Portable Spectroradiometer Research Articles

Hyperspectral Estimation of the Chlorophyll Content in Short-Term and Long-Term Restorations of Mangrove in Quanzhou Bay Estuary, China

The chlorophyll content can indicate the general health of vegetation, and can be estimated from hyperspectral data. The aim of this study is to estimate the chlorophyll content of mangroves at different stages of restoration in a coastal wetland in Quanzhou, China, using proximal hyperspectral remote sensing techniques. We determine the hyperspectral reflectance of leaves from two mangrove species, Kandelia candel and Aegiceras corniculatum, from short-term and long-term restoration areas with a portable spectroradiometer. We also measure the leaf chlorophyll content (SPAD value). We use partial-least-squares stepwise regression to determine the relationships between the spectral reflectance and the chlorophyll content of the leaves, and establish two models, a full-wave-band spectrum model and a red-edge position regression model, to estimate the chlorophyll content of the mangroves. The coefficients of determination for the red-edge position model and the full-wave-band model exceed 0.72 and 0.82, respectively. The inverted chlorophyll contents are estimated more accurately for the long-term restoration mangroves than for the short-term restoration mangroves. Our results indicate that hyperspectral data can be used to estimate the chlorophyll content of mangroves at different stages of restoration, and could possibly be adapted to estimate biochemical constituents in leaves.

Nitrogen and potassium deficiency identification in maize by image mining, spectral and true colour response

Nutrients namely nitrogen (N) and potassium (K) management is an important agronomic practice to attain higher yield. The present study was conducted to determine effective spectra ranges and significant component images of RGB intensities which restrict ineffective data to be processed in identifying N and K deficiency symptoms through image mining, hyperspectral and true color responses. Maize crop was grown under field condition as per the recommended package of practice. At seed development stage, the leaf reflectance and digital images were acquired from N and K deficient leaves along with control (N and K sufficient) leaves. A portable spectroradiometer capable of measuring the wavelength range of 350–1050 nm of the electromagnetic spectrum was used to collect spectral data. The digital image was acquired using 20.1 megapixel camera. The result indicated that N and K deficiency increased the leaf reflectance at two ranges of green (centered 555 nm) and red edge (centered 715 nm). The K deficient leaf showed increased reflectance at near infrared (NIR) region of the spectrum. Differences in spectral reflectance of the leaves were highly correlated to Red, Green and Blue intensity values of N and K deficient leaves. High values for the blue and red portions suggests that chlorophyll and other associated pigments are not as plentiful in the N and K deficient plants, and higher reflectance values in the green correlates with more yellow pigment and decreased plant functions. The results indicated that identification of nutritional deficiency symptoms through image mining techniques could yield improved accuracy using spectra range and significant component images of RGB intensities that resonate with the physiological changes in crop due to the deficiency.

Using Canopy Hyperspectral Reflectance to Predict Root Biomass Carbon and Nitrogen Content

Full assessment of soil carbon (C) and nitrogen (N) pools is necessary for long-term sustainability of agricultural production and provides information on plant health and nutrient cycling. A major component of nutrient cycling is plant root C and N. Although root C and N contribute to nutrient cycling, determination of these quantities is laborious and tedious and is, therefore, not commonly done. In this study we attempt to determine the feasibility of using remotely sensed canopy reflectance as a proxy to determine root C and N data of live, standing forages. The study site was the United States Department of Agriculture-Grazinglands Research Laboratory located in El Reno, Oklahoma. Twelve plots in each of two sites (a native, tallgrass prairie and an improved, Old World Bluestem pasture) were used for collection and measurement of root C and root N and measurement of canopy reflectance using a field portable hyperspectral spectroradiometer. Root and soil samples were then taken from under the remote sensed area for total C and N analysis using the combustion method. The results of this study indicated that it is feasible to predict root C and N, but further study is required to improve model accuracy.

Monitoring soil lead and zinc contents via combination of spectroscopy with extreme learning machine and other data mining methods

In order to limit pollution risk and develop proper remediation strategies, soil quality has to be controlled by rapid and sustainable monitoring measures. Visible and near-infrared reflectance spectroscopy (VisNIR) is an attractive surrogate to time-consuming and costly classical soil assessment protocols. It highly depends on selecting appropriate data mining methods for regression analysis. In this study, performance of a state of the art learning algorithm called extreme learning machine (ELM), was evaluated through comparing with the other calibration methods proposed in the literature for predicting lead (Pb) and Zinc (Zn) concentrations. Solid samples collected from a mine waste dump (n=120) were scanned using a Fieldspec3 portable spectroradiometer with a measurement range of (350–2500nm) in a laboratory. Transformation of the reflectance spectra to absorbance was followed by three pre-processing scenarios including Savitzky-Golay smoothing (SG), first derivative (FD) and second derivative (SD). Partial Least Square Regression (PLSR), Support Vector Machine (SVM) and neural networks with two learning algorithms models (back propagation and extreme learning machine), were calibrated on spectral features selected by genetic algorithm, and then applied to predict soil metal concentrations. The best prediction accuracy was obtained by FD-ELM method with R2p, RMSEp, concordance correlation coefficient and RPD values of 0.93, 63.01, 0.98 and 5.92 for Pb and 0.87, 167.90, 0.91 and 5.62 for Zn, respectively. Study of the prediction mechanism proved that element sorption by spectrally active Fe-oxide and clay contents of the soil was the major mechanism by which the spectrally featureless Pb and Zn ions can be predicted. The spatial patterns of predicted toxic elements showed that FD-ELM had the most similarity with those maps obtained by interpolating measured values. Over all, it is concluded that reflectance spectroscopy combined with the ELM algorithm is a rapid, inexpensive and accurate tool for indirect evaluation of Pb and Zn and mapping their spatial distribution in dumpsite soils of Sarcheshmeh copper mine.

Radiometric calibration evaluation for RSBs of Suomi-NPP/VIIRS and Aqua/MODIS based on the 2015 Dunhuang Chinese Radiometric Calibration Site in situ measurements

ABSTRACTThe Dunhuang Chinese Radiometric Calibration Site (CRCS), used for the vicarious calibration (VC) of reflective solar bands (RSBs), was determined as the primary radiometric calibration site for Chinese space-borne optical sensors and was also selected in 2008 by the Working Group on Calibration and Validation of the Committee on Earth Observation Satellites as one of the instrumented reference sites. In August 2015, an in situ measurement was carried out at the Dunhuang site to evaluate the RSB radiometric calibration of the Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (NPP) and Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua based on the reflectance-based method. A portable spectroradiometer was used in the experiment to obtain the surface reflectance, and the atmospheric parameters were obtained by sun photometers and radiosonde. A Dunhuang surface bidirectional reflectance distribution function model obtained during the field missions in 2008 and 2013 was implemented. Two days of in situ measurement data including 2 days of VIIRS data and 1 day of MODIS data were used for this evaluation. The results show that the radiometric calibration accuracy is within ±2% for most NPP/VIIRS and Aqua/MODIS RSBs based on the Dunhuang site. It should be noted that there is a relatively large difference in the NPP/VIIRS day–night band (DNB) and Aqua/MODIS band 7 results at the central wavelength of 2.1 μm, with biases of – 4.78% and – 5.71%, respectively. One factor contributing to the difference is the atmospheric transmittance calculation in these bands using the 6S radiative transfer model. If Moderate Resolution Atmospheric Transmission model is used for atmospheric transmittance correction, part of the bias of the MODIS band 7 and VIIRS DNB can be eliminated. However, the consistency of the VIIRS M11 and MODIS B7 is 3.47%, which is larger than that of the other bands.

In-situ plant hyperspectral sensing for early detection of soybean injury from dicamba

Drift of dicamba onto non-target crops is a major concern because it is highly active on susceptible crops even at low doses. Early detection of crop injury is critical in crop management. A field study was conducted to determine spectral characteristics of soybean (Progeny P4819LL) treated with dicamba. Drift deposition of dicamba was simulated by direct application at 0.05 to 1.0 times of the recommended label rate (0.56 kg [ai] ha−1) to soybean at the 5- to 6-trifloliolate leaf stage, approximately 6 weeks after planting. The canopy spectral measurements were taken at 24, 48, and 72 h after treatment (HAT) using a portable spectroradiometer in the 325–1075 nm spectral range on 3 randomly selected plants within each plot with device optimisation and data calibration. The results indicated that it was difficult to clearly differentiate the dose response of soybean to different dicamba spray rates within 72 HAT. Regardless of spray rates the soybean treated with dicamba could be clearly differentiated from untreated soybean from 24 to 72 HAT through spectral vegetation index analysis with anthocyanin reflectance and photochemical reflectance indices with accuracies at 24, 48, and 72 HAT ranging from 76 to 86%. Simulated dicamba drift injured soybean and reduced its yield by 71 and 90% at 0.05 and 0.1 times recommended rate, respectively. This study demonstrated that hyperspectral remote sensing has a potential in early detection of soybean injury from exposure to off-target dicamba drift at sub lethal rates in the field.

THE COMBINATION OF UAV SURVEY AND LANDSAT IMAGERY FOR MONITORING OF CROP VIGOR IN PRECISION AGRICULTURE

Abstract. Mapping of the with-in field variability of crop vigor has a long tradition with a success rate ranging from medium to high depending on the local conditions of the study. Information about the development of agronomical relevant crop parameters, such as above-ground biomass and crop nutritional status, provides high reliability for yield estimation and recommendation for variable rate application of fertilizers. The aim of this study was to utilize unmanned and satellite multispectral imaging for estimation of basic crop parameters during the growing season. The experimental part of work was carried out in 2014 at the winter wheat field with an area of 69 ha located in the South Moravia region of the Czech Republic. An UAV imaging was done in April 2014 using Sensefly eBee, which was equipped by visible and near infrared (red edge) multispectral cameras. For ground truth calibration the spectral signatures were measured on 20 sites using portable spectroradiometer ASD Handheld 2 and simultaneously plant samples were taken at BBCH 32 (April 2014) and BBCH 59 (Mai 2014) for estimation of above-ground biomass and nitrogen content. The UAV survey was later extended by selected cloud-free Landsat 8 OLI satellite imagery, downloaded from USGS web application Earth Explorer. After standard pre-processing procedures, a set of vegetation indices was calculated from remotely and ground sensed data. As the next step, a correlation analysis was computed among crop vigor parameters and vegetation indices. Both, amount of above-ground biomass and nitrogen content were highly correlated (r > 0.85) with ground spectrometric measurement by ASD Handheld 2 in BBCH 32, especially for narrow band vegetation indices (e.g. Red Edge Inflection Point). UAV and Landsat broadband vegetation indices varied in range of r = 0.5 – 0.7, highest values of the correlation coefficients were obtained for crop biomass by using GNDVI. In all cases results from BBCH 59 vegetation stage showed lower relationship to vegetation indices. Total amount of aboveground biomass was identified as the most important factor influencing the values of vegetation indices. Based on the results can be assumed that UAV and satellite monitoring provide reliable information about crop parameters for site specific crop management. The main difference of their utilization is coming from their specification and technical limits. Satellite survey can be used for periodic monitoring of crops as the indicator of their spatial heterogeneity within fields, but with low resolution (30 m per pixel for OLI). On the other hand UAV represents a special campaign aimed on the mapping of high-detailed spatial inputs for site specific crop management and variable rate application of fertilizers.

THE COMBINATION OF UAV SURVEY AND LANDSAT IMAGERY FOR MONITORING OF CROP VIGOR IN PRECISION AGRICULTURE

Mapping of the with-in field variability of crop vigor has a long tradition with a success rate ranging from medium to high depending on the local conditions of the study. Information about the development of agronomical relevant crop parameters, such as above-ground biomass and crop nutritional status, provides high reliability for yield estimation and recommendation for variable rate application of fertilizers. The aim of this study was to utilize unmanned and satellite multispectral imaging for estimation of basic crop parameters during the growing season. The experimental part of work was carried out in 2014 at the winter wheat field with an area of 69 ha located in the South Moravia region of the Czech Republic. An UAV imaging was done in April 2014 using Sensefly eBee, which was equipped by visible and near infrared (red edge) multispectral cameras. For ground truth calibration the spectral signatures were measured on 20 sites using portable spectroradiometer ASD Handheld 2 and simultaneously plant samples were taken at BBCH 32 (April 2014) and BBCH 59 (Mai 2014) for estimation of above-ground biomass and nitrogen content. The UAV survey was later extended by selected cloud-free Landsat 8 OLI satellite imagery, downloaded from USGS web application Earth Explorer. After standard pre-processing procedures, a set of vegetation indices was calculated from remotely and ground sensed data. As the next step, a correlation analysis was computed among crop vigor parameters and vegetation indices. Both, amount of above-ground biomass and nitrogen content were highly correlated (r > 0.85) with ground spectrometric measurement by ASD Handheld 2 in BBCH 32, especially for narrow band vegetation indices (e.g. Red Edge Inflection Point). UAV and Landsat broadband vegetation indices varied in range of r = 0.5 – 0.7, highest values of the correlation coefficients were obtained for crop biomass by using GNDVI. In all cases results from BBCH 59 vegetation stage showed lower relationship to vegetation indices. Total amount of aboveground biomass was identified as the most important factor influencing the values of vegetation indices. Based on the results can be assumed that UAV and satellite monitoring provide reliable information about crop parameters for site specific crop management. The main difference of their utilization is coming from their specification and technical limits. Satellite survey can be used for periodic monitoring of crops as the indicator of their spatial heterogeneity within fields, but with low resolution (30 m per pixel for OLI). On the other hand UAV represents a special campaign aimed on the mapping of high-detailed spatial inputs for site specific crop management and variable rate application of fertilizers.

Evaluating fertilizer applications on spectral behaviour of rice crop using remote sensing technique

Present experiments were conducted at the research farm of Indian Agricultural Research Institute, New Delhi during Kharif 1999 and Kharif 2001 to study the influence of nitrogenous fertilizer on the tilth of rice crop by remote sensing technique. Spectral radiance observations of the crop canopy were collected with the Portable Spectroradiometer which scanned from 330 nm to 1100 nm of electromagnetic spectrum range at 5nm interval (band-width). Normalized difference vegetation index was calculated for the both tillage practices, puddle and unpuddled situation at different growth stages for different fertilizer treatment. Fertilized plots were observed to have a higher value of BR than controlled ones throughout the crop growth period, both in puddled and unpuddled treatments. The higher values of band ratio were observed in puddled rice compared to the unpuddled rice irrespective of growth stages and fertilizer application levels. Statistical correlations were developed between NDVI (Normalized difference vegetation index) and RVI (Ratio vegetation index) with LAI (Leaf area index), DM (Dry matter) production and total leaf chlorophyll content. Second order polynomial equations were developed to correlate remotely sensed data with crop biometrics. Polynomial second order equations of 'band ratio' were found to be better fitted than NDVI with crop biometrics.

Improving the prediction of arsenic contents in agricultural soils by combining the reflectance spectroscopy of soils and rice plants

Visible and near-infrared reflectance spectroscopy provides a beneficial tool for investigating soil heavy metal contamination. This study aimed to investigate mechanisms of soil arsenic prediction using laboratory based soil and leaf spectra, compare the prediction of arsenic content using soil spectra with that using rice plant spectra, and determine whether the combination of both could improve the prediction of soil arsenic content. A total of 100 samples were collected and the reflectance spectra of soils and rice plants were measured using a FieldSpec3 portable spectroradiometer (350–2500nm). After eliminating spectral outliers, the reflectance spectra were divided into calibration (n=62) and validation (n=32) data sets using the Kennard-Stone algorithm. Genetic algorithm (GA) was used to select useful spectral variables for soil arsenic prediction. Thereafter, the GA-selected spectral variables of the soil and leaf spectra were individually and jointly employed to calibrate the partial least squares regression (PLSR) models using the calibration data set. The regression models were validated and compared using independent validation data set. Furthermore, the correlation coefficients of soil arsenic against soil organic matter, leaf arsenic and leaf chlorophyll were calculated, and the important wavelengths for PLSR modeling were extracted. Results showed that arsenic prediction using the leaf spectra (coefficient of determination in validation, Rv2=0.54; root mean square error in validation, RMSEv=12.99mgkg−1; and residual prediction deviation in validation, RPDv=1.35) was slightly better than using the soil spectra (Rv2=0.42, RMSEv=13.35mgkg−1, and RPDv=1.31). However, results also showed that the combinational use of soil and leaf spectra resulted in higher arsenic prediction (Rv2=0.63, RMSEv=11.94mgkg−1, RPDv=1.47) compared with either soil or leaf spectra alone. Soil spectral bands near 480, 600, 670, 810, 1980, 2050 and 2290nm, leaf spectral bands near 700, 890 and 900nm in PLSR models were important wavelengths for soil arsenic prediction. Moreover, soil arsenic showed significantly positive correlations with soil organic matter (r=0.62, p<0.01) and leaf arsenic (r=0.77, p<0.01), and a significantly negative correlation with leaf chlorophyll (r=−0.67, p<0.01). The results showed that the prediction of arsenic contents using soil and leaf spectra may be based on their relationships with soil organic matter and leaf chlorophyll contents, respectively. Although RPD of 1.47 was below the recommended RPD of >2 for soil analysis, arsenic prediction in agricultural soils can be improved by combining the leaf and soil spectra.

Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data

In order to exploit remote sensing data operationally for precision agriculture applications, efficient and automated methods are required for the accurate detection of vegetation, crops and different crop varieties. To this end, we have designed, developed and evaluated an object-based classification framework towards the detection of vineyards, the vine canopy extraction and the vine variety discrimination from very high resolution multispectral data. A novel set of spectral, spatial and textural features, as well as rules, segmentation scales and a set of parameters are proposed based on object-based image analysis. The validation of the developed methodology was carried out on multitemporal WorldView-2 satellite data at four different viticulture regions in Greece. Concurrent in situ canopy reflectance observations were acquired from a portable spectroradiometer during the field campaigns. The performed quantitative evaluation indicated that the developed approach managed in all cases to detect vineyards with high completeness and correctness detection rates, i.e., over 89%. The vine canopy extraction methodology was validated with overall accuracy (OA) rates of above 96%. The quantitative evaluation regarding the vine variety discrimination task, including experiments with up to six different varieties, reached OA rates above 85% at the parcel level. The combined analysis of the experimental results with the spectral signatures from the in situ reflectance data indicated that certain vine varieties (e.g., Merlot) presented distinct spectral patterns across the VNIR spectrum.

Selecting optimal hyperspectral bands to discriminate nitrogen status in durum wheat: a comparison of statistical approaches.

Hyperspectral data can provide prediction of physical and chemical vegetation properties, but data handling, analysis, and interpretation still limit their use. In this study, different methods for selecting variables were compared for the analysis of on-the-ground hyperspectral signatures of wheat grown under a wide range of nitrogen supplies. Spectral signatures were recorded at the end of stem elongation, booting, and heading stages in 100 georeferenced locations, using a 512-channel portable spectroradiometer operating in the 325-1075-nm range. The following procedures were compared: (i) a heuristic combined approach including lambda-lambda R(2) (LL R(2)) model, principal component analysis (PCA), and stepwise discriminant analysis (SDA); (ii) variable importance for projection (VIP) statistics derived from partial least square (PLS) regression (PLS-VIP); and (iii) multiple linear regression (MLR) analysis through maximum R-square improvement (MAXR) and stepwise algorithms. The discriminating capability of selected wavelengths was evaluated by canonical discriminant analysis. Leaf-nitrogen concentration was quantified on samples collected at the same locations and dates and used as response variable in regressive methods. The different methods resulted in differences in the number and position of the selected wavebands. Bands extracted through regressive methods were mostly related to response variable, as shown by the importance of the visible region for PLS and stepwise. Band selection techniques can be extremely useful not only to improve the power of predictive models but also for data interpretation or sensor design.

Sugar beet (Beta vulgaris L.) and thistle (Cirsium arvensis L.) discrimination based on field spectral data

Creeping thistle (Cirsium arvensis (L.) Scop.) is a perennial weed that causes yield loss in sugar beet (Beta vulgaris L.) crops. The weeds are usually mapped for site specific weed management because they tend to grow in patches. Remote sensing techniques have shown promising results in species discrimination and therefore provide potential for weed mapping. In this study we examined the feasibility of high-resolution imaging for sugar beet and thistle discrimination and proposed a protocol to select multispectral camera filters. Spectral samples from sugar beet and thistle were acquired with a field portable spectroradiometer under field conditions and Partial Least Squares Discriminant Analysis (PLS-DA) classification models were developed with 211 and 36 spectral features of 1.56 and 10 nm bandwidths, respectively. The classification rates obtained using these models were regarded as the maximum obtainable. Then, spectral responses of a multi-band camera equipped with the filter configuration proposed by the PLS-DA models were simulated. Finally, a simulation of crop-weed discrimination was made using small unmanned aerial vehicles (UAV)-based multispectral images. More than 95% of the thistles and 89% of the sugar beets were correctly classified when continuous spectral data were used with 1.56 and 10 nm bandwidths. Accuracy dropped to 93% of thistles identified and 84% of sugar beets correctly classified when only the four best bands were used. The validation based on aerial images showed that sugar beets and thistle plants could be discriminated in images if sufficient pure pixels containing leaf spectra were available, that is with spatial resolutions of 6 mm pixel−1 or finer.

Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy

The selection of calibration method is one of the main factors influencing measurement accuracy of soil properties estimation in visible and near infrared reflectance spectroscopy. In this study, the performance of three regression techniques, namely, partial least-squares regression (PLSR), support vector regression (SVR), and multivariate adaptive regression splines (MARS) were compared to identify the best method to assess organic matter (OM) and clay content in the salt-affected soils. One hundred and two soil samples collected from Northern Sinai, Egypt, were used as the data set for the calibration and validation procedures. The dry samples were scanned using a FieldSpec Pro FR Portable Spectroradiometer (Analytical Spectral Devices, ASD) with a measurement range of 350–2500nm. The spectra were subjected to seven pre-processed techniques, e.g., Savitzky–Golay (SG) smoothing, first derivative with SG smoothing (FD-SG), second derivative with SG smoothing (SD-SG), continuum removed reflectance (CR), standard normal variate and detrending (SNV-DT), multiplicative scatter correction (MSC) and extended MSC. The results of cross-validation showed that in most cases MARS models performed better than PLSR and SVR models. The best predictions were obtained using MARS calibration methods with CR prep-processing, yielding R2, root mean squared error (RMSE), and ratio of performance to deviation (RPD) values of 0.85, 0.19%, and 2.63, respectively, for OM; and 0.90, 5.32%, and 3.15, respectively, for clay content.

Mineralogy and spectral signature of reactive gossans, Victoria Island, NT, Canada

Gossans resulting from the oxidation of sulphide-rich bedrock occur sporadically across Canada, and these features are routinely indicated on geological maps published by the Geological Survey of Canada (GSC). In the Canadian Arctic Islands, some gossans display reactive zones in permafrost characterized by specific textures and mineralogical facies. In poorly-vegetated areas of the High Arctic, gossans can easily be detected on high-resolution satellite imagery. However, the mechanisms leading to their formation are poorly known. For this study, two reactive gossans located in the early Neoproterozoic Minto Inlier of Victoria Island, Northwest Territories, were investigated to determine if the deposits constitute (1) analogues of mine waste in permafrost and (2) vectors to economic resources. Mineralogy was determined in situ using a portable spectroradiometer, with follow-up laboratory measurements. Site 1, Gossan Hill, is a 75-m topographic high capped by a poorly consolidated, rust-coloured layer overlying pyrite-rich sands, and resembles a classic gossan. Site 2, Gossan Sill, underlies a mineralized gabbroic sill consisting of sulphide-rich veins and pods but appears to be inverted morphologically. Visible–Near Infrared–Short Wave Infrared spectral analyses of surficial materials at Gossan Hill indicate gypsum, goethite and jarosite, and X-ray diffraction (XRD) confirms their presence as well as quartz, illite and feldspar. At Sill Gossan, spectral analyses show gypsum and jarosite; XRD indicates variable amounts of goethite, hematite, pyrite, chlorite, calcite and feldspar. The positive relief and presence of concentric alteration zones at Gossan Hill suggest that cryogenic processes, typical of Arctic regions, are active. Water becomes expelled during the freezing cycle forcing material outwards and along with oxidation of the sulphides, forms variably coloured zones in gossanous material. The inverted stratigraphy at Sill Gossan is more problematic. Constant weathering and mass wasting from the overlying sill and pyrite-rich breccia provide fresh pyrite to the gossan on a regular (seasonal) basis.

Mapping of Asbestos Cement Roofs and Their Weathering Status Using Hyperspectral Aerial Images

The aims of this study were: (i) the mapping of asbestos cement roofs in an urban area; and (ii) the development of a spectral index related to the roof weathering status. Aerial images were collected through the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) sensor, which acquires data in 102 channels from the visible to the thermal infrared spectral range. An image based supervised classification was performed using the Spectral Angle Mapper (SAM) algorithm. The SAM was trained through a set of pixels selected on roofs of different materials. The map showed an average producer’s accuracy (PA) of 86% and a user’s accuracy (UA) of 89% for the asbestos cement class. A novel spectral index, the “Index of Surface Deterioration” (ISD), was defined based on measurements collected with a portable spectroradiometer on asbestos cement roofs that were characterized by different weathering statuses. The ISD was then calculated on the MIVIS images, allowing the distinction of two weathering classes (i.e., high and low). The asbestos cement map was handled in a Geographic Information System (GIS) in order to supply the municipalities with the cadastral references of each property having an asbestos cement roof. This tool can be purposed for municipalities as an aid to prioritize asbestos removal, based on roof weathering status.

Identificação dos padrões espectrais da alteração hidrotermal da Mina Uruguai, Caçapava do Sul (RS), utilizando espectros de reflectância experimental

Uma série de análises espectrais foi efetuada em amostras de rochas sedimentares eopaleozoicas afetadas por alteração hidrotermal associada às ocorrências de cobre na Mina Uruguai (Minas do Camaquã, RS). A base de dados contemplou espectros na faixa do visível e infravermelho próximo (350 a 2500 nm) obtidos através de leituras em um espectrorradiômetro portátil ASD FieldSpec-3 High-Resolution. A interpretação mineralógica das assinaturas espectrais foi efetuada com o apoio do software The Spectral Geologist (TSGTM Pro) que permitiu a discriminação das fases minerais presentes por comparação das feições de absorção no espectro analisado com feições similares de minerais de uma biblioteca espectral de referência. A partir da análise espectral das amostras da área de estudo, puderam ser identificados os seguintes minerais principais: caulinita, ilita, clorita, hematita e goetita. As assembleias minerais identificadas se mostraram consistentes com alteração hidrotermal sob condições epitermais de baixo enxofre descrita em estudos anteriores na região. Os resultados obtidos reforçaram o potencial de utilização da espectroscopia de reflectância em programas de exploração mineral, especialmente nas fases iniciais, mesmo em áreas com condições fisiográficas pouco favoráveis.

Low-weight and UAV-based Hyperspectral Full-frame Cameras for Monitoring Crops: Spectral Comparison with Portable Spectroradiometer Measurements

Low-weight and UAV-based Hyperspectral Full-frame Cameras for Monitoring Crops: Spectral Comparison with Portable Spectroradiometer Measurements

Spectral detection of near-surface moisture content and water-table position in northern peatland ecosystems

Wildland fire occurrence has been increasing in peatland ecosystems during recent decades. As such, there is a need for broadly applicable tools to detect and monitor controls on combustion such as surface peat moisture and water-table position. A field portable spectroradiometer was used to measure surface reflectance of two Sphagnum moss-dominated peatland experiments, one being an experimental study utilizing a factorial experiment of vegetation and water-table manipulations and the other being a field site located in the Upper Peninsula of Michigan that is broadly representative of northern peatland ecosystems. Relationships were developed correlating spectral indices to surface moisture as well as water-table position using the surface reflectance data. Spectral convolutions, or integrations of the hyperspectral data with the relative spectral response curves of multispectral sensors, were also applied to represent spectral sensitivity of commonly used Earth observing sensors. Band ratios previously used to monitor surface moisture with these sensors were assessed. Strong relationships with surface moisture and water-table position are evident for both the narrow-band indices as well as broad-band indices. This study also revealed a dependence of spectral relationships on changes in vegetation cover, particularly ericoid shrubs, within the vegetation manipulation experiment.

Empirical Estimation of Leaf Chlorophyll Density in Winter Wheat Canopies Using Sentinel-2 Spectral Resolution

A comparison between the sensitivities to leaf chlorophyll density at the canopy scale of several vegetation indices (VIs) obtained at different spectral resolutions was carried out using spectral reflectance collected in winter wheat field trials with different nitrogen fertilization levels. A total of 350 spectra were collected from experimental plots at Feekes growth stages 5, 6, and 9 using a portable spectroradiometer (ASD FieldSpec HH), along with Minolta SPAD measurements of leaf optical thickness as a proxy for leaf chlorophyll density. Indices based on visible and near-infrared (NIR) bands were obtained from average reflectance in spectral ranges corresponding to SPOT HRG and Sentinel-2 (S2) bands. Indices requiring a red-edge band were obtained from reflectance at the originally proposed VI wavelengths using the 1.6-nm nominal spectral resolution bandwidth of the spectroradiometer and from average reflectance in the S2 red-edge bands with the closest spectral position to VI originally proposed wavelengths. Among VIs obtained from Sentinel-2 bands MERIS terrestrial chlorophyll index, red-edge position and triangular chlorophyll index/optimized soil adjusted VI ratio (TCI/OSAVI) indices, obtainable at 20-m spatial resolution from future S2 red-edge bands, and chlorophyll VI (CVI), obtainable at 10 m from visible and NIR bands, were the best estimators of winter wheat leaf chlorophyll density. The sensitivity of the best-performing indices obtained from S2 bands to winter wheat with other conditions was addressed by the analysis of a large synthetic data set obtained using the PROSPECT-SAILH model in the direct mode. Analysis of the synthetic data set using Sentinel-2 spectral resolution indicates that the two leaf area index normalized (TCI/OSAVI and CVI) indices are better leaf chlorophyll estimators.