Remote sensing of invasive species is a critical component of conservation and management efforts, but reliable methods for the detection of invaders have not been widely established. In Hawaiian forests, we recently found that invasive trees often have hyperspectral signatures unique from that of native trees, but mapping based on spectral reflectance properties alone is confounded by issues of canopy senescence and mortality, intra- and inter-canopy gaps and shadowing, and terrain variability. We deployed a new hybrid airborne system combining the Carnegie Airborne Observatory (CAO) small-footprint light detection and ranging (LiDAR) system with the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) to map the three-dimensional spectral and structural properties of Hawaiian forests. The CAO-AVIRIS systems and data were fully integrated using in-flight and post-flight fusion techniques, facilitating an analysis of forest canopy properties to determine the presence and abundance of three highly invasive tree species in Hawaiian rainforests.
The LiDAR sub-system was used to model forest canopy height and top-of-canopy surfaces; these structural data allowed for automated masking of forest gaps, intra- and inter-canopy shadows, and minimum vegetation height in the AVIRIS images. The remaining sunlit canopy spectra were analyzed using spatially-constrained spectral mixture analysis. The results of the combined LiDAR-spectroscopic analysis highlighted the location and fractional abundance of each invasive tree species throughout the rainforest sites. Field validation studies demonstrated < 6.8% and < 18.6% error rates in the detection of invasive tree species at 7 m2 and 2 m2 minimum canopy cover thresholds. Our results show that full integration of imaging spectroscopy and LiDAR measurements provides enormous flexibility and analytical potential for studies of terrestrial ecosystems and the species contained within them. 相似文献
Data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) have a significant advantage over previous datasets because of the combination of high spatial resolution (15-90 m) and enhanced multispectral capabilities, particularly in the thermal infrared (TIR) atmospheric window (8-12 μm) of the Earth where common silicate minerals are more easily identified. However, the 60 km swath width of ASTER can limit the effectiveness of accurately tracing large-scale features, such as eolian sediment transport pathways, over long distances. The primary goal of this paper is to describe a method for generating a seamless and radiometrically accurate ASTER TIR mosaic of atmospherically corrected radiance and from that, extract surface emissivity for arid lands, specifically, sand seas. The Gran Desierto in northern Sonora, Mexico was used as a test location for the radiometric normalization technique because of past remote sensing studies of the region, its compositional diversity, and its size. A linear approach was taken to transform adjacent image swaths into a direct linear relationship between image acquisition dates. Pseudo-invariant features (PIFs) were selected using a threshold of correlation between radiance values, and change-pixels were excluded from the linear regression used to determine correction factors. The degree of spectral correlation between overlapping pixels is directly related to the amount of surface change over time; therefore, the gain and offsets between scenes were based only on regions of high spectral correlation. The result was a series of radiometrically normalized radiance-at-surface images that were combined with a minimum of image edge seams present. These edges were subsequently blended to create the final mosaic. The advantages of this approach for TIR radiance (as opposed to emissivity) data include the ability to: (1) analyze data acquired on different dates (with potentially very different surface temperatures) as one seamless compositional dataset; (2) perform decorrelation stretches (DCS) on the entire dataset in order to identify and discriminate compositional units; and (3) separate brightness temperature from surface emissivity for quantitative compositional analysis of the surface, reducing seam-line error in the emissivity mosaic. The approach presented here is valid for any ASTER-related study of large geographic regions where numerous images spanning different temporal and atmospheric conditions are encountered. 相似文献
Vegetation water content is an important parameter for retrieval of soil moisture from microwave data and for other remote sensing applications. Because liquid water absorbs in the shortwave infrared, the normalized difference infrared index (NDII), calculated from Landsat 5 Thematic Mapper band 4 (0.76-0.90 μm wavelength) and band 5 (1.55-1.65 μm wavelength), can be used to determine canopy equivalent water thickness (EWT), which is defined as the water volume per leaf area times the leaf area index (LAI). Alternatively, average canopy EWT can be determined using a landcover classification, because different vegetation types have different average LAI at the peak of the growing season. The primary contribution of this study for the Soil Moisture Experiment 2004 was to sample vegetation for the Arizona and Sonora study areas. Vegetation was sampled to achieve a range of canopy EWT; LAI was measured using a plant canopy analyzer and digital hemispherical (fisheye) photographs. NDII was linearly related to measured canopy EWT with an R2 of 0.601. Landcover of the Arizona, USA, and Sonora, Mexico, study areas were classified with an overall accuracy of 70% using a rule-based decision tree using three dates of Landsat 5 Thematic Mapper imagery and digital elevation data. There was a large range of NDII per landcover class at the peak of the growing season, indicating that canopy EWT should be estimated directly using NDII or other shortwave-infrared vegetation indices. However, landcover classifications will still be necessary to obtain total vegetation water content from canopy EWT and other data, because considerable liquid water is contained in the non-foliar components of vegetation. 相似文献
Land Surface Models (LSM) have been designed to describe water and energy transfers at the soil-vegetation-atmosphere interface, and are therefore essential in many environmental disciplines. These numerical models, driven by the boundary conditions in the atmosphere and in the soil, require adequate knowledge of those vegetation and soil characteristics which are determinant in the characterisation of mass and energy transfers. In view of the fact that, firstly this information is often only partially known, and secondly the transfers are sometimes incorrectly represented, these models can rapidly drift and need to be regularly corrected. To this aim, remote sensing is a promising tool and many studies are currently devoted to the development of assimilation techniques to control their inputs or internal variables. The research presented in this paper contributes to this effort. Its ambition is to explore new methodologies, designed to make use of remote sensing thermal infrared data recorded from space. This study is based on the analysis of links between the characteristics of the diurnal cycle of the surface brightness temperature and the soil-atmosphere interface parameters and variables. The proposed methodology takes advantage of these temperatures cycling features, instead of absolute temperature values, to calibrate the LSM. The results show that the model parameters have a significant impact on the diurnal temperature dynamics, sometimes to a greater extent than on the temperature itself, and that these relationships have diurnal and seasonal variations. As a consequence, the use of TIR data for LSM calibration can be optimised by considering only those parts of the information which are really relevant to parameter calibration. 相似文献
This paper presents a method for generating gaits for a one-legged articulated hopping robot. A static optimization procedure
produces the initial joint velocities for the flight phase, using the principle of conservation of angular momentum and assuming
(nearly) passive flight. Two novel objective functions for this static optimization enable one to choose different gaits by
simply changing a few parameters. A dynamic optimization procedure yields a solution for the flight trajectory that minimizes
control effort. The stance phase (when the foot is touching the ground) becomes a standard two point boundary value problem,
also solved with a dynamic optimization procedure. During the stance phase, the physical joint limitations, ground reaction
forces, and the trajectory of the zero-moment point all constrain the solution. After these single-phase optimizations, a
complete-cycle optimization procedure, incorporating both flight and stance phases, further reduces the control effort and
balances the motion phases. In simulation, the leg hops on even ground and up stairs, exhibiting energy-efficient and intuitively
satisfying gaits. 相似文献