Vegetative and geologic mapping of the Western Seward Peninsula, Alaska, based on ERTS-1 imagery
Vegetative and geologic mapping of Western Seward Peninsula, Alaska, based on ERTS-1 imagery
Anderson, J. H.↗
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Vegetation species composition and canopy architecture information expressed in leaf water absorption measured in the 1000 nm and 2200 spectral region by an imaging spectrometer
Plant species composition and plant architectural attributes are critical parameters required for the measuring, monitoring, and modeling of terrestrial ecosystems. Remote sensing is commonly cited as an important tool for deriving vegetation properties at an appropriate scale for ecosystem studies, ranging from local to regional and even synoptic scales. Classical approaches rely on vegetation indices such as the normalized difference vegetation index (NDVI) to estimate biophysical parameters such as leaf area index or intercepted photosynthetically active radiation (IPAR). Another approach is to apply a variety of classification schemes to map vegetation and thus extrapolate fine-scale information about specific sites to larger areas of similar composition. Imaging spectrometry provides additional information that is not obtainable through broad-band sensors and that may provide improved inputs both to direct biophysical estimates as well as classification schemes. Some of this capability has been demonstrated through improved discrimination of vegetation, estimates of canopy biochemistry, and liquid water estimates from vegetation. We investigate further the potential of leaf water absorption estimated from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data as a means for discriminating vegetation types and deriving canopy architectural information. We expand our analysis to incorporate liquid water estimates from two spectral regions, the 1000-nm region and the 2200-nm region. The study was conducted in the vicinity of Jasper Ridge, California, which is located on the San Francisco peninsula to the west of the Stanford University campus. AVIRIS data were acquired over Jasper Ridge, CA, on June 2, 1992, at 19:31 UTC. Spectra from three sites in this image were analyzed. These data are from an area of healthy grass, oak woodland, and redwood forest, respectively. For these analyses, the AVIRIS-measured upwelling radiance spectra for the entire Jasper Ridge scene were transformed to apparent surface reflectance using a radiative transfer code-based inversion algorithm.
Green, Robert O.↗
Land use studies with Skylab data, August 1974
The author has identified the following significant results. Capabilities of Skylab photographic data suggest significant applications for: (1) identification and mapping of all primary, most secondary, and many tertiary land use classes; (2) stratification of the landscape for more detailed sampling; and (3) rapid updating of existing land use and vegetation maps subscaled at 1:25,000 and smaller with manual interpretation techniques. Automated thematic mapping of land use categories with electronic data processing techniques is feasible with the S-192 multispectral scanner, despite the high noise levels in many channels.
Simonett, D. S.↗
Identification and mapping of soils, vegetation and water resources by computer analysis of ERTS MSS data
Identification and mapping of soils, vegetation, and water resources in Lynn County, Texas by computer analysis of ERTS-1 MSS data
Baumgardner, M. F.↗
Vegetation boundaries on ERTS-1 imagery
Comparison of systems corrected ERTS-1 imagery and microdensitometer scan printouts derived from them with base vegetation maps and current 1:120,000 and 1:60,000 scale imagery of the Great Smoky Mountains is in progress. It reveals good separation of the spruce-fir forest from the other forest areas but band seven does not distinguish between hardwood and heath dominated vegetation.
Deselm, H. R.↗
Advances in Hyperspectral Image Classification Methods for Vegetation and Agricultural Cropland Studies
Hyperspectral data are becoming more widely available via sensors on airborne and unmanned aerial vehicle (UAV) platforms, as well as proximal platforms. While space-based hyperspectral data continue to be limited in availability, multiple spaceborne Earth-observing missions on traditional platforms are scheduled for launch, and companies are experimenting with small satellites for constellations to observe the Earth, as well as for planetary missions. Land cover mapping via classification is one of the most important applications of hyperspectral remote sensing and will increase in significance as time series of imagery are more readily available. However, while the narrow bands of hyperspectral data provide new opportunities for chemistry-based modeling and mapping, challenges remain. Hyperspectral data are high dimensional, and many bands are highly correlated or irrelevant for a given classification problem. For supervised classification methods, the quantity of training data is typically limited relative to the dimension of the input space. The resulting Hughes phenomenon, often referred to as the curse of dimensionality, increases potential for unstable parameter estimates, overfitting, and poor generalization of classifiers. This is particularly problematic for parametric approaches such as Gaussian maximum likelihood–based classifiers that have been the backbone of pixel-based multispectral classification methods. This issue has motivated investigation of alternatives, including regularization of the class covariance matrices, ensembles of weak classifiers, development of feature selection and extraction methods, adoption of nonparametric classifiers, and exploration of methods to exploit unlabeled samples via semi-supervised and active learning. Data sets are also quite large, motivating computationally efficient algorithms and implementations. This chapter provides an overview of the recent advances in classification methods for mapping vegetation using hyperspectral data. Three data sets that are used in the hyperspectral classification literature (e.g., Botswana Hyperion satellite data and AVIRIS airborne data over both Kennedy Space Center and Indian Pines) are described in Section 3.2 and used to illustrate methods described in the chapter. An additional high-resolution hyperspectral data set acquired by a SpecTIR sensor on an airborne platform over the Indian Pines area is included to exemplify the use of new deep learning approaches, and a multiplatform example of airborne hyperspectral data is provided to demonstrate transfer learning in hyperspectral image classification. Classical approaches for supervised and unsupervised feature selection and extraction are reviewed in Section 3.3. In particular, nonlinearities exhibited in hyperspectral imagery have motivated development of nonlinear feature extraction methods in manifold learning, which are outlined in Section 3.3.1.4. Spatial context is also important in classification of both natural vegetation with complex textural patterns and large agricultural fields with significant local variability within fields. Approaches to exploit spatial features at both the pixel level (e.g., co-occurrence–based texture and extended morphological attribute profiles [EMAPs]) and integration of segmentation approaches (e.g., HSeg) are discussed in this context in Section 3.3.2. Recently, classification methods that leverage nonparametric methods originating in the machine learning community have grown in popularity. An overview of both widely used and newly emerging approaches, including support vector machines (SVMs), Gaussian mixture models, and deep learning based on convolutional neural networks is provided in Section 3.4. Strategies to exploit unlabeled samples, including active learning and metric learning, which combine feature extraction and augmentation of the pool of training samples in an active learning framework, are outlined in Section 3.5. Integration of image segmentation with classification to accommodate spatial coherence typically observed in vegetation is also explored, including as an integrated active learning system. Exploitation of multisensor strategies for augmenting the pool of training samples is investigated via a transfer learning framework in Section 3.5.1.2. Finally, we look to the future, considering opportunities soon to be provided by new paradigms, as hyperspectral sensing is becoming common at multiple scales from ground-based and airborne autonomous vehicles to manned aircraft and space-based platforms.
Pasolli, Edoardo↗
NGEE Arctic Phase 4 Plant Functional Type Framework for Pan-Arctic Vegetation
The NGEE-Arctic research team identified a common set of hierarchical plant functional types (PFTs) for pan-arctic vegetation that we will use across our research activities. Interdisciplinary work within a large team requires agreement regarding levels of functional organization so that knowledge, data, and technologies can be shared and combined effectively. The team has identified plant functional types as a crucial area where such interoperability is needed. PFTs are used to represent plant pools and fluxes within models, summarize observational data, and map vegetation across the landscape. Within each of these applications, varying levels of PFT specificity are needed according to the specific scientific research goal, computational limitations, and data availability. By agreeing on a specific hierarchical framework for grouping variables in our vegetation data, we ensure the resulting research products will be robust, flexible, and scalable. In this document, we lay out the agreed upon PFT framework with definitions and references to existing literature. Table 1 included in the "NGA700_Phase4PFTFramework_about*" file outlines the relationship between NGEE-Arctic Phase 4, Tier 1 PFTs and the PFTs used within prominent arctic literature as well as publications by the NGEE-Arctic team during phases 1-3.This dataset consists of a table detailing a hierarchical PFT framework that spans 4 tiers with the most granular PFTs listed in tier 1 and the most general PFTs in tier 4. The PFTs within each tier has a single column in the dataset where the PFTs are named and a separate column where the characteristics used to define that PFT are listed. Grey fill of the cells is used to indicate where a given PFT starts to “lose” tier 1 details as you look from left to right. Note the excel file has merged cells to indicate grouping of PFTs across the Tiers- it will not translate into a delimited filetype (.csv, .txt, etc) without modification thus the hierarchical PFT framework table is available in three different file formats: 1) NGA700_Phase4PTS.xlsx – maintains the merged cells and grey fill; 2) NGA700_Phase4PTS.csv – merged cells are split, and grey fill is removed; 3) NGA700_Phase4PTS.pdf – image of the table with merged cells and grey fill. Metadata document included as a *.pdf and file-level metadata and data dictionary as *.csv files.
54 ENVIRONMENTAL SCIENCES↗
Mapping Ungulate Habitats in Yellowstone National Park with Airborne Hyperspectral Data
Mapping vegetation habitats of ungulates (e.g., bison, elk, and deer) is critical to the development of efficient wildlife management and monitoring practices in Yellowstone National Park. Image endmembers were chosen using the ENVI minimum noise fraction, pixel purity index, N-dimensional visualizer approach. The spectral angle mapper algorithm was used to classify the image. This process was applied to low altitude AVIRIS and Probe-1 hyperspectral imagery of the Lamar River/Soda Butte Creek confluence to map several ungulate habitats (e.g., grasses, sedge, sage, aspen, willow, and cottonwood. The results are being compared to field measurements and large-scale color infrared aerial photography to assess mapping accuracy. The use of AVIRIS and Probe-1 data enabled the examination of hyperspectral data collected at different spatial and spectral resolutions.
Terrie, Gregory↗
Advanced Systems Map, Monitor, and Manage Earth's Resources
SpecTIR LLC, headquartered in Reno, Nevada, is recognized for innovative sensor design, on-demand hyperspectral data collection, and image-generating products for business, academia, and national and international governments. SpecTIR's current vice president of business development has brought a wealth of NASA-related research experience to the company, as the former principal investigator on a NASA-sponsored hyperspectral crop-imaging project. This project, made possible through a Small Business Technology Transfer (STTR) contract with Goddard Space Flight Center, aimed to enhance airborne hyperspectral sensing and ground-truthing means for crop inspection in the Mid-Atlantic region of the United States. Areas of application for such technology include precision farming and irrigation; oil, gas, and mineral exploration; pollution and contamination monitoring; wetland and forestry characterization; water quality assessment; and submerged aquatic vegetation mapping. Today, SpecTIR maintains its relationship with Goddard through programs at the University of Maryland in College Park, Maryland, and at the U.S. Department of Agriculture campus in Beltsville, Maryland. Additionally, work continues on the integration of hyperspectral data with LIDAR systems and other commercial-off-the-shelf technologies.
Source record↗
Restoring Historic Forest Disturbance Frequency Would Partially Mitigate Droughts in the Central Sierra Nevada Mountains
Forest thinning and prescribed fire are expected to improve the climate resilience and water security of forests in the western U.S., but few studies have directly modeled the hydrological effects of multi-decadal landscape-scale forest disturbance. By updating a distributed process-based hydrological model (DHSVM) with vegetation maps from a distributed forest ecosystem model (LANDIS-II), we simulate the water resource impacts of forest management scenarios targeting partial or full restoration of the pre-colonial disturbance return interval in the central Sierra Nevada mountains. In a fully restored disturbance regime that includes fire, thinning, and insect mortality, reservoir inflow increases by 4%–9% total and 8%–14% in dry years. At sub-watershed scales (10–100 km2), thinning dense forests can increase streamflow by >20% in dry years. In a thinner forest, increased understory transpiration compensates for decreased overstory transpiration. Consequentially, 73% of streamflow gains are attributable to decreased overstory rain and snow interception loss. Thinner forests can increase headwater peak flows, but reservoir-scale peak flows are almost exclusively influenced by climate. Uncertainty in future precipitation causes high uncertainty in future water yield, but the additional water yield attributable to forest disturbance is about five times less sensitive to annual precipitation uncertainty. This partial decoupling of the streamflow disturbance response from annual precipitation makes disturbance especially valuable for water supply during dry years. Our study can increase confidence in the water resource benefits of restoring historic forest disturbance frequencies in the central Sierra Nevada mountains, and our modeling framework is widely applicable to other forested mountain landscapes.
Boardman, Eli N. [University of Nevada, Reno, NV (↗
Vegetation analysis with radar imagery.
Radar imagery compilation of vegetation maps, noting extent of capabilities and results obtained
GEODETIC SURVEYING↗
Vegetation analysis with radar imagery
Vegetation maps prepared from radar imagery obtained over several climatic environments
Morain, S. A.↗
Applications of remote sensor data by state and Federal user agencies in Arizona
The use of NASA high altitude aerial photography of south eastern Arizona to develop a natural resources information system for Federal lands is discussed. The data are to be used by local, State, and Federal agencies in connection with geologic mapping projects, water resources investigations, and land use studies to determine the alignment of a proposed major aqueduct. In addition, the data are used to confirm land ownership boundaries, detect changes in land use, and legislative reappointment mapping. Other applications include mapping vegetive cover, evaluation of changes in wildlife habitat, location of deer kills, and as a base for recording telemetry data from radio-collared big game animals.
Schumann, H. H.↗
An evaluation of acquired data as a tool for management of wildlife habitat in Alaska
The author has identified the following significant results. Density sliced and digitized imagery of the Kuskokwin/Yukon Delta were analyzed. Color coded images of the isodensity displays were compared with existing vegetation maps of the ERTS-1 frames for the Yukon/Kuskokwin area. A high degree of positive correlation was found to exist between the ERTS-1 image and the conventionally prepared maps. Hydrologic phenomena were also analyzed. Digitization on South Dakota State Remote Sensing Center's SADE system provide some discrimination among several large lakes in the subject area. However, interpretation must await ground observations and depth measurements. An attempt will be made to classify large water bodies by depth classes.
Vantries, B. J.↗
Utilizing ERTS-A imagery for tectonic analysis through study of Big Horn Mountains Region
The author has identified the following significant results. NASA-provided color composite (1048-17234) which includes the southeastern portion of the Bighorn Mountains and the western Powder River basin is of excellent quality. The considerable variations in the red hues indicate that vegetational mapping will be enhanced over the black and white. Some additional delineation of rock units can be made, particularly the Chugwater formation. Preliminary look at just received winter scenes indicates that topographic features are enhanced both due to the snow cover and to the lower sun angle.
Hoppin, R. A.↗
Utilization of remotely-sensed data in the management of inland wetlands
The author has identified the following significant results. ERTS-1 data and aerial photography are proving to be a useful tool for the inventory and management of inland wetlands. Two examples of the application of remotely-sensed data to specific wetland management needs or requirements are discussed. Studies of the Great Dismal Swamp are utilizing ERTS-1 imagery and color IR photography in: (1) study area selection; (2) field inspection; (3) vegetation mapping; (4) identification of drainage characteristics and moisture regime; (5) location of intensive study areas; and (6) detection of change. Thematic extractions of ERTS-1 data made using the United States Geological Survey's Autographic Theme Extraction System are aiding analyses of swamp hydrologic regime and providing information pertinent to quick recognition and inventory of wetlands from ERTS-1. DCP'S in south Florida wetlands provide near-real time data for water resources managers. Data relayed by satellite can be entered into models to provide predictive data and water storage information for long-term and short-term decision making.
Carter, V.↗
Applications of ERTS data to resource surveys of Alaska
ERTS data affords a unique opportunity to perform urgently needed resource surveys and land use planning at a critical juncture in the history of Alaska's social and economic development. The available facilities for photographic, optical and digital processing of ERTS data are described, along with the interpretive techniques which have been developed. Examples of the applications of these facilities and techniques are given for three environmental disciplines: vegetation mapping for potential archeological sites; marine and sea ice surveys on the Alaskan continental shelf for the determination of surface circulation and sedimentation patterns and their effects on navigation, pollution assessment, fisheries, location of habors and construction of off-shore structures; snow surveys for inventories of water resources and flood potential in Alaska watersheds.
Belon, A. E.↗
Agriculture, forestry, range resources
In the area of crop specie identification, it has been found that temporal data analysis, preliminary stratification, and unequal probability analysis were several of the factors that contributed to high identification accuracies. Single data set accuracies on fields of greater than 80,000 sq m (20 acres) are in the 70- to 90-percent range; however, with the use of temporal data, accuracies of 95 percent have been reported. Identification accuracy drops off significantly on areas of less than 80,000 sq m (20 acres) as does measurement accuracy. Forest stratification into coniferous and deciduous areas has been accomplished to a 90- to 95-percent accuracy level. Using multistage sampling techniques, the timber volume of a national forest district has been estimated to a confidence level and standard deviation acceptable to the Forest Service at a very favorable cost-benefit time ratio. Range specie/plant community vegetation mapping has been accomplished at various levels of success (69- to 90-percent accuracy). However, several investigators have obtained encouraging initial results in range biomass (forage production) estimation and range readiness predictions. Soil association map correction and soil association mapping in new area appear to have been proven feasible on large areas; however, testing in a complex soil area should be undertaken.
Crea, W. J.↗