scholarly journals Can a Remote Sensing Approach with Hyperspectral Data Provide Early Detection and Mapping of Spatial Patterns of Black Bear Bark Stripping in Coast Redwoods?

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 378
Author(s):  
Shayne Magstadt ◽  
David Gwenzi ◽  
Buddhika Madurapperuma
Keyword(s):  
Remote Sensing ◽  
Early Detection ◽  
Spatial Patterns ◽  
Black Bear ◽  
Hyperspectral Data ◽  
Spectral Information ◽  
Electromagnetic Spectrum ◽  
Bark Stripping ◽  
Monitoring Method ◽  
Individual Tree

The prevalence of black bear (Ursus americanus) bark stripping in commercial redwood (Sequoia sempervirens (D. Don) Endl.) timber stands has been increasing in recent years. This stripping is a threat to commercial timber production because of the deleterious effects on redwood tree fitness. This study sought to unveil a remote sensing method to detect these damaged trees early and map their spatial patterns. By developing a timely monitoring method, forest timber companies can manipulate their timber harvesting routines to adapt to the consequences of the problem. We explored the utility of high spatial resolution UAV-collected hyperspectral imagery as a means for early detection of individual trees stripped by black bears. A hyperspectral sensor was used to capture ultra-high spatial and spectral information pertaining to redwood trees with no damage, those that have been recently attacked by bears, and those with old bear damage. This spectral information was assessed using the Jeffries-Matusita (JM) distance to determine regions along the electromagnetic spectrum that are useful for discerning these three-health classes. While we were able to distinguish healthy trees from trees with old damage, we were unable to distinguish healthy trees from recently damaged trees due to the inherent characteristics of redwood tree growth and the subtle spectral changes within individual tree crowns for the time period assessed. The results, however, showed that with further assessment, a time window may be identified that informs damage before trees completely lose value.

scholarly journals EARLY DETECTION OF FOREST DROUGHT STRESS WITH VERY HIGH RESOLUTION STEREO AND HYPERSPECTRAL IMAGERY

2020 ◽  
Vol V-2-2020 ◽  
pp. 781-787
Author(s):  
J. Tian ◽  
T. Schneider ◽  
C. Kempf ◽  
Y. Xia ◽  
M. Lusseau ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Drought Stress ◽  
Early Detection ◽  
Regional Scale ◽  
Satellite Image ◽  
3D Models ◽  
Middle Part ◽  
Individual Tree ◽  
Forest Sites ◽  
Study Sites

Abstract. The project ‘Application of remote sensing for the early detection of drought stress at vulnerable forest sites (ForDroughtDet)’ is funded by the German Federal Agency of Agriculture and Food and aims to detect drought stress in an early phase using remote sensing techniques. In this project, three test sites in the south and middle part of Germany are selected. Three levels of observation and analyses are performed. In the first level, close-range stereo images and spectral information are captured with a research crane in Kranzberg forest. In the second level, three study sites are imaged twice in three years by airborne hyperspectral and stereo cameras. In the third level, the drought stress detection approach will be transferred to regional scale by satellite image. In this paper, we will briefly report our results from the first and second levels. In the first level, 3D models of the forest canopies are generated with the MC-CNN based dense matching approaches, with which the 3D shapes of the stressed and healthy trees are analysed. In addition, for the spectral analyses, different chlorophyll-sensitive indices are calculated and compared for the stressed and healthy trees. In order to further analyse the tree drought stress in the second level, a novel individual tree crown (ITC) segmentation approach is proposed and tested on the airborne stereo dataset.

scholarly journals METHODS OF DETECTION OF DISEASES ON WHEAT CROPS ACCORDING TO REMOTE SENSING (overview)

2019 ◽  
Vol 48 (6) ◽  
pp. 76-89
Author(s):  
O. A. Dubrovskaya ◽  
T. A. Gurova ◽  
I. A. Pestunov ◽  
K. Yu. Kotov
Keyword(s):  
Remote Sensing ◽  
Early Detection ◽  
Crop Production ◽  
Crop Protection ◽  
Hyperspectral Data ◽  
Fungal Diseases ◽  
Crop Damage ◽  
Optical Methods ◽  
Agricultural Crops ◽  
Wheat Diseases

Nowadays multi- and hyperspectral data of remote sensing is widely used in many countries worldwide for agricultural lands monitoring. The issue of their application for detection and assessment of infestation of agricultural crops, damage from diseases and weeds is understudied both in Russia and abroad. Early detection and accurate diagnosis of various wheat diseases are key factors in crop production, contributing to the reduction of qualitative and quantitative crop losses, as well as improving the effectiveness of protective measures. The paper presents a review of up-to-date methods for detecting diseases and assessing the extent of crop damage by remote sensing of wheat using optical imaging systems, the most promising of which is hyperspectral imaging equipment. The identification spectra of healthy plants and the ones with signs of damage from the main fungal diseases as well as the correlation of spectra with the degree of damage are shown. To be able to effectively use the results of diagnostics and detection of diseases, the informational value of the spectral indices of vegetation in the detection of diseases is presented. A table of vegetation indices is given, calculated from the values of reflection coefficients in wide and narrow spectral ranges when determining wheat diseases. The use of optical methods in the monitoring of the main fungal diseases of wheat will accurately identify lesions of crops, reliably diagnose diseases and the extent of plant damage from diseases, and thereby provide support to agricultural producers in decision-making on timely and effective crop protection measures. The results of the review will be used to develop digital technology of early detection and lesion focalization of spring wheat and other agricultural crops.

scholarly journals Früherkennung von Buchdruckerbefall dank Fern erkundung: Was ist schon möglich?

2020 ◽  
Vol 171 (1) ◽  
pp. 36-43 ◽  
Author(s):  
Luzia Götz ◽  
Achilleas Psomas ◽  
Harald Bugmann
Keyword(s):  
Remote Sensing ◽  
Early Detection ◽  
Bark Beetle ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Cost Effective ◽  
Hyperspectral Data ◽  
Time Saving ◽  
Federal Institute ◽  
Institute Of Technology

Early detection of bark beetle infestations by remote sensing: what is feasible today? Infestation by the Norway spruce (Picea abies) bark beetle (Ips typographus) in uniform forest stands of the high montane and subalpine stage is a major challenge for management. It is impossible to identify in time all susceptible or already infested spruces in the often steep terrain solely by terrestrial observations and to prevent the proliferation of the beetle. A time-saving, cost-effective and effective method for finding these spruces is necessary and remote sensing techniques appear promising. Therefore, we investigated the potential of hyperspectral remote sensing data for the early detection of stressed or infested spruces using a case study in the experimental forest of the Swiss Federal Institute of Technology Zurich (ETHZ) in Sedrun. The approach that we developed is based on a combination of field surveys, hyperspectral data, vegetation indices calculated from these and their classification into the three classes “dead”, “stressed” and “healthy” using Random Forests, a machine-learning approach. We demonstrate that stressed spruces can be identified with this approach, but it is not yet ready for operational use. In particular, a slope-specific calibration of the method is necessary, which makes practical application impossible.

scholarly journals Atmospheric Compensation of PRISMA Data by Means of a Learning Based Approach

2021 ◽  
Vol 13 (15) ◽  
pp. 2967
Author(s):  
Nicola Acito ◽  
Marco Diani ◽  
Gregorio Procissi ◽  
Giovanni Corsini
Keyword(s):  
Spectral Range ◽  
Near Infrared ◽  
Random Noise ◽  
Synthetic Data ◽  
Regression Function ◽  
Short Wave ◽  
Hyperspectral Data ◽  
Electromagnetic Spectrum ◽  
Great Opportunity ◽  
Atmospheric Compensation

Atmospheric compensation (AC) allows the retrieval of the reflectance from the measured at-sensor radiance and is a fundamental and critical task for the quantitative exploitation of hyperspectral data. Recently, a learning-based (LB) approach, named LBAC, has been proposed for the AC of airborne hyperspectral data in the visible and near-infrared (VNIR) spectral range. LBAC makes use of a parametric regression function whose parameters are learned by a strategy based on synthetic data that accounts for (1) a physics-based model for the radiative transfer, (2) the variability of the surface reflectance spectra, and (3) the effects of random noise and spectral miscalibration errors. In this work we extend LBAC with respect to two different aspects: (1) the platform for data acquisition and (2) the spectral range covered by the sensor. Particularly, we propose the extension of LBAC to spaceborne hyperspectral sensors operating in the VNIR and short-wave infrared (SWIR) portion of the electromagnetic spectrum. We specifically refer to the sensor of the PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission, and the recent Earth Observation mission of the Italian Space Agency that offers a great opportunity to improve the knowledge on the scientific and commercial applications of spaceborne hyperspectral data. In addition, we introduce a curve fitting-based procedure for the estimation of column water vapor content of the atmosphere that directly exploits the reflectance data provided by LBAC. Results obtained on four different PRISMA hyperspectral images are presented and discussed.

scholarly journals Using a Groundwater Adjusted Water Balance Approach and Copulas to Evaluate Spatial Patterns and Dependence Structures in Remote Sensing Derived Evapotranspiration Products

2021 ◽  
Vol 13 (5) ◽  
pp. 853
Author(s):  
Mohsen Soltani ◽  
Julian Koch ◽  
Simon Stisen
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Principal Component ◽  
Small Scale ◽  
Dependence Structure ◽  
Test Case ◽  
Catchment Scale ◽  
Balance Approach

This study aims to improve the standard water balance evapotranspiration (WB ET) estimate, which is typically used as benchmark data for catchment-scale ET estimation, by accounting for net intercatchment groundwater flow in the ET calculation. Using the modified WB ET approach, we examine errors and shortcomings associated with the long-term annual mean (2002–2014) spatial patterns of three remote-sensing (RS) MODIS-based ET products from MODIS16, PML_V2, and TSEB algorithms at 1 km spatial resolution over Denmark, as a test case for small-scale, energy-limited regions. Our results indicate that the novel approach of adding groundwater net in water balance ET calculation results in a more trustworthy ET spatial pattern. This is especially relevant for smaller catchments where groundwater net can be a significant component of the catchment water balance. Nevertheless, large discrepancies are observed both amongst RS ET datasets and compared to modified water balance ET spatial pattern at the national scale; however, catchment-scale analysis highlights that difference in RS ET and WB ET decreases with increasing catchment size and that 90%, 87%, and 93% of all catchments have ∆ET < ±150 mm/year for MODIS16, PML_V2, and TSEB, respectively. In addition, Copula approach captures a nonlinear structure of the joint relationship with multiple densities amongst the RS/WB ET products, showing a complex dependence structure (correlation); however, among the three RS ET datasets, MODIS16 ET shows a closer spatial pattern to the modified WB ET, as identified by a principal component analysis also. This study will help improve the water balance approach by the addition of groundwater net in the ET estimation and contribute to better understand the true correlations amongst RS/WB ET products especially over energy-limited environments.

scholarly journals Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 742
Author(s):  
Canh Nguyen ◽  
Vasit Sagan ◽  
Matthew Maimaitiyiming ◽  
Maitiniyazi Maimaitijiang ◽  
Sourav Bhadra ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Viral Diseases ◽  
Support Vector ◽  
Spectral Features ◽  
Feature Spaces

Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages. An experiment was set up at a test site at South Farm Research Center, Columbia, MO, USA (38.92 N, −92.28 W), with two grapevine groups, namely healthy and GVCV-infected, while other conditions were controlled. Images of each vine were captured by a SPECIM IQ 400–1000 nm hyperspectral sensor (Oulu, Finland). Hyperspectral images were calibrated and preprocessed to retain only grapevine pixels. A statistical approach was employed to discriminate two reflectance spectra patterns between healthy and GVCV vines. Disease-centric vegetation indices (VIs) were established and explored in terms of their importance to the classification power. Pixel-wise (spectral features) classification was performed in parallel with image-wise (joint spatial–spectral features) classification within a framework involving deep learning architectures and traditional machine learning. The results showed that: (1) the discriminative wavelength regions included the 900–940 nm range in the near-infrared (NIR) region in vines 30 days after sowing (DAS) and the entire visual (VIS) region of 400–700 nm in vines 90 DAS; (2) the normalized pheophytization index (NPQI), fluorescence ratio index 1 (FRI1), plant senescence reflectance index (PSRI), anthocyanin index (AntGitelson), and water stress and canopy temperature (WSCT) measures were the most discriminative indices; (3) the support vector machine (SVM) was effective in VI-wise classification with smaller feature spaces, while the RF classifier performed better in pixel-wise and image-wise classification with larger feature spaces; and (4) the automated 3D convolutional neural network (3D-CNN) feature extractor provided promising results over the 2D convolutional neural network (2D-CNN) in learning features from hyperspectral data cubes with a limited number of samples.

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