Search

The transpiration rate is an important factor that determines the bioaccumulation potential of pesticides from soil and can present a spatiotemporal pattern. In the present study, we proposed a satellite‐based approach to map the bioaccumulation potential of pesticides from soil using the Global Land Evaporation Amsterdam Model (GLEAM). In the proposed model, the spatiotemporal variable (i.e., plant transpiration rate) was separately analyzed from the plant‐ and chemical‐specific variables. The simulated bioaccumulation factors (BAFs; steady‐state concentration ratios between plants and soil) of atrazine and lindane for the United States indicated that the proposed model can better predict the spatiotemporal pattern of bioaccumulation potentials of pesticides from soil than a previous weather‐based model. The proposed approach using GLEAM's satellite data avoids the overestimation of plant transpiration rate in regions with a dry and warm climate. The comparison of BAFs between the proposed and weather‐based models indicated that the satellite‐based simulation was consistent with the weather‐based simulation for most states and was more effective for the southwest region. Furthermore, plant‐ and chemical‐specific variables were simulated for over 700 pesticides, which could be multiplied by satellite‐based canopy transpiration rates to map the bioaccumulation potentials of chemicals from soil. Further evaluation of plant‐specific variables, partitioning behaviors of ionizable compounds, and multiple uptake routes (e.g., airborne residue deposition) will aid in the evaluation of the spatiotemporal patterns of pesticide BAFs in plants in future research. Environ Toxicol Chem 2023;42:117–129. © 2022 SETAC [ABSTRACT FROM AUTHOR]

Predicting wildfire spread is critical for land management and disaster preparedness. To this end, we present “Next Day Wildfire Spread,” a curated, large-scale, multivariate dataset of historical wildfires aggregating nearly a decade of remote-sensing data across the United States. In contrast to existing fire datasets based on Earth observation satellites, our dataset combines 2-D fire data with multiple explanatory variables (e.g., topography, vegetation, weather, drought index, and population density) aligned over 2-D regions, providing a feature-rich dataset for machine learning. To demonstrate the usefulness of this dataset, we implement a neural network that takes advantage of the spatial information of these data to predict wildfire spread. We compare the performance of the neural network with other machine learning models: logistic regression and random forest. This dataset can be used as a benchmark for developing wildfire propagation models based on remote-sensing data for a lead time of one day. [ABSTRACT FROM AUTHOR]

Mapping vegetation canopy height is a crucial aspect of understanding and management of vegetation ecosystems. In this study, we applied ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) canopy heights with ancillary Landsat, LANDFIRE (Landscape Fire and Resource Management Planning Tools) and topographic variables to produce a spatially explicit 30-m canopy height product across the contiguous United States. We used a gradient-boosted trees regression model with ancillary datasets as predictors to model and predict canopy height across various vegetation biomes. Overall, the fitted model achieved an R2 value of 0.76, a mean bias of 0.1 m, a general over-estimation, and MAE (mean absolute error) of 2.5 m on a hold-out validation dataset of 21,185 observations. Model performance varied across biomes (R2 = 0.49–0.73, mean biases = −0.3 to 0.8 m, MAE = 1.4 to 3.3 m). Independent accuracy assessment with airborne lidar (R2 = 0.72, MAE = 3.9 m) and canopy height models from the NEON program (R2 = 0.67, MAE = 6.9 m) also showed generally consistent agreement. In general, our developed canopy height product performed better in relatively homogenous environments or dense forests such as mangroves, flooded, coniferous or broadleaf and mixed forests than in relatively sparse environments associated with deserts, and chapparal forests. The generated canopy height product, designated as the continental canopy height map (CCHM), is publicly available and contributes to spatially explicit canopy height datasets over the US at an enhanced 30 m Landsat spatial resolution. • Produced a 30-m canopy height map across the contiguous United States. • Applied ICESat-2, Landsat, LANDFIRE and topographic data to model canopy heights. • Canopy height model predictions were generally consistent with reference data. • Canopy height model predictions varied by biome. [ABSTRACT FROM AUTHOR]

Monitoring vegetation moisture conditions is paramount to better understand and assess drought impacts on vegetation, enhance crop yield predictions, and improve ecosystem models. Passive microwave remote sensing allows retrievals of the vegetation optical depth (VOD; [unitless]), which is directly proportional to the vegetation water content (VWC; in units of water mass per unit area [kg/m2]). However, VWC is largely dependent on the dry biomass and structure imprints on the VOD signal. Previously, statistical models have been used to isolate the water component from the biomass and structure components. Physically-based approaches have not yet been proposed for this goal. In this study, we present a multi-sensor semi-physical approach to retrieve the vegetation moisture from the VOD and express it as Live Fuel Moisture Content (LFMC [%]; the percentage of water mass per dry biomass unit). The study is performed in the western United States for the period April 2015 – December 2018. There, in situ LFMC samples are available for assessment. We rely on a VOD model based on vegetation height data from GEDI/Sentinel-2 and radar backscatter from Sentinel-1, which account for the biomass and structure components. Vegetation moisture is retrieved at L-, X- and Ku-bands by minimizing the difference between the modeled VOD and the VOD estimates from SMAP (L-band) and AMSR-2 (X- and Ku-band) satellites. Results show that the LFMC retrievals are independent of canopy height, land cover, and radar backscatter, demonstrating the capability of the proposed algorithm to separate water dynamics from the biomass/structure component in VOD. LFMC estimates at X- and Ku-bands reproduce well the expected spatio-temporal dynamics of in situ LFMC. Results show good agreement with in situ at a regional scale, with Pearson's correlations (r) between in situ LFMC samples and LFMC estimates of 0.64 (Ku-band), 0.60 (X-band) and 0.47 (L-band). Similar results are obtained independently for shrub and forest sites at X- and Ku-bands. In most comparisons between in situ and estimated LFMC, biases are below 10% of the dynamic range of LFMC. Performance at L-band is limited by the fact that this frequency senses the full vertical extent of the canopy, while in situ samples are taken only from top of canopy leaves to which X- and Ku-bands are much more sensitive. More insight will be needed for grasslands (r = 0.44 at X-band) using time-dynamic canopy height data. Furthermore, a pixel-scale assessment is conducted, showing a good agreement in most sites (r > 0.6). The proposed method can be tailored to exploit the synergies of past (e.g. , AMSR-E), current (e.g. , AMSR-2) and future satellite sensors such as CIMR and ROSE-L for global vegetation moisture mapping at different canopy layers. [Display omitted] • A multi-sensor semi-physical algorithm is applied to retrieve vegetation moisture. • The water component in vegetation optical depth is disentangled from dry biomass. • Vegetation moisture retrievals at X- and Ku-bands capture the in situ dynamics. • L-band has poorer performance due to lack of in situ samples in wood canopy layers. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Systems (UAS) have emerged as a platform capable of providing valuable data on vegetation structure, health, and productivity. The platform provides maximum temporal flexibility in data collection and customizability of spatial footprints which is important for mapping and monitoring vegetation conditions especially for high spectrally and structurally heterogeneous landscapes. In this letter, we investigate the potential improvement in structural information when using sensor payloads that include infrared bands (730–810 nm) in addition to the typical visible bands of many UAS sensor payloads. Specifically, we use derived point clouds and imagery collected from UAS-mounted sensors at a test site in Louisville, Kentucky, USA to assess whether multispectral sensors improve estimates of vegetation structure for plant greenness (leaf-on) and senescence (leaf-off) periods. Results indicate that including detailed multispectral reflectance information beyond the visible portion of the electromagnetic spectrum aids in estimating structural characteristics of woody vegetation, regardless of season. While both leaf-on and leaf-off periods result in reasonable crown height estimates, leaf-on conditions also improve radial estimates, likely due to continuous canopy surfaces. These findings advance research for remote sensing analyses assessing structural composition in heterogeneous landscapes where varying levels of vegetation structure have implications on land use and land function. [ABSTRACT FROM AUTHOR]

Understanding the fate of terrestrial plant waxes from source to sink is critical for improving paleoclimate interpretations from sedimentary plant waxes. However, there is limited knowledge about the controls on plant wax integration and transport in large catchments with multiple biomes and climates. To address this lack of understanding, we investigated the vegetation and climatic controls on plant wax integration and transport from the Mississippi River Basin (MRB), the largest river in the U.S., to the Gulf of Mexico (GOM). We first estimated the geographic distribution of n -alkane carbon (δ13C alk) and hydrogen (δ2H alk) isotopic compositions (i.e. isoscapes) in the MRB using plant isotope fractionation calibrations from North America and similar climate regions for the pre-industrial. We utilized two different vegetation maps (i.e. biome map and %C 4 map) and two plant isotope fractionation estimation approaches, and discussed advantages and disadvantages of each method. Then, we developed mixing models weighting the isotope values by biological and climatic parameters (i.e. vegetation area, n -alkane concentration by chain lengths, net primary productivity (NPP), and runoff) and their combinations to test the sensitivity of basin-integrated plant wax isotopic compositions to these variables. Our approach does not factor in soil stocks of plant wax nor degradation in transit, as future work will need to constrain these factors for the MRB for the period of interest. Vegetation area weighting alone predicted relatively high C 4 plant contributions to the pool of waxes exported from the basin. When n -alkane concentration in leaves, NPP, or runoff was considered, the contribution of forest-derived plant waxes increased. Sensitivity of plant wax isotopic compositions to productivity and transport efficiency varied among models and chain lengths. For example, n -C 29 alkanes were sensitive to plant wax contribution increases from forests whereas n -C 33 alkanes were more sensitive to increases in C 4 grassland productivity. The plant wax concentration and NPP were combined as an estimate for total wax productivity, and a combination of this total wax productivity and runoff were used to simulate both production and transport. These multi-parameter models estimated the highest plant wax contribution from C 3 forests because these areas were calculated to have the highest productivity and greatest runoff. Despite large differences in δ13C alk and δ2H alk values across the MRB, variation in the predicted δ13C and δ2H of exported n -alkanes is small across all models. This small range is consistent with a calculated predominance of plant wax export from wet and tree-dominated biomes and suggests that drier and C 4 grass-dominated biomes may be under-represented in the sedimentary plant wax record. [ABSTRACT FROM AUTHOR]

The southeastern U.S. produces the most industrial roundwood in the U.S. each year, largely from commercial pine plantations. The extent of plantation forests and management dynamics can be difficult to ascertain from periodic forest inventories, yet short-rotation tree plantations also present challenges for remote sensing. Here, we integrated spectral, temporal, and structural information from airborne and satellite platforms to distinguish pine plantations from natural forests and evaluate the contribution from planted forests to regional forest cover in the southeastern U.S. Within flight lines from NASA Goddard's Lidar, Hyperspectral, and Thermal (G-LiHT) Airborne Imager, lidar metrics of forest structure had the highest overall accuracy for pine plantations among single-source classifications (90%), but the combination of spectral and temporal metrics from Landsat generated comparable accuracy (91%). Combined structural, temporal, and spectral information from G-LiHT and Landsat had the highest accuracy for plantations (92%) and natural forests (88%). At a regional scale, classifications using Landsat spectral and temporal metrics had between 74 and 82% mean class accuracy for plantations. Regionally, plantations accounted for 28% of forest cover in the southeastern U.S., a result similar to plot-based estimates, albeit with greater spatial detail. Regional maps of plantation forests differed from existing map products, including the National Land Cover Database. Combining plantation extent in 2011 with Landsat-based forest change data identified strong regional gradients in plantation dynamics since 1985, with distinct spatial patterns of rotation age (east-west) and plantation expansion (interior). Our analysis demonstrates the potential to improve the characterization of dynamic land cover classes, including economically important timber plantations, by integrating diverse remote sensing datasets. Critically, multi-source remote sensing provides an approach to leverage periodic forest inventory data for annual monitoring of managed forest landscapes. [ABSTRACT FROM AUTHOR]

The purpose of the EcoVeg classification approach is to describe the diversity of terrestrial ecosystems across the globe and inform decisions about conservation and resource management. The approach provides the scientific basis for the U.S. National Vegetation Classification, Canadian National Vegetation Classification and NatureServe's International Vegetation Classification, and has encouraged international and national collaborations elsewhere in the Americas. The approach is global, but most advanced in the western hemisphere, especially the U.S. and Canada. EcoVeg provides a consistent thematic framework to support extensive vegetation mapping across the U.S. and Latin America. The approach provides an 8-level hierarchy for natural types, with three upper (formation) levels, three mid (physiognomic-biogeographic-floristic) levels and 2 lower (floristic) levels, and a separate 8-level hierarchy for cultural types. Types are maintained through a review board to ensure consistent definition. All protocols use the best available scientific information, including plot data and secondary sources. Preferred plot sizes typically range from 0.01 to 0.1 ha (to 1.0 ha in tropical vegetation). Plot data include full species lists by strata with cover values, and supporting environmental and site data. The classification approach meets the need for a dynamic, 8- level catalog of types for all existing vegetation. The open, peer-review model allows for ongoing improvement by ecologists, while always providing comprehensive versions for users. Use of the best available scientific information ensures that the legacy of previous classification efforts is fully incorporated. Limitations include somewhat complicated names for types, limited availability of comprehensive plot data sets, and sparse testing beyond the Americas and Africa. The conservation or at-risk status of macrogroups, groups and associations are evaluated using both the IUCN Red List of Ecosystems and NatureServe Conservation Status Assessment protocols. [ABSTRACT FROM AUTHOR]

Satellite derived vegetation indices (VIs) are broadly used in ecological research, ecosystem modeling, and land surface monitoring. The Normalized Difference Vegetation Index (NDVI), perhaps the most utilized VI, has countless applications across ecology, forestry, agriculture, wildlife, biodiversity, and other disciplines. Calculating satellite derived NDVI is not always straight-forward, however, as satellite remote sensing datasets are inherently noisy due to cloud and atmospheric contamination, data processing failures, and instrument malfunction. Readily available NDVI products that account for these complexities are generally at coarse resolution; high resolution NDVI datasets are not conveniently accessible and developing them often presents numerous technical and methodological challenges. We address this deficiency by producing a Landsat derived, high resolution (30 m), long-term (30+ years) NDVI dataset for the conterminous United States. We use Google Earth Engine, a planetary-scale cloud-based geospatial analysis platform, for processing the Landsat data and distributing the final dataset. We use a climatology driven approach to fill missing data and validate the dataset with established remote sensing products at multiple scales. We provide access to the composites through a simple web application, allowing users to customize key parameters appropriate for their application, question, and region of interest. [ABSTRACT FROM AUTHOR]

Accurate distribution maps are essential to understand further and control the fast expansion of the introduced saltcedar ( Tamarix spp.) in the western U.S. Remote sensing imagery, due to its broad spatial and temporal coverage, is well-suited for this task. More specifically, images acquired during late fall and early winter are used for saltcedar identification as the plant's foliage turns a unique orange-yellow color (coloration) during this time of a year, which makes them easily distinguishable from other plant species. However, saltcedar coloration usually lasts for only several weeks, and the timing of this phenological event varies across space. Therefore, without prior knowledge of saltcedar phenology, it is often challenging to select the remote sensing image collected at the optimal time of the year to capture the distinctive spectral signature of the coloring saltcedar. Moreover, the timing of saltcedar coloration also varies within a single remote sensing scene making it inappropriate to use an entire scene as the mapping unit. In our study, we found that the timing of saltcedar peak coloration and leaf drop were linearly correlated with each other and we were able to model this relationship using the MODIS End of Season–Time (EoST) product and Landsat TM images. Guided by a per-pixel model estimation of saltcedar coloration date, we constructed composite Landsat images at two study sites along the Rio Grande River such that each pixel in the composite images was acquired during saltcedar peak coloration. Classification results showed that the phenology-informed composite image was better for saltcedar identification than the single scene image as it was able to capture the within-scene variations in saltcedar phenology. In addition, given our lack of training data for the model, the successful classification results obtained at the Middle Rio Grande River site also demonstrated the potential of our proposed method to be applied for large-scale saltcedar mapping. [ABSTRACT FROM AUTHOR]

Historical reference conditions provide important context for creating ecological restoration and management plans. The U.S. 19th Century Public Land Survey (PLS) records provide extensive ecological information for constructing such reference conditions. We used PLS records to reconstruct pre-Euro-American tree species cover class and vegetation structure types for the Midwest Driftless Area, a 55,000 km2 region currently experiencing multiple conservation threats. We related cover classes to soil texture, topographic roughness, and distance from waterway. Our analyses revealed that the landscape of the Driftless Area was mostly composed of savanna, with two large patches of closed forest and smaller, scattered patches of closed forest, open woodland, and prairie. The Driftless Area was heavily dominated by a variety of oak communities, with bur (Quercus macrocarpa), white (Q. alba), and black (Q. velutina) oak by far the most dominant species across the region. A variety of non-oak communities occurred within the closed forest patches, along rivers, or in smaller areas near the periphery of the region. The prevalence of savanna and oak communities indicates that fire played a key role in mediating historical landscape patterns and ecosystem processes in the region. Variation in soil texture, topographic roughness, and distance from waterways additionally contributed to the diversity of cover classes present prior to Euro-American settlement. Restoration practitioners can use our reconstructions to inform regional and site-specific restoration planning. Because oaks tend to be foundational species within ecosystems and are currently in decline throughout the Driftless Area, restoration activities that encourage these species are urgently needed. [ABSTRACT FROM AUTHOR]

The National Aeronautics and Space Administration's (NASA) proposed Soil Moisture Active Passive (SMAP) satellite mission (\sim2014) will include a radar system that will provide L-band multi-polarization backscatter at a constant incidence angle of 40^\circ. During the pre-launch phase of the project, there is a need for observations that will support the radar-based soil moisture algorithm development and validation. A valuable resource for providing these observations is the NASA Jet Propulsion Laboratory Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). However, SMAP will observe at a constant incidence angle of 40^ \circ, and UAVSAR collects data over a wide range of incidence angles (25 ^\circ–60^\circ). In this investigation, a technique was developed and tested for normalizing UAVSAR data to a constant incidence angle. The approach is based on a histogram matching procedure. The data used to develop and demonstrate this approach were collected as part of the Canadian Soil Moisture Experiment 2010 (CanEx-SM10). Land cover in the region included agriculture and forest. Evaluation was made possible by the acquisition of numerous overlapping UAVSAR flight lines that provided multiple incidence angle observations of the same locations. Actual observations at a 40^\circ incidence angle were compared to the normalized data to assess performance of the normalization technique. An optimum technique should be able to reduce the systematic error (Bias) to 0 dB and to lower the total root mean square error (RMSE) computed after correction to the level of the initial residual error (\RMSEres) present in the data set. The normalization approach developed here achieved both of these. Bias caused by the incidence angle variability was minimized to \sim 0 dB, whereas the residual error caused by instrument related random errors and amplitude fluctuations due to ground variability was reduced to approximately 3 dB for agricultural areas and 2.6 dB for forests; these values were consistent with the initial \RMSEres estimated using the un-corrected data. The residual error can be reduced further by aggregating the radar observations to a coarser grid spacing. The technique adequately adjusted the backscatter over the full swath width irrespective of the original incidence angle, polarization, and ground conditions (vegetation cover and soil moisture). In addition to providing a basis for fully exploiting UAVSAR (or similar aircraft systems) for SMAP algorithm development and validation, the technique could also be adapted to satellite radar systems. This normalization approach will also be beneficial in terms of reducing the number of flight lines required to cover a study area, which would eventually result in more cost-effective soil moisture field campaigns. [ABSTRACT FROM PUBLISHER]

Saltcedar is commonly recognized as one of the most threatening invasive species in the United States and has the potential to cause great environmental harm over the coming decade. Accurate mapping of saltcedar distribution and abundance in a timely manner plays a central role in assisting with effective control. Current studies have mostly concentrated on large-area detection with coarse-resolution remote sensing data. In this study, a comprehensive test was designed and carried out to examine the ability to integrate multitemporal and multiresolution imagery for differentiating saltcedar from other riparian vegetation types in the Rio Grande basin of Texas, including very high spatial resolution (QuickBird), hyperspectral resolution imagery (AISA), and moderate resolution satellite imagery (Landsat TM). Two types of analyses were fulfilled. First, five pixel-based classification methods were adopted for assessing the effectiveness of QuickBird and AISA for discerning saltcedar, respectively; that is, the maximum likelihood classifier (MLC), neural network classifier (NNC), support vector machine (SVM), spectral angle mapper (SAM), and maximum matching feature (MMF). Second, Landsat TM imagery was synthesized from AISA and tested for mapping the abundance of saltcedar with four linear spectral unmixing methods and three back-propagation neural network methods. Results indicate that AISA outperformed QuickBird imagery in differentiating saltcedar from other riparian vegetation species. SVM achieved the highest classification accuracy among the five classifiers. Linear spectral unmixing methods exhibited similar mapping accuracy to neural network methods in estimating the abundance of saltcedar at a spatial resolution of 30 by 30 m2 but with significantly better computing efficiency. [ABSTRACT FROM AUTHOR]

In this study we show that multiangle remote sensing is useful for increasing the accuracy of vegetation community type mapping in desert regions. Using images from the National Aeronautics and Space Administration (NASA) Multiangle Imaging Spectroradiometer (MISR), we compared roles played by Bidirectional Reflectance Distribution Function (BRDF) model parameters with those played by topographic parameters in improving vegetation community type classifications for the Jornada Experimental Range and the Sevilleta National Wildlife Refuge in New Mexico, USA. The BRDF models used were the Rahman-Pinty-Verstraete (RPV) model and the RossThin-LiSparseReciprocal (RTnLS) model. MISR nadir multispectral reflectance was considered as baseline because nadir observation is the most basic remote sensing observation. The BRDF model parameters and the topographic parameters were considered as additional data. The BRDF model parameters were obtained by inversion of the RPV model and the RTnLS model against the MISR multiangle reflectance data. The results of 32 classification experiments show that the BRDF model parameters are useful for vegetation mapping; they can be used to raise classification accuracies by providing information that is not available in the spectral-nadir domain, or from ancillary topographic parameters. This study suggests that the Moderate Resolution Imaging Spectroradiometer (MODIS) and MISR BRDF model parameter data products have great potential to be used as additional information for vegetation mapping. [ABSTRACT FROM AUTHOR]

The "Dust Bowl" drought of the 1930s was highly unusual for North America, deviating from the typical pattern forced by "La Nina" with the maximum drying in the central and northern Plains, warm temperature anomalies across almost the entire continent, and widespread dust storms. General circulation models (GCMs), forced by sea surface temperatures (SST5) from the 1930s, produce a drought, but one that is centered in southwestern North America and without the warming centered in the middle of the continent. Here, we show that the inclusion of forcing from human land degradation during the period, in addition to the anomalous SSTs, is necessary to reproduce the anomalous features of the Dust Bowl drought. The degradation over the Great Plains is represented in the GCM as a reduction in vegetation cover and the addition of a soil dust aerosol source, both consequences of crop failure. As a result of land surface feedbacks, the simulation of the drought is much improved when the new dust aerosol and vegetation boundary conditions are included. Vegetation reductions explain the high temperature anomaly over the northern U.S., and the dust aerosols intensify the drought and move it northward of the purely ocean forced drought pattern. When both factors are included in the model simulations, the precipitation and temperature anomalies are of similar magnitude and in a similar location compared with the observations. Human-induced land degradation is likely to have not only contributed to the dust storms of the 1930s but also amplified the drought, and these together turned a modest SST-forced drought into one of the worst environmental disasters the U.S. has experienced. [ABSTRACT FROM AUTHOR]

Three categories of digital wetland maps widely available in the United States were used to develop models relating wetlands to DOC: (1) wetlands mapped by the U.S. National Wetlands Inventory (NWI) (2) wetland vegetation cover mapped by the U.S. National Land Cover Dataset (NLCD), and (3) maps of hydric soils. Data extracted from these maps for 27 headwater catchments of the Ontonagon River in northern Michigan, USA were used with DOC concentrations measured in catchment streams to develop stepwise multiple regressions based on wetland area and type. The catchments of the 27 tributaries ranged in area from 2 to 66 km2 and wetlands constituted 10 to 53% of their area. Although all three databases provided regressions that were highly significant (p <0.001), the variance explained was greater for NWI maps (R 2 =0.75) than for NLCD (R 2 =0.61) or soil maps (R 2 =0.60). Wetland–stream relationships were strongest during September 2002, but were significant for nine out of ten dates sampled during subsequent seasons. The individual wetland type most highly correlated (r >0.62) with stream DOC concentrations was conifer peatland, represented on the NWI maps as Palustrine Needle-leaved Forest, the NLCD maps as woody wetland, and the soil maps as organic soils. For the NWI dataset, DOC was negatively correlated with area of palustrine emergent wetlands (i.e., sedge meadows and graminoid fens) and bog shrubs, inferring that these wetland types may be sinks for DOC. Because of the different effects of wetland vegetation types on DOC, a GIS data source such as the NWI which depicts those wetland types is superior for predicting landscape contributions to stream DOC concentrations. [Copyright &y& Elsevier]

A spatially explicit dataset of aboveground live forest biomass was made from ground measured inventory plots for the conterminous U.S., Alaska and Puerto Rico. The plot data are from the USDA Forest Service Forest Inventory and Analysis (FIA) program. To scale these plot data to maps, we developed models relating field-measured response variables to plot attributes serving as the predictor variables. The plot attributes came from intersecting plot coordinates with geospatial datasets. Consequently, these models serve as mapping models. The geospatial predictor variables included Moderate Resolution Imaging Spectrometer (MODIS)-derived image composites and percent tree cover; land cover proportions and other data from the National Land Cover Dataset (NLCD); topographic variables; monthly and annual climate parameters; and other ancillary variables. We segmented the mapping models for the U.S. into 65 ecologically similar mapping zones, plus Alaska and Puerto Rico. First, we developed a forest mask by modeling the forest vs. nonforest assignment of field plots as functions of the predictor layers using classification trees in See5©. Secondly, forest biomass models were built within the predicted forest areas using tree-based algorithms in Cubist©. To validate the models, we compared field-measured with model-predicted forest/nonforest classification and biomass from an independent test set, randomly selected from available plot data for each mapping zone. The estimated proportion of correctly classified pixels for the forest mask ranged from 0.79 in Puerto Rico to 0.94 in Alaska. For biomass, model correlation coefficients ranged from a high of 0.73 in the Pacific Northwest, to a low of 0.31 in the Southern region. There was a tendency in all regions for these models to over-predict areas of small biomass and under-predict areas of large biomass, not capturing the full range in variability. Map-based estimates of forest area and forest biomass compared well with traditional plot-based estimates for individual states and for four scales of spatial aggregation. Variable importance analyses revealed that MODIS-derived information could contribute more predictive power than other classes of information when used in isolation. However, the true contribution of each variable is confounded by high correlations. Consequently, excluding any one class of variables resulted in only small effects on overall map accuracy. An estimate of total C pools in live forest biomass of U.S. forests, derived from the nationwide biomass map, also compared well with previously published estimates. [Copyright &y& Elsevier]

Reliable information about the geographic distribution and abundance of major plant functional types (PFTs) around the world is increasingly needed for global change research. Using remote sensing techniques to map PFTs is a relatively recent field of research. This paper presents a method to map PFTs from the Moderate Resolution Imaging Spectroradiometer (MODIS) data using a multisource evidential reasoning (ER) algorithm. The method first utilizes a suite of improved and standard MODIS products to generate evidence measures for each PFT class. The multiple lines of evidence computed from input data are then combined using Dempster''s Rule of combination. Finally, a decision rule based on maximum support is used to make classification decisions. The proposed method was tested over the states of Illinois, Indiana, Iowa, and North Dakota, USA where crops dominate. The Cropland Data Layer (CDL) data provided by the United States Department of Agriculture were employed to validate our new PFT maps and the current MODIS PFT product. Our preliminary results suggest that multisource data fusion is a promising approach to improve the mapping of PFTs. For several major PFT classes such as crop, trees, and grass and shrub, the PFT maps generated with the ER method provide greater spatial details compared to the MODIS PFT. The overall accuracies increased for all the four states, with the biggest improvement occurring in Iowa from 51% (MODIS) to 64% (ER). The overall kappa statistic also increased for all the four states, with the biggest improvement occurring in Iowa from 0.03 (MODIS) to 0.38 (ER). The paper concludes with a discussion of several methodological issues pertaining to the further improvement of the ER approach. [Copyright &y& Elsevier]

Aim The purposes of this study were to develop a Geographic Information System and spatial analytical methodology to reconstruct and represent the presettlement vegetation in a spatially continuous manner over large areas and to investigate vegetation–site relationships before widespread changes of the vegetation had taken place. Location The study area was the Holland Land Company Purchase in western New York, a 14,400 km2 area extending across the physiographic provinces of the Erie–Ontario Lowlands and the Appalachian Uplands. Methods Bearing-tree records from the Holland Land Company township surveys of western New York in c. 1800 were collected and analysed. The geostatistical method of indicator kriging was used to map spatially continuous representations of individual tree species. Rule-based and statistically clustered approaches were used to analyse and classify the reconstructed tree species distributions in order to obtain the vegetation association distribution. Contingency table analysis was conducted to quantify species relationships with soil conditions. Results The presettlement vegetation at both the tree species and the vegetation association levels were easier to interpret and visually more effective as a spatially continuous representation than as a discontinuous distribution of symbols. The results for tree species were probability occurrences of species distribution, showing spatial patterns that were not apparent in discrete maps of points or in summary tables of species frequencies. Analysis of the 8792 bearing trees suggested the dominance of American beech ( Fagus grandifolia) and sugar maple ( Acer saccharum) in the forest composition 200 years ago. Both soil drainage and texture were important site determinants of the vegetation in western New York. The rule-based and statistically clustered approaches had the advantage of summarizing vegetation compositional patterns in a single image, thus avoiding the need to delineate manually and subjectively the location of boundaries between adjacent vegetation associations. Main conclusions The study offers more insights into the spatial pattern of presettlement forests in western New York than do prior studies. The spatially continuous representation could also enable the comparison of vegetation distribution from data sources that have different sampling schemes, for example the comparison of presettlement vegetation from the presettlement land survey records with current vegetation from modern forest inventories. The results are of value, providing a useful benchmark against which to examine vegetation change and the impacts of human land use. [ABSTRACT FROM AUTHOR]

Abstract: The Shuttle Radar Topography Mission distinguished itself as the first near-global spaceborne mission to demonstrate direct sensitivity to vertical vegetation structure. Whether this sensitivity is viewed as exploitable signal or unwanted bias, a great deal of interest exists in retrieving vegetation canopy height information from the SRTM data. This study presents a comprehensive application-specific assessment of SRTM data quality, focusing on the characterization and mitigation of two primary sources of relative vertical error: uncompensated Shuttle mast motion and random phase noise. The assessment spans four test sites located in the upper Midwestern United States and examines the dependence of data quality on both frequency, i. E. , C-band vs. X-band, and the number of acquired datatakes. The results indicate that the quality of SRTM data may be higher than previously thought. Novel mitigation strategies include a knowledge-based approach to sample averaging, which has the potential to reduce phase noise error by 43 to 80%. The strategies presented here are being implemented as part of an ongoing effort to produce regional-to continental-scale estimates of vegetation canopy height within the conterminous U. S. [Copyright &y& Elsevier]

Aim In this paper, we explore the relationship between pollen concentration in surface soil and extant perennial vegetation across a rapidly urbanizing arid ecosystem along with its surrounding agricultural and native Sonoran Desert land. We also investigate whether taxa behave as expected from known pollination characteristics (e.g. anemophilous vs. zoophilous) and whether the pollen–plant relationship differs between the undeveloped desert and agro-urban regions. Location We used a probability-based sampling scheme at 200, 900-m2 sites across the Central Arizona–Phoenix region, a 6400-km2 area of the south-western USA. Methods Pollen–plant abundances were mapped across the study area using interpolation techniques, summary bar charts and scatterplots, then two statistical approaches were applied to examine the data quantitatively. Firstly, we used regression analysis of paired pollen concentration and plant cover data; secondly,pc-ord was used on a cross-matrix (contingency table) containing the presence/absence scores of both pollen and plants for each taxon at each site. Results Mapping and quantitative analysis revealed that pollen–plant relationships do vary both between anemophilous and zoophilous groupings and by individual taxon. They also revealed that distribution patterns of both pollen and plant abundance vary consistently across the three main landscape types (desert, urban, agricultural) and that pollen taxa groupings are consistent with pollination method and source plant distribution regardless of landscape type. Main conclusions These findings provide a broad range of comparative data to facilitate palynological reconstruction of past vegetation and aid in assessing types of prehistoric impacts on this vegetation. They also document that significant variation can occur in pollen rain across a city, even for taxa with widely distributed pollen, leading to the conclusion that forecasting pollen distribution patterns for allergy-related pollen types using only one or a few pollen traps is likely to entail substantial error. [ABSTRACT FROM AUTHOR]

The task of modeling the distribution of a large number of tree species under future climate scenarios presents unique challenges. First, the model must be robust enough to handle climate data outside the current range without producing unacceptable instability in the output. In addition, the technique should have automatic search mechanisms built in to select the most appropriate values for input model parameters for each species so that minimal effort is required when these parameters are fine-tuned for individual tree species. We evaluated four statistical models—Regression Tree Analysis (RTA), Bagging Trees (BT), Random Forests (RF), and Multivariate Adaptive Regression Splines (MARS)—for predictive vegetation mapping under current and future climate scenarios according to the Canadian Climate Centre global circulation model. To test, we applied these techniques to four tree species common in the eastern United States: loblolly pine ( Pinus taeda), sugar maple ( Acer saccharum), American beech ( Fagus grandifolia), and white oak ( Quercus alba). When the four techniques were assessed with Kappa and fuzzy Kappa statistics, RF and BT were superior in reproducing current importance value (a measure of basal area in addition to abundance) distributions for the four tree species, as derived from approximately 100,000 USDA Forest Service’s Forest Inventory and Analysis plots. Future estimates of suitable habitat after climate change were visually more reasonable with BT and RF, with slightly better performance by RF as assessed by Kappa statistics, correlation estimates, and spatial distribution of importance values. Although RTA did not perform as well as BT and RF, it provided interpretive models for species whose distributions were captured well by our current set of predictors. MARS was adequate for predicting current distributions but unacceptable for future climate. We consider RTA, BT, and RF modeling approaches, especially when used together to take advantage of their individual strengths, to be robust for predictive mapping and recommend their inclusion in the ecological toolbox. [ABSTRACT FROM AUTHOR]

Weighted estimation formulas are developed for producing stratified estimates of means and variances where data come from plots that can contain multiple forest conditions. Each plot is mapped to allow the analyst to focus on specific forest types or conditions. The weights required to accommodate mapped plots are somewhat more complicated than the weights for unmapped plots. In particular, these weights depend on the mapped condition of interest. The implication is that a single plot weight or expansion factor will not suffice for all analyses as it does for unmapped plots. The methods are demonstrated using USDA Forest Service inventory data. [ABSTRACT FROM AUTHOR]

Multiple endmember spectral mixture analysis (MESMA) models mixed spectra as a linear combination of endmembers that are allowed to vary in number and type on a per pixel basis. For modeling an image using MESMA, a parsimonious set of endmembers is desirable for computational efficiency and operational simplicity. This paper presents a method of selecting endmembers from a spectral library for use in MESMA. Endmember average root mean square error (EAR) uses MESMA to determine the average error of an endmember modeling spectra within its land cover class. The minimum EAR endmember is the most representative endmember for a land cover class within the spectral library and can be used to model the larger image.These techniques were used to map land cover, including four dominant vegetation species, soil, and senesced grass, in the Santa Ynez Mountains above Santa Barbara, CA, USA. Image spectra were extracted from a 20-m resolution airborne visible infrared imaging spectrometer (AVIRIS) reflectance image using reference polygons and combined into a library of 915 spectra. Possible confusion between land cover classes was determined using the class average RMSE (CAR). EAR was used to select the single most representative endmember within each land cover class. The six minimum EAR endmembers were used to map the AVIRIS image. Land cover class accuracy was assessed at 88.6%. Using a fractional accuracy assessment, undermodeling of dominant land cover classes and overmodeling of absent land cover classes was found at the pixel scale. Land cover mapped using the minimum EAR endmembers represents a substantial improvement in accuracy over previous efforts. [Copyright &y& Elsevier]

The objective of this study was to test the applicability of using Normalized Difference Vegetation Index (NDVI) values derived from a temporal sequence of six Landsat Thematic Mapper (TM) scenes to map fuel models for Yosemite National Park, USA. An unsupervised classification algorithm was used to define 30 unique spectral-temporal classes of NDVI values. A combination of graphical, statistical and visual techniques was used to characterize the 30 classes and identify those that responded similarly and could be combined into fuel models. The final classification of fuel models included six different types: short annual and perennial grasses, tall perennial grasses, medium brush and evergreen hardwoods, short-needled conifers with no heavy fuels, long-needled conifers and deciduous hardwoods, and short-needled conifers with a component of heavy fuels. The NDVI, when analysed over a season of phenologically distinct periods along with ancillary data, can elicit information necessary to distinguish fuel model types. Fuels information derived from remote sensors has proven to be useful for initial classification of fuels and has been applied to fire management situations on the ground. [ABSTRACT FROM AUTHOR]

This study assesses the ability of multitemporal Landsat Thematic Mapper (TM) data and the normalized difference vegetation index (NDVI) to spectrally separate grazed cool season and warm season grassland cover types in Douglas County, Kansas. Biophysical data collected during the summer of 1997 suggest that differences in the per cent of total living vegetation cover, per cent of senescent vegetation, and proportion of forb cover between the two grassland cover types could make cool season and warm season grassland cover types spectrally distinct The results show that the two grassland cover types were spectrally different in several spring (May) and mid-summer (July) bands, but not in any fall (September) bands. Furthermore, the two grassland cover types could be discriminated with a high level of accuracy. Accuracy assessments of the three single dates showed that the mid-summer (July) image and NDVI discriminated between the grassland cover types most accurately (81.8%). The multitemporal TM and NDVI data did not improve the spectral discrimination of the two grassland cover types over the mid-summer image or NDVI and had classification accuracy levels of 63.6% and 68.2%, respectively. [ABSTRACT FROM AUTHOR]

Hyperspectral sensors can make narrow-band measurements for several hundred regions of the electromagnetic spectrum, and with increasing frequency, multi-dates of remotely sensed data are being used for Earth observation purposes. The use of more spectral bands is creating greater demand for larger computer storage capacity and faster data processors. This study evaluates the use of raw Thematic Mapper (TM) band combinations and several derived vegetation indices to determine optimal vegetation indices and band combinations for discriminating among six grassland management practices in eastern Kansas. Results showed that among the transformed dataset, the Greenness Vegetation Index was found to be the best for discriminating among grassland management types. When evaluating the raw TM bands, TM4 (NIR) was always selected in Discriminate Analysis as the best discriminating variable. There is no significant improvement in grassland discrimination by using a combination of the raw TM bands and the vegetation indices. Increasing the number of TM bands by using multiple dates of imagery will improve discrimination accuracy up to a point, but the use of too many bands (greater than 10 or 12) can actually decrease discrimination accuracy. [ABSTRACT FROM AUTHOR]

Discusses the use of Geographic Information Systems to integrate historic maps of vegetation and soils with more digital soils and wetland maps and multi-spectral satellite imagery to estimate changes in the area and distribution of natural areas in Indianapolis-Marion County, Indiana. Details of the mapping tree cover in urban environments; Details of the pre-European settlement vegetation communities.

This paper presents a methodology for the development of a high-resolution (30-m), standardized biogenic volatile organic compound (BVOC) emissions inventory and a subsequent application of the methodology to Tucson, AZ. The region's heterogeneous vegetation cover cannot be modeled accurately with low-resolution (e.g., 1-km) land cover and vegetation information. Instead, local vegetation data are used in conjunction with multispectral satellite data to generate a detailed vegetation-based landcover database of the region. A high-resolution emissions inventory is assembled by associating the vegetation data with appropriate emissions factors. The inventory reveals a substantial variation in BVOC emissions across the region, resulting from the region's diversity of both native and exotic vegetation. The importance of BVOC emissions from forest lands, desert lands, and the urban forest changes according to regional, metropolitan, and urban scales. Within the entire Tucson region, the average isoprene, monoterpene, and OVOC fluxes observed were 454, 248, and 91 µg/m²/hr, respectively, with forest and desert lands emitting nearly all of the BVOCs. Within the metropolitan area, which does not include the forest lands, the average fluxes were 323, 181, and 70 µg/m²/hr, respectively. Within the urban area, the average fluxes were 801, 100, and 100 µg/m²/hr, respectively, with exotic trees such as eucalyptus, pine, and palm emitting most of the urban BVOCs. The methods presented in this paper can be modified to create detailed, standardized BVOC emissions inventories for other regions, especially those with spatially complex vegetation patterns. [ABSTRACT FROM AUTHOR]

Traditionally, the classes in thematic maps have been treated as crisp sets, using classical set theory. In this formulation, map classes are assumed to be mutually exclusive and exhaustive. This approach limits the ability of thematic maps to represent the continuum of variation found in most landscapes. Substitution of fuzzy sets allows more flexibility for treatment of map classes in the areas of accuracy assessment and area estimation. Accuracy assessment methods based on fuzzy sets allow consideration of the magnitude of errors and assessment of the frequency of ambiguity in map classes. An example of an accuracy assessment from a vegetation map of the Plumas National Forest illustrates the implementation of these methods. Area estimation based on fuzzy sets and using accuracy assessment data allows estimation of the area of classes as a function of levels of class membership. The fuzzy area estimation methods are an extension of previous methods presented by Card (1982). One interesting result is that the sum of the areas of the classes in a map need not be unity. This approach allows a wider range of queries within a GIS. [ABSTRACT FROM AUTHOR]

A six-step integrated vegetation mapping approach is described for making a small-scale (1:4 million) map of northern Alaska. The method uses two primary maps: (1) a Phytogeographic subzones and Floristic subprovinces Map (PFM) adjusted to Advanced Very High Resolution Radiometer false colour infrared (AVHRR CIR) imagery, and (2) an Integrated Vegetation-Complex Map (IVCM). The IVCM map-polygon boundaries are guided by information from a variety of remote-sensing data (AVHRR imagery, maximum greenness maps and classified images) and hard-copy source maps (surficial geology, bedrock geology, soils, percentage water cover). The map-polygon boundaries are integrated so that they conform to terrain features that are interpretable on the AVHRR CIR. The PFM and IVCM are overlaid in a geographic information system (GIS), and a series of derived maps is created through the use of look-up tables. Northern Alaska is a prototype area for the Circumpolar Arctic Vegetation Mapping (CAVM) project, which has a goal of producing a new vegetation map of the region north of the arctic tree line by the year 2001. The method could be modified and adapted to any region of the Arctic based on locally available information. [ABSTRACT FROM AUTHOR]

Summary Aim Our main goals were to develop a map of the life zones for the conterminous United States, based on the Holdridge Life Zone system, as a tool for ecosystem mapping, and to compare the map of Holdridge life zones with other global vegetation classification and mapping efforts. Location The area of interest is the forty-eight contiguous states of the United States. Methods We wrote a PERL program for determining life zones from climatic data and linked it to the image processing workbench (IPW). The inputs were annual precipitation (Pann), biotemperature (Tbio), sea-level biotemperature (T0bio), and the frost line. The spatial resolution chosen for this study (2.5 arc-minute for classification, 4-km for mapping) was driven by the availability of current state-of-the-art, accurate and reliable precipitation data. We used the Precipitation-elevation Regressions on Independent Slopes Model, or PRISM, output for the contiguous United States downloaded from the Internet. The accepted standard data for air temperature surfaces were obtained from the Vegetation/Ecosystem Modelling and Analysis Project (VEMAP). This data set along with station data obtained from the National Climatic Data Center for the US, were used to develop all temperature surfaces at the same resolution as the Pann. Results The US contains thirty-eight life zones (34% of the world's life zones and 85% of the temperate ones) including one boreal, twelve cool temperate, twenty warm temperate, four subtropical, and one tropical. Seventy-four percent of the US falls in the ‘basal belt’, 18% is montane, 8% is subalpine, 1% is alpine, and < 0.1% is nival. The US ranges from superarid to superhumid, and the humid province is the largest (45% of the US). The most extensive life zone is the warm temperate moist forest, which covers 23% of the country. We compared the Holdridge life zone map with output from the BIOME model, Bailey's ecoregions, Küchler potential vegetation, and land cover, all aggregated to four cover classes. Despite differences in the goals and methods for all these classification systems, there was a very good to excellent agreement among them for forests but poor for grasslands, shrublands, and nonvegetated lands. Main conclusions We consider the life zone approach to have many strengths for ecosystem mapping because it is based on climatic driving factors of ecosystem processes and recognizes ecophysiological responses of plants; it is hierarchical and allows for the use of other mapping criteria at the association and successional levels of analysis; it can be expanded or contracted without losing functional continuity among levels of ecological complexity; it is a relatively simple system based on few empirical data; and it uses objective mapping criteria. [ABSTRACT FROM AUTHOR]

Aboveground biomass was estimated on the shortgrass steppe of Eastern Colorado using Landsat TM Tasseled Cap green vegetation index (GVI), brightness index (BI), and wetness index (WI), the normalized difference vegetation index (NDVI) and the red waveband (RED), for two grazing treatments (moderately grazed or ungrazed). Field measurements of standing crop were obtained on six sites per grazing treatment. Ordinary least squares regression models of biomass as a function of one or more indices were tested for grazed, ungrazed, and combined grazed and ungrazed data. Biomass from grazed sites was linearly related to GVI, NDVI, WI, and RED indices (R2=0.62–0.67). Ungrazed sites produced no significant relations. With combined ungrazed and grazed data, biomass was not significantly related to GVI, NDVI, WI, or BI, and was poorly related to the RED index (R2=0.35). When grazing treatments were treated as dummy variables for the combined data, the RED index was moderately related to biomass (R2=0.70). These results suggest that information about grazing utilization is useful for estimating aboveground biomass in rangelands. The RED index appears to be sensitive to biomass variations for green vegetation and to a lesser extent dry or senescent vegetation when relatively bright soil backgrounds are present which is often the case for semi-arid environments such as the shortgrass steppe. [ABSTRACT FROM AUTHOR]

Explores the historical interpretation of the myth of the Alabama Black Belt. Markers of vegetation map; Results of geological surveys; Confusion over soil prairies.

This paper describes a method of mapping riparian vegetation in semi-arid to arid environments using the Landsat normalized difference vegetation index (NDVI). The method successfully identified a range of riparian community types across the entire state of Nevada, USA, which spans 7 degrees of latitude and almost 4000 m of elevation. The landscape was stratified into units of similar elevation and solar exposure, and riparian areas were identified as having anomalously high NDVI within a local neighborhood. Thousands of calibration points were used in a simplex optimization to select the spatial neighborhood, the elevation and insolation strata, the minimum NDVI to be considered as potentially riparian, the number of standard deviations from the mean for an anomaly to be classified as riparian, and a limit on upslope position. Mapping of subpixel riparian corridors was improved by applying a directional high-pass filter to the NDVI data. Irrigated areas in agricultural and urban areas were removed based on land ownership maps and manual editing. The final map was tested with 400 independent test points: producer's accuracy was 84.6% and user's accuracy was 93.5%. This method should be broadly useful for mapping riparian features across large and complex regions with arid to semi-arid environments. [ABSTRACT FROM AUTHOR]