The new hyperspectral satellite prisma: Imagery for forest types discrimination

Different forest types based on different tree species composition may have similar spectral signatures if observed with traditional multispectral satellite sensors. Hyperspectral imagery, with a more continuous representation of their spectral behavior may instead be used for their classification. The new hyperspectral Precursore IperSpettrale della Missione Applicativa (PRISMA) sensor, developed by the Italian Space Agency, is able to capture images in a continuum of 240 spectral bands ranging between 400 and 2500 nm, with a spectral resolution smaller than 12 nm. The new sensor can be employed for a large number of remote sensing applications, including forest types discrimination. In this study, we compared the capabilities of the new PRISMA sensor against the well-known Sentinel-2 Multi-Spectral Instrument (MSI) in recognition of different forest types through a pairwise separability analysis carried out in two study areas in Italy, using two different nomenclature systems and four separability metrics. The PRISMA hyperspectral sensor, compared to Sentinel-2 MSI, allowed for a better discrimination in all forest types, increasing the performance when the complexity of the nomenclature system also increased. PRISMA achieved an average improvement of 40% for the discrimination between two forest categories (coniferous vs. broadleaves) and of 102% in the discrimination between five forest types based on main tree species groups

A review of geothermal mapping techniques using remotely sensed data

Exploiting geothermal (GT) resources requires first and foremost locating suitable areas for its development. Remote sensing offers a synoptic capability of covering large areas in real time and can cost effectively explore prospective geothermal sites not easily detectable using conventional survey methods, thus can aid in the prefeasibility stages of geothermal exploration. In this paper, we evaluate the techniques and approaches used in literature for the detection of prospective geothermal sites. Observations have indicated that, while thermal temperature anomalies detection have been applicable in areas of magmatic episodes and volcanic activity, poor resolution especially from space borne data is still a challenge. Consequently, thermal anomalies have been detected with some degree of success using airborne data, however, this is mostly in locations of known surface manifestations such as hot springs and fumaroles. The indirect identification of indicator minerals related to geothermal systems have been applied using multispectral and hyperspectral data in many studies. However, the effectiveness of the techniques relies on the sophistication and innovative digital image processing methods employed to sieve out relevant spectral information. The use of algorithms to estimate land surface temperature and heat fluxes are also applied to aid thermal anomaly detection, nevertheless, remote sensing techniques are still complementary to geologic, geophysical and geochemical survey methods. While not the first of its kind, this review is aimed at identifying new developments, with a focus on the trends and limitations intrinsic to the techniques and a look at current gaps and prospects for the future.Keywords: Geothermal, remote sensing, thermal anomalies, indicator minerals, multispectral, hyperspectra

Hyperspectral Remote Sensing of Vegetation and Agricultural Crops

There are now over 40 years of research in hyperspectral remote sensing (or imaging spectroscopy) of vegetation and agricultural crops (Thenkabail et al., 2011a). Even though much of the early research in hyperspectral remote sensing was overwhelmingly focused on minerals, now there is substantial literature in characterization, monitoring, modeling, and mapping of vegetation and agricultural crops using ground-based, platform-mounted, airborne, Unmanned Aerial Vehicle (UAV) mounted, and spaceborne hyperspectral remote sensing (Swatantran et al., 2011; Atzberger, 2013; Middleton et al., 2013; Schlemmer et al., 2013; Thenkabail et al., 2013; Udelhoven et al., 2013; Zhang et al., 2013). The state-of-the-art in hyperspectral remote sensing of vegetation and agriculture shows significant enhancement over conventional remote sensing, leading to improved and targeted modeling and mapping of specific agricultural characteristics such as: (a) biophysical and biochemical quantities (Galvão, 2011; Clark and Roberts, 2012), (b) crop type\species (Thenkabail et al., 2013), (c) management and stress factors such as nitrogen deficiency, moisture deficiency, or drought conditions (Delalieux et al., 2009; Gitelson, 2013; Slonecker et al., 2013), and (d) water use and water productivities (Thenkabail et al., 2013). At the same time, overcoming Hughes’ phenomenon or curse of dimensionality of data and data redundancy (Plaza et al., 2009) is of great importance to make rapid advances in a much wider utilization of hyperspectral data. This is because, for a specific application, a large number of hyperspectral bands are redundant (Thenkabail et al., 2013). Selecting the relevant bands will require the use of data mining techniques (Burger and Gowen, 2011) to focus on utilizing the optimal or best ones to maximize the efficiency of data use and reduce unnecessary computing..

Canopy structural attributes derived from AVIRIS imaging spectroscopy data in a mixed broadleaf/conifer forest

There is a well-established need within the remote sensing community for improved estimation and understanding of canopy structure and its influence on the retrieval of leaf biochemical properties. The main goal of this research was to assess the potential of optical spectral information from NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) to discriminate different canopy structural types. In the first phase, we assessed the relationships between optical metrics and canopy structural parameters obtained from LiDAR in terms of different canopy structural attributes (biomass (i.e., area under Vegetation Vertical Profile, VVPint), canopy height and vegetation complexity). Secondly, we identified and classified different âcanopy structural typesâ by integrating several structural traits using Random Forests (RF). The study area is a heterogeneous forest in Sierra National Forest in California (USA). AVIRIS optical properties were analyzed by means of several sets of variables, including single narrow band reflectance and 1st derivative, sub-pixel cover fractions, narrow-band indices, spectral absorption features, optimized normalized difference indices and Principal Component Analysis (PCA) components. Our results demonstrate that optical data contain structural information that can be retrieved. The first principal component, used as a proxy for albedo, was the most strongly correlated optical metric with vegetation complexity, and it also correlated well with biomass (VVPint) and height. In conifer forests, the shade fraction was especially correlated to vegetation complexity, while water-sensitive optical metrics had high correlations with biomass (VVPint). Single spectral band analysis results showed that correlations differ in magnitude and in direction, across the spectrum and by vegetation type and structural variable. This research illustrates the potential of AVIRIS to analyze canopy structure and to distinguish several structural types in a heterogeneous forest. Furthermore, RF using optical metrics derived from AVIRIS proved to be a powerful technique to generate maps of structural attributes. The results emphasize the importance of using the whole optical spectrum, since all spectral regions contributed to canopy structure assessment

Satellite remote sensing for hydrothermal alteration minerals mapping of subtle geothermal system in unexplored aseismic environment

Mapping prospective geothermal (GT) resources and monitoring associated surface manifestations can be challenging and prohibitively expensive in subtle systems especially when using conventional survey methods. Remote sensing offers a synoptic and costeffective capability for identification of GT systems. The objective of this research is to refine and develop methods of identifying unconventional GT systems by evaluating the applicability of the ASTER, Landsat 8 and Hyperion satellite data for mapping hydrothermal alteration indicator minerals as proxy for detecting subtle GT targets in unexplored aseismic settings. The study area is Yankari Park in North Eastern Nigeria, characterized by the thermal springs; Wikki, Mawulgo, Gwana and Dimmil. Spectral Angle Mapper (SAM), Linear spectral Unmixing (LSU) and Mixture Tuned Matched Filtering (MTMF) were comparatively evaluated by using image derived spectra and corresponding library spectra for mapping pixel abundance of GT indicator minerals in a novel and efficient manner. The results indicated that employing image derived spectra from field validated and laboratory verified regions of interest as reference, gives more accurate results than using library spectra around known alteration zones remotely detectable on the imagery. The MTMF provided high performance subpixel target detection with an accuracy of 50-100% and 70-100% subpixel abundance for argillicphyllic- silicic and propylitic alteration mineral assemblages respectively, as compared to less than 10% for the same endmembers when using library spectra. The MTMF is thus best suited for mapping alterations associated with subtle GT systems than the less selective LSU. The per-pixel SAM was unsuitable for target detection of alteration indicators of interest with poor overall accuracy of 33.81% and 0.24 Kappa coefficient at 0.02 radian angle. Results of mapping thermally anomalous pixels do not conform to known locations of the thermal springs signifying the limitations of the current thermal sensors in mapping low temperature GT systems even at 60m spatial resolution. However, examining the spatial correlation of the anomaly areas with the major geologic structure systems from geological map of the study area indicates a close affinity between them and with previously reported thermal gradients within heat insulating sedimentary formations. This study establishes the integrative applicability of Multispectral and Hyperspectral data for mapping subtle GT targets in unexplored regions using in-situ validated alteration mineral mapping and thermal anomaly detection. This has significant implication for the GT green energy industry as the developed methods and GT prospect map could aid the prefeasibility stage narrowing of targets for in-depth geophysical, geochemical, geothermometric and related surveys

Characterizing tree species in the Northwest Territories using spectral mixture analysis and multi-temporal satellite imagery

Natural resource management in northern boreal forests requires tree species identification for improved decision making. Satellite remote sensing provides a more cost-effective and time-efficient way to obtain this information in these large, remote, inaccessible areas. However, satellite signals are highly mixed due to increased tree shadows and visible understory vegetation in higher latitude, lower density open forests. Thus, methods used in southern forests are largely unsuitable. Therefore, spectral mixture analysis (SMA) was tested as it separates these signal components (trees, understory, shadow) at sub-pixel scales, allowing improved forest information. In this study, SMA was used to identify the dominant species near Fort Providence NWT using Landsat-5 Thematic Mapper imagery. An accuracy of 79 % was achieved for four species validated against 48 ground plots using multiple-date imagery acquired at different stages of the growing season. These positive results indicate SMA’s capability to retrieve species information of highly mixed open stands

Spectroscopic remote sensing of plant stress at leaf and canopy levelsusing the chlorophyll 680 nm absorption feature with continuumremoval

FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOThis paper explores the use of spectral feature analysis to detect plant stress in visible/near infrared wavelengths. A time series of close range leaf and canopy reflectance data of two plant species grown in hydrocarbon-contaminated soil was acquired with a portable spectrometer. The ProSpecTIR-VS airborne imaging spectrometer was used to obtain far range hyperspectral remote sensing data over the field experiment. Parameters describing the chlorophyll 680. nm absorption feature (depth, width, and area) were derived using continuum removal applied to the spectra. A new index, the Plant Stress Detection Index (PSDI), was calculated using continuum-removed values near the chlorophyll feature centre (680. nm) and on the green-edge (560 and 575. nm). Chlorophyll feature's depth, width and area, the PSDI and a narrow-band normalised difference vegetation index were evaluated for their ability to detect stressed plants. The objective was to analyse how the parameters/indices were affected by increasing degrees of plant stress and to examine their utility as plant stress indicators at the remote sensing level (e.g. airborne sensor). For leaf data, PSDI and the chlorophyll feature area revealed the highest percentage (67-70%) of stressed plants. The PSDI also proved to be the best constraint for detecting the stress in hydrocarbon-impacted plants with field canopy spectra and airborne imaging spectroscopy data. This was particularly true using thresholds based on the ASD canopy data and considering the combination of higher percentage of stressed plants detected (across the thresholds) and fewer false-positives.This paper explores the use of spectral feature analysis to detect plant stress in visible/near infrared wavelengths. A time series of close range leaf and canopy reflectance data of two plant species grown in hydrocarbon-contaminated soil was acquired with a portable spectrometer. The ProSpecTIR-VS airborne imaging spectrometer was used to obtain far range hyperspectral remote sensing data over the field experiment. Parameters describing the chlorophyll 680 nm absorption feature (depth, width, and area) were derived using continuum removal applied to the spectra. A new index, the Plant Stress Detection Index (PSDI), was calculated using continuum-removed values near the chlorophyll feature centre (680 nm) and on the green-edge (560 and 575 nm). Chlorophyll feature’s depth, width and area, the PSDI and a narrow-band normalised difference vegetation index were evaluated for their ability to detect stressed plants. The objective was to analyse how the parameters/indices were affected by increasing degrees of plant stress and to examine their utility as plant stress indicators at the remote sensing level (e.g. airborne sensor). For leaf data, PSDI and the chlorophyll feature area revealed the highest percentage (67–70%) of stressed plants. 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