HyMap Image Research Articles

ASTER data processing and fusion for alteration minerals and silicification detection: Implications for cupriferous mineralization exploration in the western Anti-Atlas, Morocco

Alteration minerals and silicification are typically associated with a variety of ore mineralizations and could be detected using multispectral remote sensing sensors as indicators for mineral exploration. In this investigation, the Visible Near-Infra-Red (VNIR), Short-Wave Infra-Red (SWIR), and Thermal Infra-Red (TIR) bands of the ASTER satellite sensor derived layers were fused to detect alteration minerals and silicification in east the Kerdous inlier for cupriferous mineralization exploration. Several image processing techniques were executed in the present investigation, namely, Band Ratio (BR), Selective Principal Component Analysis (SPCA) and Constrained Energy Minimization (CEM) techniques. Initially, the BR and SPCA processing results revealed several alteration zones, including argillic, phyllic, dolomitization and silicification as well as iron oxides and hydroxides. Then, these zones were mapped at sub-pixel level using the CEM technique. Pyrophyllite, kaolinite, dolomite, illite, muscovite, montmorillonite, topaz and hematite were revealed displaying a significant distribution in relation with the eastern Amlen region lithological units and previously detected mineral potential zones using HyMap imaging spectroscopy. Mainly, a close spatial association between iron oxides and hydroxide minerals, argillic, and phyllic alteration was detected, as well as a strong silicification was detected around doleritic dykes unit in Jbel Lkest area. A weighted overlay approach was used in the integration of hydrothermal alteration minerals and silicification, which allowed the elaboration of a new mineral alteration map of study area with five alteration intensities. ASTER and the various employed processing techniques allowed a practical and cost effective mapping of alteration features, which corroborates well with field survey and X-ray diffraction analysis. Therefore, ASTER data and the employed processing techniques offers a practical approach for mineral prospection in comparable settings.

Effects of hyperspectral data with different spectral resolutions on the estimation of soil heavy metal content: From ground-based and airborne data to satellite-simulated data

Soil heavy metal distribution maps can provide decision-making information for pollution control and agricultural management. However, the estimation of heavy metals is sensitive to the resolution of the soil spectra due to their sparse content in soils. The purposes of this study were to test the sensitivity of Ni, Zn and Pb prediction results to variations in spectral resolution, then to map their spatial distributions over a large area. In addition, the effectiveness of spectral feature extraction was investigated. In total, 92 soil samples and corresponding field soil spectra were obtained from the Tongwei-Zhuanglang area in Gansu Province, China. Airborne HyMap hyperspectral image of this area was acquired simultaneously. Three satellite image spectra (AHSIGF-5, Hyperion, AHSIZY-1 02D) were simulated using the field spectra which were measured under real environmental conditions rather than laboratory conditions. The combination of genetic algorithm and partial least squares regression (GA-PLSR) was used as prediction algorithm. The models calibrated by HyMap image full spectral bands had the highest accuracies (RP2 = 0.8558, 0.8002, and 0.8592 for Ni, Zn, and Pb, respectively) because of high consistency. For field spectra and three simulated satellite spectra, models calibrated by simulated AHSIGF-5 spectra performed best because of appropriate resolution (5 nm in the visible near-infrared [VNIR] and 10 nm in the short-wave infrared [SWIR]). The spectral feature extraction method only improved prediction accuracy of the field spectra, indicating that this method benefited from higher spectral resolution. The mapping of the spatial distribution of soil heavy metals over a large area was realized based on HyMap image. According to the results of the satellite simulation spectra, this study proposes to use GF-5 hyperspectral image to estimate heavy metals content. The outcomes provide a reference for the utilization of aerial and satellite hyperspectral images in prediction of soil heavy metal concentrations.

Solar photovoltaic module detection using laboratory and airborne imaging spectroscopy data

Over the past decades, solar panels have been widely used to harvest solar energy owing to the decreased cost of silicon-based photovoltaic (PV) modules, and therefore it is essential to remotely map and monitor the presence of solar PV modules. Many studies have explored on PV module detection based on color aerial photography and manual photo interpretation. Imaging spectroscopy data are capable of providing detailed spectral information to identify the spectral features of PV, and thus potentially become a promising resource for automated and operational PV detection. However, PV detection with imaging spectroscopy data must cope with the vast spectral diversity of surface materials, which is commonly divided into spectral intra-class variability and inter-class similarity. We have developed an approach to detect PV modules based on their physical absorption and reflection characteristics using airborne imaging spectroscopy data. A large database was implemented for training and validating the approach, including spectra-goniometric measurements of PV modules and other materials, a HyMap image spectral library containing 31 materials with 5627 spectra, and HySpex imaging spectroscopy data sets covering Oldenburg, Germany. By normalizing the widely used Hydrocarbon Index (HI), we solved the intra-class variability caused by different detection angles, and validated it against the spectra-goniometric measurements. Knowing that PV modules are composed of materials with different transparencies, we used a group of spectral indices and investigated their interdependencies for PV detection with implementing the image spectral library. Finally, six well-trained spectral indices were applied to HySpex data acquired in Oldenburg, Germany, yielding an overall PV map. Four subsets were selected for validation and achieved overall accuracies, producer's accuracies and user's accuracies, respectively. This physics-based approach was validated against a large database collected from multiple platforms (laboratory measurements, airborne imaging spectroscopy data), thus providing a robust, transferable and applicable way to detect PV modules using imaging spectroscopy data. We aim to create greater awareness of the potential importance and applicability of airborne and spaceborne imaging spectroscopy data for PV modules identification.

Model of Peatland Vegetation Species using HyMap Image and Machine Learning

Species Tumih / Parepat (Combretocarpus-rotundatus Mig. Dancer) family Anisophylleaceae and Meranti (Shorea Belangerang, Shorea Teysmanniana Dyer ex Brandis) family Dipterocarpaceae is a group of vegetation species distribution model. Species pioneer is predicted as an indicator of the succession of ecosystem restoration of tropical peatland characteristics and extremely fragile (unique) in the endemic hot spot of Sundaland. Climate change projections and conservation planning are hot topics of current discussion, analysis of alternative approaches and the development of combinations of species projection modelling algorithms through geospatial information systems technology. Approach model to find out the research problem of vegetation level based on the machine learning hybrid method, wavelet and artificial neural networks. Field data are used as a reference collection of natural resource field sample objects and biodiversity assessment. The testing and training ANN data set iterations times 28, achieve a performance value of 0.0867 MSE value is smaller than the ANN training data, above 50%, and spectral accuracy 82.1 %. Identify the location of the sample point position of the Tumih / Parepat vegetation species using HyMap Image is good enough, at least the modelling, design of the species distribution can reach the target in this study. The computation validation rate above 90% proves the calculation can be considered.

Hyperspectral remote sensing applied to mineral exploration in southern Peru: A multiple data integration approach in the Chapi Chiara gold prospect

Remote sensing is a strategic key tool for mineral exploration, due to its capacity of detecting hydrothermal alteration minerals or alteration mineral zones associated with different types of mineralization systems. A case study of an epithermal system located in southern Peru is presented, aimed at the characterization of mineral assemblies for discriminating potential high sulfidation epithermal targets, using hyperspectral imagery integrated with petrography, XRD and magnetic data. HyMap images were processed using the Mixture Tuned Matched Filtering (MTMF) technique for producing alteration map in the Chapi Chiara epithermal gold prospect. Extensive areas marked by advanced argillic alteration (alunite-kaolinite-dickite±topaz) were mapped in detail, as well as limited argillic (illite-smectite) and propylitic (chlorite spectral domain) alteration. The magmatic-hydrothermal processes responsible for the formation of hypogene minerals were also related to the destruction of ferrimagnetic minerals (e.g., magnetite) of host rocks such as andesite, and the remobilization/formation of paramagnetic Fe-Ti oxides (e.g., rutile, anatase). The large alteration zones of advanced argillic alteration are controlled by structures related to a regional NW-SE trend, and also by local NE-SW and ENE-WSW ones.

Mapping tropical dry forest succession using multiple criteria spectral mixture analysis

Tropical dry forests (TDFs) in the Americas are considered the first frontier of economic development with less than 1% of their total original coverage under protection. Accordingly, accurate estimates of their spatial extent, fragmentation, and degree of regeneration are critical in evaluating the success of current conservation policies. This study focused on a well-protected secondary TDF in Santa Rosa National Park (SRNP) Environmental Monitoring Super Site, Guanacaste, Costa Rica. We used spectral signature analysis of TDF ecosystem succession (early, intermediate, and late successional stages), and its intrinsic variability, to propose a new multiple criteria spectral mixture analysis (MCSMA) method on the shortwave infrared (SWIR) of HyMap image. Unlike most existing iterative mixture analysis (IMA) techniques, MCSMA tries to extract and make use of representative endmembers with spectral and spatial information. MCSMA then considers three criteria that influence the comparative importance of different endmember combinations (endmember models): root mean square error (RMSE); spatial distance (SD); and fraction consistency (FC), to create an evaluation framework to select a best-fit model. The spectral analysis demonstrated that TDFs have a high spectral variability as a result of biomass variability. By adopting two search strategies, the unmixing results showed that our new MCSMA approach had a better performance in root mean square error (early: 0.160/0.159; intermediate: 0.322/0.321; and late: 0.239/0.235); mean absolute error (early: 0.132/0.128; intermediate: 0.254/0.251; and late: 0.191/0.188); and systematic error (early: 0.045/0.055; intermediate: −0.211/−0.214; and late: 0.161/0.160), compared to the multiple endmember spectral mixture analysis (MESMA). This study highlights the importance of SWIR in differentiating successional stages in TDFs. The proposed MCSMA provides a more flexible and generalized means for the best-fit model determination than common IMA methods.

A Geometric Unmixing Concept for the Selection of Optimal Binary Endmember Combinations

One of the major issues with spectral mixture analysis remains the lack of ability to properly account for the spectral variability of endmembers (EMs). EM variability is most often addressed using large spectral libraries incorporating the variability present in the image. We propose a new geometric-based methodology to efficiently evaluate different binary EM combinations. Our approach selects the best EM combination prior to unmixing, building upon the equivalence between the reconstruction error in least squares unmixing and spectral angle minimization in geometric unmixing. This geometric approach is tested on both a simulated data set based on field measurements and a HyMap image. It is demonstrated that selecting the best EM combination for a pixel based on the angle minimization provided identical results compared with using the projection distance or reconstruction error. It also has the additional benefit of reducing the computation time due to the simplicity of the angle calculations.

Brown and green LAI mapping through spectral indices

When crops senescence, leaves remain until they fall off or are harvested. Hence, leaf area index (LAI) stays high even when chlorophyll content degrades to zero. Current LAI approaches from remote sensing techniques are not optimized for estimating LAI of senescent vegetation. In this paper a two-step approach has been proposed to realize simultaneous LAI mapping over green and senescent croplands. The first step separates green from brown LAI by means of a newly proposed index, ‘Green Brown Vegetation Index (GBVI)’. This index exploits two shortwave infrared (SWIR) spectral bands centred at 2100 and 2000nm, which fall right in the dry matter absorption regions, thereby providing positive values for senescent vegetation and negative for green vegetation. The second step involves applying linear regression functions based on optimized vegetation indices to estimate green and brown LAI estimation respectively. While the green LAI index uses a band in the red and a band in the red-edge, the brown LAI index uses bands located in the same spectral region as GBVI, i.e. an absorption band located in the region of maximum absorption of cellulose and lignin at 2154nm, and a reference band at 1635nm where the absorption of both water and dry matter is low. The two-step approach was applied to a HyMap image acquired over an agroecosystem at the agricultural site Barrax, Spain.

New hyperspectral difference water index for the extraction of urban water bodies by the use of airborne hyperspectral images

Extracting surface land-cover types and analyzing changes are among the most common applications of remote sensing. One of the most basic tasks is to identify and map surface water boundaries. Spectral water indexes have been successfully used in the extraction of water bodies in multispectral images. However, directly applying a water index method to hyperspectral images disregards the abundant spectral information and involves difficulty in selecting appropriate spectral bands. It is also a challenge for a spectral water index to distinguish water from shadowed regions. The purpose of this study is therefore to develop an index that is suitable for water extraction by the use of hyperspectral images, and with the capability to mitigate the effects of shadow and low-albedo surfaces, especially in urban areas. Thus, we introduce a new hyperspectral difference water index (HDWI) to improve the water classification accuracy in areas that include shadow over water, shadow over other ground surfaces, and low-albedo ground surfaces. We tested the new method using PHI-2, HyMAP, and ROSIS hyperspectral images of Shanghai, Munich, and Pavia. The performance of the water index was compared with the normalized difference water index (NDWI) and the Mahalanobis distance classifier (MDC). With all three test images, the accuracy of HDWI was significantly higher than that of NDWI and MDC. Therefore, HDWI can be used for extracting water with a high degree of accuracy, especially in urban areas, where shadow caused by high buildings is an important source of classification error.

On the Semi-Automatic Retrieval of Biophysical Parameters Based on Spectral Index Optimization

Regression models based on spectral indices are typically empirical formulae enabling the mapping of biophysical parameters derived from Earth Observation (EO) data. Due to its empirical nature, it remains nevertheless uncertain to what extent a selected regression model is the most appropriate one, until all band combinations and curve fitting functions are assessed. This paper describes the application of a Spectral Index (SI) assessment toolbox in the Automated Radiative Transfer Models Operator (ARTMO) package. ARTMO enables semi-automatic retrieval and mapping of biophysical parameters from optical remote sensing observations. The SI toolbox facilitates the assessment of biophysical parameter retrieval accuracy of established as well as new and generic SIs. For instance, based on the SI formulation used, all possible band combinations of formulations with up to ten bands can be defined and evaluated. Several options are available in the SI assessment: calibration/validation data partitioning, the addition of noise and the definition of curve fitting models. To illustrate its functioning, all two-band combinations according to simple ratio (SR) and normalized difference (ND) formulations as well as various fitting functions (linear, exponential, power, logarithmic, polynomial) have been assessed. HyMap imaging spectrometer (430–2490 nm) data obtained during the SPARC-2003 campaign in Barrax, Spain, have been used to extract leaf area index (LAI) and leaf chlorophyll content (LCC) estimates. For both SR and ND formulations the most sensitive regions have been identified for two-band combinations of green (539–570 nm) with longwave SWIR (2421–2453 nm) for LAI (r2: 0.83) and far-red (692 nm) with NIR (1340 nm) or shortwave SWIR (1661–1686 nm) for LCC (r2: 0.93). Polynomial, logarithmic and linear fitting functions led to similar best correlations, though spatial differences emerged when applying the functions to HyMap imagery. We suggest that a systematic SI assessment is a strong requirement in the quality assurance approach for accurate biophysical parameter retrieval.

A new approach for thresholding spectral change detection using multispectral and hyperspectral image data, a case study over Sokolov, Czech republic

Change detection and multitemporal analyses aim to detect changes occurring over a specific geographical area using two or more images acquired at two or more different times. In this article, we present a new thresholding approach for unsupervised change detection. This approach focuses on determining the threshold that discriminates between change and no-change pixels. The differences between pixels in the two images are associated with real changes or noise. We propose a thresholding scheme that separates the threshold into two parts: (1) a spectral domain threshold that accounts for errors related to sensor stability, atmospheric conditions, and data-processing variations, and (2) a spatial domain threshold associated with georectification errors. We demonstrate our method using both multispectral Landsat images and airborne imaging spectroscopy HyMap images. The results show that the spectral domain threshold gives high detection capabilities with moderate false-alarm rate. Adding the spatial domain threshold to the spectral domain threshold reduces the false-alarm rates while maintaining good detection capabilities.

Monitoring the Sewage Draining in Shenzhen Reservoirs Using Hyperspectral Data

Freshwater resources are regarded as the foundation of urban development and assure the sustainable prosperity of the city. The contaminations of fresh water in reservoirs can threaten safety of people directly and force the Industrial processes to be suspended. Therefore, developing a method to detect the potential locations where contaminated water drains off into the reservoirs efficiently and precisely is a challenging task but in urgent need. In this research, we used the air-borne sensor Hymap to get the hyperspectral data of Shenzhen. Finally we find a way to invert five water quality parameters (Suspended Solid (SS), Total Nitrogen (TN), Total Phosphorus (TP), Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD5)) from the Hymap image and we distinguish the clear water and polluted water on the image successfully.

Mineral mapping in the Maherabad area, eastern Iran, using the HyMap remote sensing data

This study applies matched filtering on the HyMap airborne hyperspectral data to obtain the distribution map of alteration minerals in the Maherabad area and uses virtual verification to verify the results. This paper also introduces “moving threshold” which tries to find an appropriate threshold value to convert gray scale images, produced by mapping methods, to target and background pixels. The Maherabad area, located in the eastern part of the Lut block, is a Cu–Au porphyry system in which quartz–sericite–pyrite, argillic and propylitic alteration are most common. Minimum noise fraction transform coupled with a pixel purity index was applied on the HyMap images to extract the endmembers of the alteration minerals, including kaolinite, montmorillonite, sericite (muscovite/illite), calcite, chlorite, epidote, and goethite. Since there was no access to any portable spectrometer and/or lab spectral measurements for the verification of the remote sensing imagery results, virtual verification achieved using the USGS spectral library and showed an agreement of 83.19%. The comparison between the results of the matched filtering and X-ray diffraction (XRD) analyses also showed an agreement of 56.13%.

Assessing the potential of hyperspectral imagery to map bark beetle-induced tree mortality

Natural hazards caused by insect outbreaks, such as those induced by the European bark beetle (Ips typographus L.), are among the most extensive disturbances affecting forest health in various geographical regions. Accurate and up-to-date knowledge of the spatial distribution of bark beetle-infested trees is critical for forest managers to effectively plan appropriate countermeasures and predict future bark beetle infestation dynamics. In this study, three scenarios for mapping bark beetle-induced tree mortality based on airborne hyperspectral data (HyMap) were examined including combining a genetic algorithm (GA) for feature selection with a supervised support vector machine (SVM) classification. The scenarios differed in how the mortality classes (used to extract the spectral signatures) were defined. Scenario 1 included three mortality classes, while Scenario 2 and 3 each included only one general mortality-related class but with different definitions. The classes, derived from the three scenarios, served as inputs into feature selection facilitated by the GA. The most stable bands, selected by the GA, were then used as input bands for a bootstrapped supervised SVM classification procedure. In two of the three scenarios, the trained classifier was additionally applied to a second independent hyperspectral image to evaluate the stability of the spectral signatures derived from the training samples. The classification procedure was further refined by varying the number of input bands and the input data types, where the bands not only included the original reflectances of the HyMap images but also a first order Savitzky–Golay derivation and continuum-removed spectra.The results of the study indicate that an accurate differentiation of three mortality classes was not possible with the applied methodology. The defined pre-visual “green mortality stage” class in particular was found to be heavily overestimated based on the qualitative analysis of the classified maps and validation statistics (error of commission 65% as shown by the user's accuracy).However, on the other hand, the mapping of dead trees was possible with notably high overall accuracies (OAs) (84%–96%). These results could also be transferred to the second independent hyperspectral image, in which the separation between the healthy trees and the dead trees was possible with OAs of 94%–97%. Somewhat lower but still reasonable OAs (76%–85%) were found when all defined classes were considered. The main confusion occurred between dead trees and sparsely vegetated soil.GA analysis showed that bands that notably contributed to high classification accuracies were frequently located in the visual part of the spectrum, close to the green peak (560nm), as well as around the chlorophyll absorption feature (680nm) and the red edge rise (690nm). In some of the tested cases, additional bands in the SWIR (1532nm) and one in the NIR (1076nm) were stably selected by the GA, which indicated the importance of these bands in the classification process.

Retrieving the Bioenergy Potential from Maize Crops Using Hyperspectral Remote Sensing

Biogas production from energy crops by anaerobic digestion is becoming increasingly important. The amount of biogas that can be produced per unit of biomass is referred to as the biomethane potential (BMP). For energy crops, the BMP varies among varieties and with crop state during the vegetation period. Traditional ways of analytical BMP determination are based on fermentation trials and require a minimum of 30 days. Here, we present a faster method for BMP retrievals using near infrared spectroscopy and partial least square regression (PLSR). PLSR prediction models were developed based on two different sets of spectral reflectance data: (i) laboratory spectra of silage samples and (ii) airborne imaging spectra (HyMap) of maize canopies under field (in situ) conditions. Biomass was sampled from 35 plots covering different maize varieties and the BMP was determined as BMP per mass (BMPFM, Nm3 biogas/t fresh matter (Nm3/t FM)) and BMP per area (BMParea, Nm3 biogas/ha (Nm3/ha)). We found that BMPFM significantly differs among maize varieties; it could be well retrieved from silage samples in the laboratory approach (Rcv2 = 0.82, n = 35), especially at levels >190 Nm3/t. In the in situ approach PLSR prediction quality declined (Rcv2 = 0.50, n = 20). BMParea, on the other hand, was found to be strongly correlated with total biomass, but could not be satisfactorily predicted using airborne HyMap imaging data and PLSR.

Suitability and adaptation of PROSAIL radiative transfer model for hyperspectral grassland studies

Methods are presented testing the suitability of PROSAIL radiative transfer model for analysing HyMap imaging spectroscopy data over grassland. The presented methods include forward modelling and cross-checks of 2D correlation plots. In the forward modelling, it is taken into account that the in situ data are not error free. To increase the predictive power of PROSAIL, a simple and fully automatic feature selection (FS) algorithm is presented identifying and discarding poorly modelled wavebands, yielding more reliable parameter retrievals.

HYPERSPECTRAL IMAGE RESOLUTION ENHANCEMENT BASED ON SPECTRAL UNMIXING AND INFORMATION FUSION

Abstract. Hyperspectral imaging sensors exibit high spectral resolution, but normally low spatial resolution. This leads to spectral signatures of pixels originating from different object types. Such pixels are called mixed pixels. Spectral unmixing methods can be employed to estimate the fractions of reflected light from the different objects within the pixel area. However, spectral unmixing does not provide any spatial information about the sources and therefore additional information is needed to precisely locate the sources. In order to restore the spatial information of hyperspectral images we propose a hyperspectral and multispectral image fusion method based on spectral unmixing. The algorithm is tested with HyMAP image data consisting of 125 spectral bands and a simulated multispectral image consisting of 8 bands.

ANOMALY DETECTION AND COMPARATIVE ANALYSIS OF HYDROTHERMAL ALTERATION MATERIALS TROUGH HYPERSPECTRAL MULTISENSOR DATA IN THE TURRIALBA VOLCANO

Abstract. The aim of this work is the comparative study of the presence of hydrothermal alteration materials in the Turrialba volcano (Costa Rica) in relation with computed spectral anomalies from multitemporal and multisensor data adquired in spectral ranges of the visible (VIS), short wave infrared (SWIR) and thermal infrared (TIR). We used for this purposes hyperspectral and multispectral images from the HyMAP and MASTER airborne sensors, and ASTER and Hyperion scenes in a period between 2002 and 2010. Field radiometry was applied in order to remove the atmospheric contribution in an empirical line method. HyMAP and MASTER images were georeferenced directly thanks to positioning and orientation data that were measured at the same time in the acquisition campaign from an inertial system based on GPS/IMU. These two important steps were allowed the identification of spectral diagnostic bands of hydrothermal alteration minerals and the accuracy spatial correlation. Enviromental impact of the volcano activity has been studied through different vegetation indexes and soil patterns. Have been mapped hydrothermal materials in the crater of the volcano, in fact currently active, and their surrounding carrying out a principal components analysis differentiated for a high and low absorption bands to characterize accumulations of kaolinite, illite, alunite and kaolinite+smectite, delimitating zones with the presence of these minerals. Spectral anomalies have been calculated on a comparative study of methods pixel and subpixel focused in thermal bands fused with high-resolution images. Results are presented as an approach based on expert whose main interest lies in the automated identification of patterns of hydrothermal altered materials without prior knowledge or poor information on the area.

An angular vegetation index for imaging spectroscopy data—Preliminary results on forest damage detection in the Bavarian National Park, Germany

A vegetation index (VI) is presented which is calculated as the inner angles of a triangle. The triangle is spanned between three distinct points (on a spectral curve of imaging spectroscopy data) which are defined for each individual pixel by the wavelength (x-axis) and the reflection value (y-axis). The ideal wavelengths of the three points are dependent on the response variable investigated. The case-study within this paper in which this angular VI is applied, aims at the development of an index to automatically detect spruce bark beetle infections in the Bavarian National Park, Germany. In order to determine the optimum wavelengths to separate damaged from non-damaged trees the three-angle-indices (TAIs) for all possible combinations of available wavelengths of HyMap imaging spectroscopy data in the vis–NIR-region (0.455–0.986μm) were calculated. The resulting 27,417 images served as input predictor variables to a genetic algorithm (GA) which used nearest centroid classifier as fitness functions to detect the most stable predictors and separate the six classes (three damage classes) defined within the study area.The fitness functions integrated in the GA reached classification accuracies of up to 94.8% when using forward selection models of the most stable genes featuring maximum 50 predictors. Based on those results, three from the original 27,417 predictors were extracted to map forest damages over the full image extent based on a support vector machines (SVMs) classification. We conducted the same experiment with 82 vegetation indices described in literature and achieved slightly lower GA overall accuracies of 86.8% using the nearest centroid classifier. The SVM maps produced with the three best VI predictors were comparable to the TAI results.

Utilization of hyperspectral image optical indices to assess the Norway spruce forest health status

The work is concerned with assessing the health status of trees of the Norway spruce species using airborne hyperspectral (HS) data (HyMap). The study was conducted in the Sokolov basin in the western part of the Czech Republic. First, statistics were employed to assess and validate diverse empirical models based on spectral information using the ground truth data (biochemically determined chlorophyll content). The model attaining the greatest accuracy (D718/D704∶RMSE = 0.2055 mg/g, R2 = 0.9370) was selected to produce a map of foliar chlorophyll concentrations (Cab). The Cab values retrieved from the HS data were tested together with other nonquantitative vegetation indicators derived from the HyMap image reflectance to create a statistical method allowing assessment of the condition of Norway spruce. As a result, we integrated the following HyMap derived parameters (Cab, REP, and SIPI) to assess the subtle changes in physiological status of the macroscopically undamaged foliage of Norway spruce within the four studied test sites. Our classification results and the previously published studies dealing with assessing the condition of Norway spruce using chlorophyll contents are in a good agreement and indicate that this method is potentially useful for general applicability after further testing and validation.