Analysis (PCA) differencing were also applied. The resulting images were tested for their capacity to detect nine change phenomena, which were a priori defined in a three-level classification scheme. These change phenomena included agricultural encroachment, sand dune afforestation, coastline changes and removal/expansion of reed beds. The percentage overall accuracies of different algebra products for each phenomenon were calculated and compared. The results showed that some of the changes such as sand dune afforestation and reed bed expansion were detected with accuracies varying between 85 and 97% by the majority of the algebra operations, while some other changes such as logging could only be detected by mid-infrared (MIR) ratioing. For optimizing change detection in similar coastal landscapes, underlying causes of these changes were discussed and the guidelines for selecting band and algebra operations were provided.     

Object-Based Classification as an Alternative Approach to the Traditional Pixel-Based Classification to Identify Potential Habitat of the Grasshopper Sparrow

The traditional method of identifying wildlife habitat distribution over large regions consists of pixel-based classification of satellite images into a suite of habitat classes used to select suitable habitat patches. Object-based classification is a new method that can achieve the same objective based on the segmentation of spectral bands of the image creating homogeneous polygons with regard to spatial or spectral characteristics. The segmentation algorithm does not solely rely on the single pixel value, but also on shape, texture, and pixel spatial continuity. The object-based classification is a knowledge base process where an interpretation key is developed using ground control points and objects are assigned to specific classes according to threshold values of determined spectral and/or spatial attributes. We developed a model using the eCognition software to identify suitable habitats for the Grasshopper Sparrow, a rare and declining species found in southwestern Québec. The model was developed in a region with known breeding sites and applied on other images covering adjacent regions where potential breeding habitats may be present. We were successful in locating potential habitats in areas where dairy farming prevailed but failed in an adjacent region covered by a distinct Landsat scene and dominated by annual crops. We discuss the added value of this method, such as the possibility to use the contextual information associated to objects and the ability to eliminate unsuitable areas in the segmentation and land cover classification processes, as well as technical and logistical constraints. A series of recommendations on the use of this method and on conservation issues of Grasshopper Sparrow habitat is also provided.     

Classifying environmentally significant urban land uses with satellite imagery

We investigated Bayesian networks to classify urban land use from satellite imagery. Landsat Enhanced Thematic Mapper Plus (ETM(+)) images were used for the classification in two study areas: (1) Marina del Rey and its vicinity in the Santa Monica Bay Watershed, CA and (2) drainage basins adjacent to the Sweetwater Reservoir in San Diego, CA. Bayesian networks provided 80-95% classification accuracy for urban land use using four different classification systems. The classifications were robust with small training data sets with normal and reduced radiometric resolution. The networks needed only 5% of the total data (i.e., 1500 pixels) for sample size and only 5- or 6-bit information for accurate classification. The network explicitly showed the relationship among variables from its structure and was also capable of utilizing information from non-spectral data. The classification can be used to provide timely and inexpensive land use information over large areas for environmental purposes such as estimating stormwater pollutant loads.     

Combining remote sensing imagery and forest age inventory for biomass mapping

Aboveground biomass (AGB) of forests is an important component of the global carbon cycle. In this study, Landsat ETM(+) images and field forest inventory data were used to estimate AGB of forests in Liping County, Guizhou Province, China. Three different vegetation indices, including simple ratio (SR), reduced simple ratio (RSR), and normalized difference vegetation index (NDVI), were calculated from atmospherically corrected ETM(+) reflectance images. A leaf area index (LAI) map was produced from the RSR map using a regression model based on measured LAI and RSR. The LAI map was then used to develop an initial AGB map, from which forest stand age was deduced. Vegetation indices, LAI, and forest stand age were together used to develop AGB estimation models for different forest types through a stepwise regression analysis. Significant predictors of AGB changed with forest types. LAI and NDVI were significant predictors of AGB for Chinese fir (R(2)=0.93). The model using LAI and stand age as predictors explained 94% of the AGB variance for coniferous forests. Stand age captured 79% of the AGB variance for broadleaved forests (R(2)=0.792). AGB of mixed forests was predicted well by LAI and SR (R(2)=0.931). Without differentiating among forest types, the model with SR and LAI as predictors was able to explain 90% of AGB variances of all forests. In Liping County, AGB shows a strong gradient that increases from northeast to southwest. About 64% of the forests have AGB in the range from 90 to 180 t ha(-1).     

Predictive models of turbidity and water depth in the Doñana marshes using Landsat TM and ETM+ images

We have used Landsat-5 TM and Landsat-7 ETM+ images together with simultaneous ground-truth data at sample points in the Doñana marshes to predict water turbidity and depth from band reflectance using Generalized Additive Models. We have point samples for 12 different dates simultaneous with 7 Landsat-5 and 5 Landsat-7 overpasses. The best model for water turbidity in the marsh explained 38% of variance in ground-truth data and included as predictors band 3 (630–690 nm), band 5 (1550–1750 nm) and the ratio between bands 1 (450–520 nm) and 4 (760–900 nm). Water turbidity is easier to predict for water bodies like the Guadalquivir River and artificial ponds that are deep and not affected by bottom soil reflectance and aquatic vegetation. For the latter, a simple model using band 3 reflectance explains 78.6% of the variance. Water depth is easier to predict than turbidity. The best model for water depth in the marsh explains 78% of the variance and includes as predictors band 1, band 5, the ratio between band 2 (520–600 nm) and band 4, and bottom soil reflectance in band 4 in September, when the marsh is dry. The water turbidity and water depth models have been developed in order to reconstruct historical changes in Doñana wetlands during the last 30 years using the Landsat satellite images time series.     

Production of vermifertilizer from guar gum industrial wastes by using composting earthworm <Emphasis Type="Italic">Perionyx sansibaricus</Emphasis> (Perrier)

Efforts have been made to convert the guar gum industrial waste into a value-added product, by employing a new earthworm species for vermicomposting e.g. Perionyx sansibaricus (Perrier) (Megascolecidae), under laboratory conditions. Industrial lignocellulosic waste was amended with other organic supplements (saw dust and cow dung); and three types of vermibeds were prepared: guar gum industrial waste + cow dung + saw dust in 40: 30: 30 ratio (T₁), guar gum industrial waste + cow dung + saw dust in 60: 20: 20 ratio (T_2,), and guar gum industrial waste + cow dung + saw dust in 75: 15: 10 ratio (T₃). As compared to initial concentrations, vermicomposts exhibited a decrease in organic C content (5.0–11.3%) and C:N ratio (11.1–24.4%) and an increase in total N (18.4–22.8%), available P (39.7–92.4%), and exchangeable K (9.4–19.7%) contents, after 150 days of vermicomposting. A vermicomposting coefficient (VC) was used to compare of vermicomposting with the experimental control (composting). P. sansibaricus exhibited maximum value of mean individual live weight (742.8 ± 21.1 mg), biomass gain (442.94 ± 21.8 mg), growth rate (2.95 ± 0.15 mg day⁻¹), cocoon numbers (96.0 ± 5.1) and reproduction rate (cocoons worm⁻¹ day⁻¹) (0.034 ± 0.001) in T₂ treatment. In T₃ maximum mortality (30.0 ± 4.01 %) in earthworm population was observed. Overall, T₂ vermibed appeared as an ideal substrate to manage guar gum industrial waste effectively. Vermicomposting can be proposed as a low-input basis technology to convert industrial waste into value-added biofertilizer.     

Water quality assessment using remote sensing techniques: Medrano Creek, Argentina

Two spectral bands of the visible spectrum [0.45-0.52 microm (Blue), 0.52-0.60 microm (Green)] of satellite images obtained by LANDSAT 7 ETM+ have been used in this study to follow the contaminated waters of Medrano Creek when it flows into Río de la Plata River. The former is one of the five fresh watercourses going through the Metropolitan Area of Buenos Aires, Argentina, where 13 million people live. Previous studies have shown that the water quality of Rio de la Plata at the outlet of Medrano Creek has decreased more than 50% as a source of water for human consumption. The non-treated effluents of the textile industry probably affect the water quality. We have developed a model that predicts the water quality index (WQI) of surface waters in the study area and uses linear regression analysis. The model has been validated using a data set of 12 physicochemical parameters obtained during the last 3 years. The potentiality of using satellite images was confirmed by the results: (a) to trace the organic contamination (associated with dyes) in freshwater systems and (b) as tools for decision making in the management of water resources.     

Changes in Forest Area Along Stream Networks in an Agricultural Catchment of the Great Barrier Reef Lagoon

Scenes from the series of multispectral sensors on the Landsat satellites were used to map recent changes (between 1972 and 2004) in forest cover within and adjacent to stream networks of an intensively farmed region of the southern Great Barrier Reef catchment (Australia). Unsupervised ISODATA classifications of Tasseled-Cap transformed data (at 57 m ground resolution) mapped forest and cleared areas within 150 m of Pisoneer catchment waterways with 72.2% overall accuracy (K(hat) = 0.469), when adjusted for the size of each class. Although the user's accuracy was higher for the forest class (82.1 +/- 8.4% at alpha = 0.05), large errors of commission (34.2 +/- 8.3%) substantially affected map accuracy for the cleared class. The main reasons for misclassification include: (1) failure to discriminate narrowly vegetated riparian strips; (2) misregistration of scenes; and (3) spectral similarity of ground cover. Error matrix probabilities were used to adjust the mapped area of classes, resulting in a decline of forest cover by 12.3% and increase of clearing by 18.5% (22.4 km(2) change; 95% confidence interval: 14.3-29.6 km(2)) between 1972 and 2004. Despite the mapping errors, Landsat data were able to identify broad patterns of land cover change that were verified from aerial photography. Most of the forest losses occurred in open forest to woodland habitat dominated by Eucalyptus, Corymbia, and Lophostemon species, which were largely replaced by sugarcane cropping. Melaleuca communities were similarly affected, though they have a much smaller distribution in the catchment.     

The National Vegetation Classification Standard applied to the remote sensing classification of two semiarid environments

The National Vegetation Classification Standard (NVCS) was implemented at two US National Park Service (NPS) sites in Texas, the Padre Island National Seashore (PINS) and the Lake Meredith National Recreation Area (LMNRA), to provide information for NPS oil and gas management plans. Because NVCS landcover classifications did not exist for these two areas prior to this study, we created landcover classes, through intensive ground and aerial reconnaissance, that characterized the general landscape features and at the same time complied with NVCS guidelines. The created landcover classes were useful for the resource management and were conducive to classification with optical remote sensing systems, such as the Landsat Thematic Mapper (TM). In the LMNRA, topographic elevation data were added to the TM data to reduce confusion between cliff, high plains, and forest classes. Classification accuracies (kappa statistics) of 89.9% (0.89) and 88.2% (0.87) in PINS and LMNRA, respectively, verified that the two NPS landholdings were adequately mapped with TM data. Improved sensor systems with higher spectral and spatial resolutions will ultimately refine the broad classes defined in this classification; however, the landcover classifications created in this study have already provided valuable information for the management of both NPS lands. Habitat information provided by the classifications has aided in the placement of inventory and monitoring plots, has assisted oil and gas operators by providing information on sensitive habitats, and has allowed park managers to better use resources when fighting wildland fires and in protecting visitors and the infrastructure of NPS lands.     

The use of Landsat multispectral scanner data for the analysis and management of flooding on the river Severn,England

Remote sensing has emerged as one of the major techniques for the analysis and delineation of large floods. This analysis can provide data invaluable for the hydrological management of large river systems. A need for information on the extent of floodplain inundation for the lower reaches of the largest river in the UK was met by a search through Landsat images of floods and the analysis of the best example recorded. Automated classification of the Landsat imagery of this flood on the river Severn in 1977 was used to provide estimates of the extent and spatial distribution of inundation. Flood images were generated using the Plessey IDP 3000 image processor, and the maps derived accorded well with aerial photography and qualitative flood information. Three distinct floodplain environments were delineated and flood images produced by different spectral bands compared. Specific questions prompted by flood hazard management and concerning the processes and extent of flooding were answered by the Landsat data analysis. Management of the flood risk of large rivers is expensive and remote sensing data is a relatively cheap and effective way of monitoring control works and providing data for the prediction of the effects of future hydrological works. Remote sensing is a practical way in which spatial information concerning the behavior of large dynamic systems can be obtained both quickly and relatively cheaply.     

Building spectral libraries for wetlands land cover classification and hyperspectral remote sensing

Recent advances in remote sensing provide opportunities to map plant species and vegetation within wetlands at management relevant scales and resolutions. Hyperspectral imagers, currently available on airborne platforms, provide increased spectral resolution over existing space-based sensors that can document detailed information on the distribution of vegetation community types, and sometimes species. Development of spectral libraries of wetland species is a key component needed to facilitate advanced analytical techniques to monitor wetlands. Canopy and leaf spectra at five sites in California, Texas, and Mississippi were sampled to create a common spectral library for mapping wetlands from remotely sensed data. An extensive library of spectra (n=1336) for coastal wetland communities, across a range of bioclimatic, edaphic, and disturbance conditions were measured. The wetland spectral libraries were used to classify and delineate vegetation at a separate location, the Pacheco Creek wetland in the Sacramento Delta, California, using a PROBE-1 airborne hyperspectral data set (5m pixel resolution, 128 bands). This study discusses sampling and collection methodologies for building libraries, and illustrates the potential of advanced sensors to map wetland composition. The importance of developing comprehensive wetland spectral libraries, across diverse ecosystems is highlighted. In tandem with improved analytical tools these libraries provide a physical basis for interpretation that is less subject to conditions of specific data sets. To facilitate a global approach to the application of hyperspectral imagers to mapping wetlands, we suggest that criteria for and compilation of wetland spectral libraries should proceed today in anticipation of the wider availability and eventual space-based deployment of advanced hyperspectral high spatial resolution sensors.     

Determinants of deforestation in Nepal's Central Development Region

The process of deforestation in the Central Development Region (CDR) of Nepal is diverse in space and time, with rapid deforestation still occurring in areas outside the national parks and wildlife reserves. This paper identifies the spatial driving forces (SDFs) of deforestation in the CDR for 1975–2000 using satellite data of 1975 (MSS), 1990 (TM), and 2000 (ETM+) along with socio-demographic and socioeconomic variables. Radiometrically calibrated satellite images are individually classified into seven distinct classes and merged together to cover the entire CDR. Classification accuracies are also assessed. Areas of land use and cover within the areas of each Village Development Committee (VDC) and municipality represented by GIS polygons are calculated from the classified images by overlaying vector files of 1845 polygons representing sections of VDCs and municipalities in 30–1199 m, 1200–2399 m, 2400–4999 m and >5000 m elevation levels. These elevation levels were estimated from the DEM compiled from 24 ASTER scenes taken on different dates. Only the first three elevation levels are used in the analysis because area >5000 m is under permanent snow cover where human related forestry activities are almost negligible. A transition matrix is generated for 1975–1990 using classified images of 1975 and 1990 and then this product is used to further develop another transition matrix for 1990–2000 with the classified ETM+ 2000 images as the final stage. The GIS polygon layer is overlaid on the transition matrices to calculate deforestation areas for 1975–1990 and 1990–2000. Biophysical and socioeconomic information collected from various sources is then brought into a GIS platform for statistical analyses. Six linear regression models are estimated using SAS; in effect, two models for each elevation range representing the 1975–1990 and 1990–2000 periods of change to identify SDF influences on deforestation. These regression analyses reveal that deforestation in the CDR is related to multiple factors, such as farming population, genders of various ages, migration, elevation, road, distance from road to forest, meandering and erosion of river, and most importantly the conversion of forestland into farmland.