The sparse crown along both riversides of the Tarim River plays an important role in firming the sand and restraining the
desertification. It is very difficult to obtain the spectrum information from the remotely sensed data because of the low
percentage of coverage of the sparse vegetation, which affects the classification accuracy of the identification of ground
objects and the extraction of vegetation biophysics. It is a key obstruction in developing the quantification of the RS
technology. Taking the sparse vegetation at the Tarim River Basin as the research object, this paper predicts the surface
bidirectional reflectance of the discontinuous plant canopies in the extremely arid based on the observed ground
spectrum. Two different approaches are presented for the tree and the shrub. The first is to simulate the spectrum of the
tree with the Geometric Optical-Radiative Transfer model based on ground observation. In the second approach,the
spectral responses of sparse shrub and bare soil have been simulated using the linear Geometric Optical (GO) model.
Comparing the simulated bidirectional reflectance with actual remote sensing data (EO-1), the spectral differences of
these data are analyzed.
The selection of suitable scales is one of the key issues in the monitoring of the land use, or more generally, in the study
areas of ecology and geography. The scale change trend of land use in the mainstream area of the Tarim River in recent
50 years are lucubrated in this paper by interpreting the land use data in the 1950s, 1970s, 1990s and 2000 with the
available maps and RS images. Taking the area of land use as the parameter in selecting the scales, the histograms of the
patches in area as are charted. The normalized scale variances under 9 scales are calculated. By reinforcing the calculated
results with the landscape indexes including the Shannon-Weaver diversity index, Simpson diversity index and fractal
dimensions, the characteristics and scale change trends of the land use in the Tarim River Basin can be summarized as
following: (1) The variance of the areas as patches in the region is in great disparity. The patch of sandlands is the largest
and its proportion in the year of 2000 was 43.77%; (2) In the course of the study period, the frequency distribution of the
areas of patches, sandlands, saline or alkaline lands, forest land and shrub lands in this region was in normal distribution.
However, the positions of the peak values and the distribution patterns were different; (3) Normalized scale variance
table reveals that the most suitable scale of land use in the region is at 1:50000 or in the range of 1: 50000~1: 100000 in
general. The general patterns of the normalized scale variances in the Tarim River Basin in the 4 study periods were
similar; (4) Comparing with the normalized scale variances, there were no significant distribution trends of the three
A technique is presented for detecting vegetation crop nutrient stress from hyperspectral data. Experiments are conducted on peach trees. It is shown that nutrient deficiencies that caused stress could an be detected reliably on hyperspectral spectra. During an extensive field campaign, foliar and crown reflectance has been measured with a portable field spectroradiometer. Airborne hyperspectral imagery is acquired over the orchard with the AHS hyperspectral sensor. The multi-level approach (leaf level and top of canopy) enabled the assessment of vegetation indices and their relationship with pigment concentration at both leaf and canopy levels, showing the potential and limitations of hyperspectral remote sensing on the different levels. Stress on the peach orchard is was treated with iron chelates to recover from iron chlorosis conditions. Blocks of trees treated with iron chelates created a dynamic range of chlorophyll concentration as measured in leaves. A relationship is obtained between the measured spectra and estimated biochemical parameters via inversion of a linked directional homogeneous canopy reflectance model (ACRM) and the PROSPECT leaf model. Numerical model inversion was conducted by minimizing the difference between the measured reflectance samples and modeled values. An improved optimization method is presented. Results are compared with a simple linear regression analysis, linking chlorophyll to the reflectance measured at the leaf level and Top of Canopy (TOC). Optimal band regions and bandwidths are analyzed.
Hyperspectral image classification impose challenging requirements to
a classifier. It is well known that more spectral bands can be difficult to process and introduce problems such as the Hughes phenomenon. Nevertheless, user requirements are very demanding, as expectations grow with the available number of spectral bands: subtle differences in a large number of classes must be distinguished. As multiclass classifiers become rather complex for a large number of classes, a combination of binary classification results are often used to come to a class decision. In this approach, the posterior probability is retained for each of the binary classifiers. From these, a combined posterior probability for the multiclass case is obtained. The proposed technique is applied to map the highly diverse Belgian coastline. In total, 17 vegetation types are defined. Additionally, bare soil, shadow, water and urban area are also classified. The posterior probabilities are used for unmixing. This is demonstrated for 4 classes: bare soil and 3 vegetation classes. Results are very promosing, outperforming other approaches such as linear unmixing.
This paper studies the detection of vegetation stress in orchards via remote sensing. During previous research, it was shown that stress can be detected reliably on hyperspectral reflectances of the fresh leaves, using a generic wavelet based hyperspectral classification. In this work, we demonstrate the capability to detect stress from airborne/spaceborne hyperspectral sensors by upscaling the leaf reflectances to top of atmosphere (TOA) radiances. Several data sets are generated, measuring the foliar reflectance with a portable field spectroradiometer, covering different time periods, fruit variants and stress types. We concentrated on the Jonagold and Golden Delicious apple trees, induced with mildew and nitrogen deficiency. First, a directional homogeneous canopy reflectance model (ACRM) is applied on these data sets for simulating top of canopy (TOC) spectra. Then, the TOC level is further upscaled to TOA, using the atmospheric radiative transfer model MODTRAN4. To simulate hyperspectral imagery acquired with real airborne/spaceborne sensors, the spectrum is further filtered and subsampled to the available resolution. Using these simulated upscaled TOC and TOA spectra in classification, we will demonstrate that there is still a differentiation possible between stresses and non-stressed trees. Furthermore, results show it is possible to train a classifier with simulated TOA data, to make a classification of real hyperspectral imagery over the orchard.
Recently, a joint Swiss/Belgian initiative started a project to build a new generation airborne imaging spectrometer, namely APEX (Airborne Prism Experiment) under the ESA funding scheme named PRODEX. APEX is a dispersive pushbroom imaging spectrometer operating in the spectral range between 380 - 2500 nm. The spectral resolution will be better then 10 nm in the SWIR and < 5 nm in the VNIR range of the solar reflected range of the spectrum. The total FOV will be ± 14 deg, recording 1000 pixels across track with max. 300 spectral bands simultaneously. APEX is subdivided into an industrial team responsible for the optical instrument, the calibration homebase, and the detectors, and a science and operational team, responsible for the processing and archiving of the imaging spectrometer data, as well as for its operation. APEX is in its design phase and the instrument will be operationally available to the user community in the year 2006.
Proc. SPIE. 5238, Image and Signal Processing for Remote Sensing IX
KEYWORDS: Statistical analysis, FDA class I medical device development, Wavelets, Remote sensing, Reflectivity, Linear filtering, Discrete wavelet transforms, Feature extraction, Vegetation, Feature selection
The high spectral and high spatial resolution, intrinsic to hyperspectral remote sensing, result in huge quantities of data, which slows down the data processing and can result in a poor performance of classifiers. To improve the classification performance, efficient feature extraction methods are needed. This paper introduces a set of features based on the discrete wavelet transform (DWT). Wavelet coefficients, wavelet energies and wavelet detail histogram features are employed as new features for classification. As a feature reduction procedure, we propose a sequential floating search method. Selection is performed using a cost function based on the estimated probability of error, using the Fisher criterion. This procedure selects the best combination of features. To demonstrate the proposed wavelet features and selection procedure, we apply it to vegetation stress detection. For this application, it is shown that wavelet coefficients outperform spectral reflectance and that the proposed selection procedure outperforms combining the best single features.