Art Neural Networks for Remote Sensing: Vegetation Classification from Landsat TM and Terrain Data

A new methodology for automatic mapping from Landsat Thematic Mapper (TM) and terrain data, based on the fuzzy ARTMAP neural network, is developed. System capabilities are tested on a challenging remote sensing classification problem, using spectral and terrain features for vegetation classification in the Cleveland National Forest. After training at the pixel level, system performance is tested at the stand level, using sites not seen during training. Results are compared to those of maximum likelihood classifiers, as well as back propagation neural networks and K Nearest Neighbor algorithms. ARTMAP dynamics are fast, stable, and scalable, overcoming common limitations of back propagation, which did not give satisfactory performance. Best results are obtained using a hybrid system based on a convex combination of fuzzy ARTMAP and maximum likelihood predictions. A prototype remote sensing example introduces each aspect of data processing and fuzzy ARTMAP classification. The example shows how the network automatically constructs a minimal number of recognition categories to meet accuracy criteria. A voting strategy improves prediction and assigns confidence estimates by training the system several times on different orderings of an input set.National Science Foundation (IRI 94-01659, SBR 93-00633); Office of Naval Research (N00014-95-l-0409, N00014-95-0657

ART and ARTMAP Neural Networks for Applications: Self-Organizing Learning, Recognition, and Prediction

ART and ARTMAP neural networks for adaptive recognition and prediction have been applied to a variety of problems. Applications include parts design retrieval at the Boeing Company, automatic mapping from remote sensing satellite measurements, medical database prediction, and robot vision. This chapter features a self-contained introduction to ART and ARTMAP dynamics and a complete algorithm for applications. Computational properties of these networks are illustrated by means of remote sensing and medical database examples. The basic ART and ARTMAP networks feature winner-take-all (WTA) competitive coding, which groups inputs into discrete recognition categories. WTA coding in these networks enables fast learning, that allows the network to encode important rare cases but that may lead to inefficient category proliferation with noisy training inputs. This problem is partially solved by ART-EMAP, which use WTA coding for learning but distributed category representations for test-set prediction. In medical database prediction problems, which often feature inconsistent training input predictions, the ARTMAP-IC network further improves ARTMAP performance with distributed prediction, category instance counting, and a new search algorithm. A recently developed family of ART models (dART and dARTMAP) retains stable coding, recognition, and prediction, but allows arbitrarily distributed category representation during learning as well as performance.National Science Foundation (IRI 94-01659, SBR 93-00633); Office of Naval Research (N00014-95-1-0409, N00014-95-0657

Near-Real-Time Monitoring of Insect Defoliation Using Landsat Time Series

Introduced insects and pathogens impact millions of acres of forested land in the United States each year, and large-scale monitoring efforts are essential for tracking the spread of outbreaks and quantifying the extent of damage. However, monitoring the impacts of defoliating insects presents a significant challenge due to the ephemeral nature of defoliation events. Using the 2016 gypsy moth (Lymantria dispar) outbreak in Southern New England as a case study, we present a new approach for near-real-time defoliation monitoring using synthetic images produced from Landsat time series. By comparing predicted and observed images, we assessed changes in vegetation condition multiple times over the course of an outbreak. Initial measures can be made as imagery becomes available, and season-integrated products provide a wall-to-wall assessment of potential defoliation at 30 m resolution. Qualitative and quantitative comparisons suggest our Landsat Time Series (LTS) products improve identification of defoliation events relative to existing products and provide a repeatable metric of change in condition. Our synthetic-image approach is an important step toward using the full temporal potential of the Landsat archive for operational monitoring of forest health over large extents, and provides an important new tool for understanding spatial and temporal dynamics of insect defoliators

Changes in Summer Irrigated Crop Area and Water Use in Southeastern Turkey from 1993 to 2002: Implications for Current and Future Water Resources

Changes in summer irrigated cropland acreage and related water use are estimated from satellite remote sensing and ancillary data in semi-arid Southeastern Turkey where traditionally dry agricultural lands are being rapidly transformed into irrigated fields with the help of water from the Euphrates-Tigris Rivers. An image classification methodology based on thresholding of Landsat NDVI images from the peak summer period reveals that the total area of summer irrigated crops has increased three-fold (from 35,000 ha to over 100,000) in the Harran Plain between 1993 and 2002. Coupled analysis of annual irrigated crop area from remote sensing and potential evapotranspiration based estimates of irrigation water requirements for cotton indicate a corresponding increase in agricultural water use from about 370 million cubic meters to over one billion cubic meters, a volume in accordance with the state estimates. These estimates have important implications for understanding the rapid changes in current agricultural withdrawals in Southeastern Turkey and form a quantitative basis for exploring the changes in future water demands in the region. For example, expansion of irrigated lands have led to a steady decrease in potential evaporation due to increased roughness and decreased humidity deficit in the Harran Plain. Assuming that the changes in future evaporation conditions will be of similar nature, water use for irrigation is expected to decrease over 40 percent in future irrigation sites. Incorporating this decrease in overall planning of the irrigation projects currently under construction should lead to improved management, and by extension, sustainability of water resources in the region

View angle effects on MODIS snow mapping in forests

Binary snow maps and fractional snow cover data are provided routinely from MODIS (Moderate Resolution Imaging Spectroradiometer). This paper investigates how the wide observation angles of MODIS influence the current snow mapping algorithm in forested areas. Theoretical modeling results indicate that large view zenith angles (VZA) can lead to underestimation of fractional snow cover (FSC) by reducing the amount of the ground surface that is viewable through forest canopies, and by increasing uncertainties during the gridding of MODIS data. At the end of the MODIS scan line, the total modeled error can be as much as 50% for FSC. Empirical analysis of MODIS/Terra snow products in four forest sites shows high fluctuation in FSC estimates on consecutive days. In addition, the normalized difference snow index (NDSI) values, which are the primary input to the MODIS snow mapping algorithms, decrease as VZA increases at the site level. At the pixel level, NDSI values have higher variances, and are correlated with the normalized difference vegetation index (NDVI) in snow covered forests. These findings are consistent with our modeled results, and imply that consideration of view angle effects could improve MODIS snow monitoring in forested areas

Consistency of MODIS surface bidirectional reflectance distribution function and albedo retrievals: 2. Validation

The evaluation of the first available satellite-based global albedo product at 1-km resolution is essential for its application in climate studies. We evaluate the accuracy of the Moderate-Resolution Imaging Spectroradiometer (MODIS) albedo product using available field measurements at Surface Radiation Budget Network (SURFRAD) and Cloud and Radiation Testbed–Southern Great Plains (CART/SGP) stations and examine the consistency between the MODIS surface albedos and the Clouds and Earth’s Radiant Energy System (CERES) top-of-the-atmosphere albedos as well as historical global albedos from advanced very high resolution radiometer (AVHRR) and Earth Radiation Budget Experiment (ERBE) observations. A comparison with the field measurements shows that the MODIS surface albedo generally meets an absolute accuracy requirement of 0.02 for our study sites during April–September 2001, with the root mean square errors less than 0.018. Larger differences appear in the winter season probably due to the increased heterogeneity of surface reflectivity in the presence of snow. To examine the effect of spatial heterogeneity on the validation of the MODIS albedos using fine resolution field measurements, we derive an intermediate albedo product from four Landsat Enhanced Thematic Mapper Plus (ETM+) images at 30-m spatial resolution as a surrogate for the distributed field measurements. The surface albedo is relatively homogeneous over the study stations in growing seasons, and therefore the validation during April–September is supported. A case study over three SURFRAD stations reveals that the MODIS bidirectional reflectance distribution function model is able to capture the solar zenith angle dependence of surface albedo as shown by the field measurements. We also find that the MODIS surface shortwave albedo is consistent with the contemporary and collocated CERES top-of atmosphere albedos derived directly from broadband observations. The MODIS albedo is also well correlated with historical surface albedos derived from AVHRR and ERBE observations, and a high bias of 0.016 and a low bias of 0.034 compared to those of the latter albedos are reasonable considering the differences in instruments and retrieval algorithms as well as environmental changes

The Global Landsat Archive: Status, Consolidation, and Direction

New and previously unimaginable Landsat applications have been fostered by a policy change in 2008 that made analysis-ready Landsat data free and open access. Since 1972, Landsat has been collecting images of the Earth, with the early years of the program constrained by onboard satellite and ground systems, as well as limitations across the range of required computing, networking, and storage capabilities. Rather than robust on-satellite storage for transmission via high bandwidth downlink to a centralized storage and distribution facility as with Landsat-8, a network of receiving stations, one operated by the U.S. government, the other operated by a community of International Cooperators (ICs), were utilized. ICs paid a fee for the right to receive and distribute Landsat data and over time, more Landsat data was held outside the archive of the United State Geological Survey (USGS) than was held inside, much of it unique. Recognizing the critical value of these data, the USGS began a Landsat Global Archive Consolidation (LGAC) initiative in 2010 to bring these data into a single, universally accessible, centralized global archive, housed at the Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota. The primary LGAC goals are to inventory the data held by ICs, acquire the data, and ingest and apply standard ground station processing to generate an L1T analysis-ready product. As of January 1, 2015 there were 5,532,454 images in the USGS archive. LGAC has contributed approximately 3.2 million of those images, more than doubling the original USGS archive holdings. Moreover, an additional 2.3 million images have been identified to date through the LGAC initiative and are in the process of being added to the archive. The impact of LGAC is significant and, in terms of images in the collection, analogous to that of having had twoadditional Landsat-5 missions. As a result of LGAC, there are regions of the globe that now have markedly improved Landsat data coverage, resulting in an enhanced capacity for mapping, monitoring change, and capturing historic conditions. Although future missions can be planned and implemented, the past cannot be revisited, underscoring the value and enhanced significance of historical Landsat data and the LGAC initiative. The aim of this paper is to report the current status of the global USGS Landsat archive, document the existing and anticipated contributions of LGAC to the archive, and characterize the current acquisitions of Landsat-7 and Landsat-8. Landsat-8 is adding data to the archive at an unprecedented rate as nearly all terrestrial images are now collected. We also offer key lessons learned so far from the LGAC initiative, plus insights regarding other critical elements of the Landsat program looking forward, such as acquisition, continuity, temporal revisit, and the importance of continuing to operationalize the Landsat program

A Global Analysis of the Spatial and Temporal Variability of Usable Landsat Observations at the Pixel Scale

The Landsat program has the longest collection of moderate-resolution satellite imagery, and the data are free to everyone. With the improvements of standardized image products, the flexibility of cloud computing platforms, and the development of time series approaches, it is now possible to conduct global-scale analyses of time series using Landsat data over multiple decades. Efforts in this regard are limited by the density of usable observations. The availability of usable Landsat Tier 1 observations at the scale of individual pixels from the perspective of time series analysis for land change monitoring is remarkably variable both in space (globally) and time (1985–2020), depending most immediately on which sensors were in operation, the technical capabilities of the mission, and the acquisition strategies and objectives of the satellite operators (e.g., USGS, commercial company) and the international ground receiving stations. Additionally, analysis of data density at the pixel scale allows for the integration of quality control data on clouds, cloud shadows, and snow as well as other properties returned from the atmospheric correction process. Maps for different time periods show the effect of excluding observations based on the presence of clouds, cloud shadows, snow, sensor saturation, hazy observations (based on atmospheric opacity), and lack of aerosol optical depth information. Two major discoveries are: 1) that filtering saturated and hazy pixels is helpful to reduce noise in the time series, although the impact may vary across different continents; 2) the atmospheric opacity band needs to be used with caution because many images are removed when no value is given in this band, when many of those observations are usable. The results provide guidance on when and where time series analysis is feasible, which will benefit many users of Landsat data

Implications of land use change on the national terrestrial carbon budget of Georgia

<p>Abstract</p> <p>Background</p> <p>Globally, the loss of forests now contributes almost 20% of carbon dioxide emissions to the atmosphere. There is an immediate need to reduce the current rates of forest loss, and the associated release of carbon dioxide, but for many areas of the world these rates are largely unknown. The Soviet Union contained a substantial part of the world's forests and the fate of those forests and their effect on carbon dynamics remain unknown for many areas of the former Eastern Bloc. For Georgia, the political and economic transitions following independence in 1991 have been dramatic. In this paper we quantify rates of land use changes and their effect on the terrestrial carbon budget for Georgia. A carbon book-keeping model traces changes in carbon stocks using historical and current rates of land use change. Landsat satellite images acquired circa 1990 and 2000 were analyzed to detect changes in forest cover since 1990.</p> <p>Results</p> <p>The remote sensing analysis showed that a modest forest loss occurred, with approximately 0.8% of the forest cover having disappeared after 1990. Nevertheless, growth of Georgian forests still contribute a current national sink of about 0.3 Tg of carbon per year, which corresponds to 31% of the country anthropogenic carbon emissions.</p> <p>Conclusions</p> <p>We assume that the observed forest loss is mainly a result of illegal logging, but we have not found any evidence of large-scale clear-cutting. Instead local harvesting of timber for household use is likely to be the underlying driver of the observed logging. The Georgian forests are a currently a carbon sink and will remain as such until about 2040 if the current rate of deforestation persists. Forest protection efforts, combined with economic growth, are essential for reducing the rate of deforestation and protecting the carbon sink provided by Georgian forests.</p