A Neural Network Method for Efficient Vegetation Mapping

This paper describes the application of a neural network method designed to improve the efficiency of map production from remote sensing data. Specifically, the ARTMAP neural network produces vegetation maps of the Sierra National Forest, in Northern California, using Landsat Thematic Mapper (TM) data. In addition to spectral values, the data set includes terrain and location information for each pixel. The maps produced by ARTMAP are of comparable accuracy to maps produced by a currently used method, which requires expert knowledge of the area as well as extensive manual editing. In fact, once field observations of vegetation classes had been collected for selected sites, ARTMAP took only a few hours to accomplish a mapping task that had previously taken many months. The ARTMAP network features fast on-line learning, so the system can be updated incrementally when new field observations arrive, without the need for retraining on the entire data set. In addition to maps that identify lifeform and Calveg species, ARTMAP produces confidence maps, which indicate where errors are most likely to occur and which can, therefore, be used to guide map editing

A Neural Network Method for Mixture Estimation for Vegetation Mapping

While most forest maps identify only the dominant vegetation class in delineated stands, individual stands are often better characterized by a mix of vegetation types. Many land management applications, including wildlife habitat studies, can benefit from knowledge of mixes. This paper examines various algorithms that use data from the Landsat Thematic Mapper (TM) satellite to estimate mixtures of vegetation types within forest stands. Included in the study are maximum likelihood classification and linear mixture models as well as a new methodology based on the ARTMAP neural network. Two paradigms are considered: classification methods, which describe stand-level vegetation mixtures as mosaics of pixels, each identified with its primary vegetation class; and mixture methods, which treat samples as blends of vegetation, even at the pixel level. Comparative analysis of these mixture estimation methods, tested on data from the Plumas National Forest, yields the following conclusions: (1) accurate estimates of proportions of hardwood and conifer cover within stands can be obtained, particularly when brush is not present in the understory; (2) ARTMAP outperforms statistical methods and linear mixture models in both the classification and the mixture paradigms; (3) topographic correction fails to improve mapping accuracy; and (4) the new ARTMAP mixture system produces the most accurate overall results. The Plumas data set has been made available to other researchers for further development of new mapping methods and comparison with the quantitative studies presented here, which establish initial benchmark standards.National Science Foundation (IRI 94-0165, SBR 95-13889); Office of Naval Research (N00014-95-1-0409, N00014-95-0657); Region 5 Remote Sensing Laboratory of the U.S. Forest Servic

Proteomic and Physiological Responses of Kineococcus radiotolerans to Copper

Copper is a highly reactive, toxic metal; consequently, transport of this metal within the cell is tightly regulated. Intriguingly, the actinobacterium Kineococcus radiotolerans has been shown to not only accumulate soluble copper to high levels within the cytoplasm, but the phenotype also correlated with enhanced cell growth during chronic exposure to ionizing radiation. This study offers a first glimpse into the physiological and proteomic responses of K. radiotolerans to copper at increasing concentration and distinct growth phases. Aerobic growth rates and biomass yields were similar over a range of Cu(II) concentrations (0–1.5 mM) in complex medium. Copper uptake coincided with active cell growth and intracellular accumulation was positively correlated with Cu(II) concentration in the growth medium (R2 = 0.7). Approximately 40% of protein coding ORFs on the K. radiotolerans genome were differentially expressed in response to the copper treatments imposed. Copper accumulation coincided with increased abundance of proteins involved in oxidative stress and defense, DNA stabilization and repair, and protein turnover. Interestingly, the specific activity of superoxide dismutase was repressed by low to moderate concentrations of copper during exponential growth, and activity was unresponsive to perturbation with paraquot. The biochemical response pathways invoked by sub-lethal copper concentrations are exceptionally complex; though integral cellular functions are preserved, in part, through the coordination of defense enzymes, chaperones, antioxidants and protective osmolytes that likely help maintain cellular redox. This study extends our understanding of the ecology and physiology of this unique actinobacterium that could potentially inspire new biotechnologies in metal recovery and sequestration, and environmental restoration

Monitoring large areas for forest change using Landsat: Generalization across space, time and Landsat sensors

Landsat 7 ETM+ provides an opportunity to extend the area and frequency with which we are able to monitor the Earth’s surface with fine spatial resolution data. To take advantage of this opportunity it is necessary to move beyond the traditional image-by-image approach to data analysis. A new approach to monitoring large areas is to extend the application of a trained image classifier to data beyond its original temporal, spatial, and sensor domains. A map of forest change in the Cascade Range of Oregon developed with methods based on such generalization shows accuracies comparable to a map produced with current state-of-the-art methods. A test of generalization across sensors to monitor forest change in the Rocky Mountains indicates that Landsat 7 ETM+ data can be combined with earlier Landsat 5 TM data without retraining the classifier. Methods based on generalization require less time and effort than conventional methods and as a result may allow monitoring of larger areas or more frequent monitoring at reduced cost. One key component to achieving this goal is the improved availability and affordability of Landsat 7 imagery. These results highlight the value of the existing Landsat archive and the importance for continuity in the Landsat Program

Vegetation mapping and monitoring

The nature and properties of vegetation are fundamental attributes of landscapes. The nature of the vegetation in an area is determined by a complex combination of effects related to climate, soils, history, fire and human influences which can date back several millennia in some locations. People have been interested in understanding the distribution of vegetation types since the times of Theophrastus, with significant contributions coming from such noted historical figures as Alexander von Humboldt and Lord Alfred Wallace. When viewed from this historical perspective, vegetation mapping has a long history which includes a variety of contexts and a wide range of geographic scales. ... The goal of this chapter is to provide some history and context to recent and current efforts to map and monitor vegetation, while providing some indications regarding the way vegetation maps are used in environmental modelling. Remote sensing has revolutionized vegetation mapping, as the synoptic perspective is ideal for mapping landscape attributes. The focus here will be on the use of satellite remote sensing for vegetation mapping and monitoring, and the discussion attempts to characterize the recent innovations and ongoing areas of active research. Please note that this chapter focuses on vegetation maps at local to regional scales. Discussion of the use of satellite imagery for continental to global scales is included in Chapters 4 and 5