scholarly journals Forest species mapping using airborne hyperspectral APEX data

2016 ◽  
Vol 20 (1) ◽  
pp. 28-33 ◽  
Author(s):  
Giulia Tagliabue ◽  
Cinzia Panigada ◽  
Roberto Colombo ◽  
Francesco Fava ◽  
Chiara Cilia ◽  
...  
Keyword(s):  
Near Infrared ◽  
Hyperspectral Data ◽  
Forest Species ◽  
Blue Band ◽  
Vegetation Development ◽  
Infrared Spectral ◽  
Multi Temporal ◽  
High Resolution Images ◽  
Shortwave Infrared ◽  
Species Mapping

Abstract The accurate mapping of forest species is a very important task in relation to the increasing need to better understand the role of the forest ecosystem within environmental dynamics. The objective of this paper is the investigation of the potential of a multi-temporal hyperspectral dataset for the production of a thematic map of the dominant species in the Forêt de Hardt (France). Hyperspectral data were collected in June and September 2013 using the Airborne Prism EXperiment (APEX) sensor, covering the visible, near-infrared and shortwave infrared spectral regions with a spatial resolution of 3 m by 3 m. The map was realized by means of a maximum likelihood supervised classification. The classification was first performed separately on images from June and September and then on the two images together. Class discrimination was performed using as input 3 spectral indices computed as ratios between red edge bands and a blue band for each image. The map was validated using a testing set selected on the basis of a random stratified sampling scheme. Results showed that the algorithm performances improved from an overall accuracy of 59.5% and 48% (for the June and September images, respectively) to an overall accuracy of 74.4%, with the producer’s accuracy ranging from 60% to 86% and user’s accuracy ranging from 61% to 90%, when both images (June and September) were combined. This study demonstrates that the use of multi-temporal high-resolution images acquired in two different vegetation development stages (i.e., 17 June 2013 and 4 September 2013) allows accurate (overall accuracy 74.4%) local-scale thematic products to be obtained in an operational way.

scholarly journals Mapping Canopy Chlorophyll Content in a Temperate Forest Using Airborne Hyperspectral Data

2020 ◽  
Vol 12 (21) ◽  
pp. 3573
Author(s):  
J. Malin Hoeppner ◽  
Andrew K. Skidmore ◽  
Roshanak Darvishzadeh ◽  
Marco Heurich ◽  
Hsing-Chung Chang ◽  
...  
Keyword(s):  
Chlorophyll Content ◽  
Spectral Resolution ◽  
Near Infrared ◽  
Deciduous Forest ◽  
Mixed Forest ◽  
Hyperspectral Data ◽  
Least Squares Regression ◽  
Area Index ◽  
Management Interventions ◽  
Shortwave Infrared

Chlorophyll content, as the primary pigment driving photosynthesis, is directly affected by many natural and anthropogenic disturbances and stressors. Accurate and timely estimation of canopy chlorophyll content (CCC) is essential for effective ecosystem monitoring to allow for successful management interventions to occur. Hyperspectral remote sensing offers the possibility to accurately estimate and map canopy chlorophyll content. In the past, research has predominantly focused on the use of hyperspectral data on canopy chlorophyll content retrieval of crops and grassland ecosystems. Therefore, in this study, a temperate mixed forest, the Bavarian Forest National Park in Germany, was chosen as the study site. We compared different statistical models (narrowband vegetation indices (VIs), partial least squares regression (PLSR) and random forest (RF)) in their accuracy to predict CCC using airborne hyperspectral data. The airborne hyperspectral imagery was acquired by the AisaFenix sensor (623 bands; 3.5 nm spectral resolution in the visible near-infrared (VNIR) region, and 12 nm spectral resolution in the shortwave infrared (SWIR) region; 3 m spatial resolution) on July 6, 2017. In situ leaf chlorophyll content and leaf area index measurements were sampled from the upper canopy of coniferous, mixed, and deciduous forest stands in July and August 2017. The study yielded the highest retrieval accuracies with PLSR (root mean square error (RMSE) = 0.25 g/m2, R2 = 0.66). It further indicated specific spectral regions within the visible (390–400 nm and 470–540 nm), red edge (680–780 nm), near-infrared (1050–1100 nm) and shortwave infrared regions (2000–2270 nm) that were important for CCC retrieval. The results showed that forest CCC can be mapped with relatively high accuracies using image spectroscopy.

scholarly journals Estimating growth and photosynthetic properties of wheat grown in simulated saline field conditions using hyperspectral reflectance sensing and multivariate analysis

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Salah El-Hendawy ◽  
Nasser Al-Suhaibani ◽  
Majed Alotaibi ◽  
Wael Hassan ◽  
Salah Elsayed ◽  
...  
Keyword(s):  
Near Infrared ◽  
Agricultural Sector ◽  
Hyperspectral Data ◽  
Field Conditions ◽  
Least Squares Regression ◽  
Hyperspectral Reflectance ◽  
Full Spectrum ◽  
Salinity Levels ◽  
Shortwave Infrared ◽  
Related Trait

Abstract The timely estimation of growth and photosynthetic-related traits in an easy and nondestructive manner using hyperspectral data will become imperative for addressing the challenges of environmental stresses inherent to the agricultural sector in arid conditions. However, the handling and analysis of these data by exploiting the full spectrum remains the determining factor for refining the estimation of crop variables. The main objective of this study was to estimate growth and traits underpinning photosynthetic efficiency of two wheat cultivars grown under simulated saline field conditions and exposed to three salinity levels using hyperspectral reflectance information from 350–2500 nm obtained at two years. Partial least squares regression (PLSR) based on the full spectrum was applied to develop predictive models for estimating the measured parameters in different conditions (salinity levels, cultivars, and years). Variable importance in projection (VIP) of PLSR in combination with multiple linear regression (MLR) was implemented to identify important waveband regions and influential wavelengths related to the measured parameters. The results showed that the PLSR models exhibited moderate to high coefficients of determination (R2) in both the calibration and validation datasets (0.30–0.95), but that this range of R2 values depended on parameters and conditions. The PLSR models based on the full spectrum accurately and robustly predicted three of four parameters across all conditions. Based on the combination of PLSR-VIP and MLR analysis, the wavelengths selected within the visible (VIS), red-edge, and middle near-infrared (NIR) wavebands were the most sensitive to all parameters in all conditions, whereas those selected within the shortwave infrared (SWIR) waveband were effective for some parameters in particular conditions. Overall, these results indicated that the PLSR analysis and band selection techniques can offer a rapid and nondestructive alternative approach to accurately estimate growth- and photosynthetic-related trait responses to salinity stress.

scholarly journals HYPERSPECTRAL ANOMALY DETECTION IN URBAN SCENARIOS

2016 ◽  
Vol XLI-B7 ◽  
pp. 111-116 ◽  
Author(s):  
J. G. Rejas Ayuga ◽  
R. Martínez Marín ◽  
M. Marchamalo Sacristán ◽  
J. Bonatti ◽  
J. C. Ojeda
Keyword(s):  
Anomaly Detection ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Detection Methods ◽  
San Jose ◽  
First Results ◽  
Anomaly Detector ◽  
A New Technique ◽  
Spectral Ranges ◽  
Shortwave Infrared

We have studied the spectral features of reflectance and emissivity in the pattern recognition of urban materials in several single hyperspectral scenes through a comparative analysis of anomaly detection methods and their relationship with city surfaces with the aim to improve information extraction processes. Spectral ranges of the visible-near infrared (VNIR), shortwave infrared (SWIR) and thermal infrared (TIR) from hyperspectral data cubes of AHS sensor and HyMAP and MASTER of two cities, Alcalá de Henares (Spain) and San José (Costa Rica) respectively, have been used. <br><br> In this research it is assumed no prior knowledge of the targets, thus, the pixels are automatically separated according to their spectral information, significantly differentiated with respect to a background, either globally for the full scene, or locally by image segmentation. Several experiments on urban scenarios and semi-urban have been designed, analyzing the behaviour of the standard RX anomaly detector and different methods based on subspace, image projection and segmentation-based anomaly detection methods. A new technique for anomaly detection in hyperspectral data called DATB (Detector of Anomalies from Thermal Background) based on dimensionality reduction by projecting targets with unknown spectral signatures to a background calculated from thermal spectrum wavelengths is presented. First results and their consequences in non-supervised classification and extraction information processes are discussed.

VNIR-SWIR Superspectral Mineral Mapping: An Example from Cuprite, Nevada

2020 ◽  
Vol 86 (11) ◽  
pp. 695-700
Author(s):  
Kathleen E. Johnson ◽  
Krzysztof Koperski
Keyword(s):  
Near Infrared ◽  
Thermal Emission ◽  
High Spatial Resolution ◽  
Ground Truth ◽  
Random Forest Classifier ◽  
Hyperspectral Data ◽  
United States Geological Survey ◽  
Mineral Mapping ◽  
Mapping Techniques ◽  
Shortwave Infrared

Cuprite, Nevada, is a location well known for numerous studies of its hydrothermal mineralogy. This region has been used to validate geological interpretations of airborne hyperspectral imagery (AVIRIS HSI ), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER ) imagery, and most recently eight-band WorldView-3 shortwave infrared (SWIR ) imagery. WorldView-3 is a high-spatial-resolution commercial multispectral satellite sensor with eight visible-to-near-infrared (VNIR ) bands (0.42–1.04 μm) and eight SWIR bands (1.2–2.33 μm). We have applied mineral mapping techniques to all 16 bands to perform a geological analysis of the Cuprite, Nevada, location. Ground truth for the training and validation was derived from AVIRIS hyperspectral data and United States Geological Survey mineral spectral data for this location. We present the results of a supervised mineral-mapping classification applying a random-forest classifier. Our results show that with good ground truth, WorldView-3 SWIR + VNIR imagery produces an accurate geological assessment.

scholarly journals HYPERSPECTRAL ANOMALY DETECTION IN URBAN SCENARIOS

2016 ◽  
Vol XLI-B7 ◽  
pp. 111-116
Author(s):  
J. G. Rejas Ayuga ◽  
R. Martínez Marín ◽  
M. Marchamalo Sacristán ◽  
J. Bonatti ◽  
J. C. Ojeda
Keyword(s):  
Anomaly Detection ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Detection Methods ◽  
San Jose ◽  
First Results ◽  
Anomaly Detector ◽  
A New Technique ◽  
Spectral Ranges ◽  
Shortwave Infrared

We have studied the spectral features of reflectance and emissivity in the pattern recognition of urban materials in several single hyperspectral scenes through a comparative analysis of anomaly detection methods and their relationship with city surfaces with the aim to improve information extraction processes. Spectral ranges of the visible-near infrared (VNIR), shortwave infrared (SWIR) and thermal infrared (TIR) from hyperspectral data cubes of AHS sensor and HyMAP and MASTER of two cities, Alcalá de Henares (Spain) and San José (Costa Rica) respectively, have been used. <br><br> In this research it is assumed no prior knowledge of the targets, thus, the pixels are automatically separated according to their spectral information, significantly differentiated with respect to a background, either globally for the full scene, or locally by image segmentation. Several experiments on urban scenarios and semi-urban have been designed, analyzing the behaviour of the standard RX anomaly detector and different methods based on subspace, image projection and segmentation-based anomaly detection methods. A new technique for anomaly detection in hyperspectral data called DATB (Detector of Anomalies from Thermal Background) based on dimensionality reduction by projecting targets with unknown spectral signatures to a background calculated from thermal spectrum wavelengths is presented. First results and their consequences in non-supervised classification and extraction information processes are discussed.

scholarly journals Inconsistency among Landsat Sensors in Land Surface Mapping: A Comprehensive Investigation Based on Simulation

2021 ◽  
Vol 13 (7) ◽  
pp. 1383
Author(s):  
Feng Chen ◽  
Chenxing Wang ◽  
Yuansheng Zhang ◽  
Zhenshi Yi ◽  
Qiancong Fan ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Near Infrared ◽  
Specific Class ◽  
Spectral Indices ◽  
Surface Mapping ◽  
Multispectral Data ◽  
Multi Temporal ◽  
Time Series Analyses ◽  
Shortwave Infrared

Comprehensive investigations on the between-sensor comparability among Landsat sensors have been relatively limited compared with the increasing use of multi-temporal Landsat records in time series analyses. More seriously, the sensor-related difference has not always been considered in applications. Accordingly, comparisons were conducted among all Landsat sensors available currently, including Multispectral Scanner (MSS), Thematic Mappers (TM), Enhanced Thematic Mappers (ETM+), and Operational Land Imager (OLI)) in land cover mapping, based on a collection of synthesized, multispectral data. Compared to TM, OLI showed obvious between-sensor differences in channel reflectance, especially over the near infrared (NIR) and shortwave infrared (SWIR) channels, and presented positive bias in vegetation spectral indices. OLI did not always outperform TM and ETM+ in classification, which related to the methods used. Furthermore, the channels over SWIR of TM and its successors contributed largely to enhancement of inter-class separability and to improvement of classification. Currently, the inclusion of MSS data is confronted with significant challenges regarding the consistency of surface mapping. Considering the inconsistency among the Landsat sensors, it is applicable to generate a consistent time series of spectral indices through proper transformation models. Meanwhile, it suggests the generation of specific class(es) based on interest instead of including all classes simultaneously.

scholarly journals A MULTI-TEMPORAL APPROACH FOR DETECTING SNOW COVER AREA USING GEOSTATIONARY IMAGERY DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 511-512
Author(s):  
Hwa-Seon Lee ◽  
Kyu-Sung Lee
Keyword(s):  
Snow Cover ◽  
Near Infrared ◽  
Detection Method ◽  
Temporal Variations ◽  
High Temporal Resolution ◽  
Snow Cover Area ◽  
Swir Band ◽  
Multi Temporal ◽  
The Difference ◽  
Shortwave Infrared

In this study, we attempt to detect snow cover area using multi-temporal geostationary satellite imagery based on the difference of spectral and temporal characteristics between snow and clouds. The snow detection method is based on sequential processing of simple thresholds on multi-temporal GOCI data. We initially applied a simple threshold of blue reflectance and then root mean square deviation (RMSD) threshold of near infrared (NIR) reflectance that were calculated from time-series GOCI data. Snow cover detected by the proposed method was compared with the MODIS snow products. The proposed snow detection method provided very similar results with the MODIS cloud products. Although the GOCI data do not have shortwave infrared (SWIR) band, which can spectrally separate snow cover from clouds, the high temporal resolution of the GOCI was effective for analysing the temporal variations between snow and clouds.

scholarly journals Estimation of the Relative Abundance of Quartz to Clay Minerals Using the Visible–Near-Infrared–Shortwave-Infrared Spectral Region

2021 ◽  
pp. 000370282199830
Author(s):  
Nicolas Francos ◽  
Gila Notesco ◽  
Eyal Ben-Dor
Keyword(s):  
Clay Minerals ◽  
Spectral Region ◽  
Near Infrared ◽  
Raw Materials ◽  
Gradient Boosting ◽  
Quartz Content ◽  
Spectral Identification ◽  
Optical Region ◽  
Infrared Spectral ◽  
Shortwave Infrared

Quartz is the most abundant mineral on the earth’s surface. It is spectrally active in the longwave infrared (LWIR) region with no significant spectral features in the optical domain, i.e., visible–near-infrared–shortwave-infrared (Vis–NIR–SWIR) region. Several space agencies are planning to mount optical image spectrometers in space, with one of their missions being to map raw materials. However, these sensors are active across the optical region, making the spectral identification of quartz mineral problematic. This study demonstrates that indirect relationships between the optical and LWIR regions (where quartz is spectrally dominant) can be used to assess quartz content spectrally using solely the optical region. To achieve this, we made use of the legacy Israeli soil spectral library, which characterizes arid and semiarid soils through comprehensive chemical and mineral analyses along with spectral measurements across the Vis–NIR–SWIR region (reflectance) and LWIR region (emissivity). Recently, a Soil Quartz Clay Mineral Index (SQCMI) was developed using mineral-related emissivity features to determine the content of quartz, relative to clay minerals, in the soil. The SQCMI was highly and significantly correlated with the Vis–NIR–SWIR spectral region (R2 = 0.82, root mean square error (RMSE) = 0.01, ratio of performance to deviation (RPD) = 2.34), whereas direct estimation of the quartz content using a gradient-boosting algorithm against the Vis–NIR–SWIR region provided poor results (R2 = 0.45, RMSE = 15.63, RPD = 1.32). Moreover, estimation of the SQCMI value was even more accurate when only the 2000–2450 nm spectral range (atmospheric window) was used (R2 = 0.9, RMSE = 0.005, RPD = 1.95). These results suggest that reflectance data across the 2000–2450 nm spectral region can be used to estimate quartz content, relative to clay minerals in the soil satisfactorily using hyperspectral remote sensing means.

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