scholarly journals Monitoring the Severity of Pantana phyllostachysae Chao on Bamboo Using Leaf Hyperspectral Data

2021 ◽  
Vol 13 (20) ◽  
pp. 4146
Author(s):  
Xuying Huang ◽  
Zhanghua Xu ◽  
Xu Yang ◽  
Jingming Shi ◽  
Xinyu Hu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Gradient Boosting ◽  
Leaf Water Content ◽  
Leaf Nitrogen Content ◽  
Light Gradient ◽  
Bamboo Leaves ◽  
Remote Sensing Methods ◽  
The Impact

Effectively monitoring Pantana phyllostachysae Chao (PPC) is essential for the sustainable development of the bamboo industry. However, the morphological similarity between damaged and off-year bamboo imposes challenges in the monitoring. The knowledge on whether the severity of this pest could be effectively monitored by using remote sensing methods is very limited. To fill this gap, this study aimed to identify the PPC damage of moso bamboo leaves using hyperspectral data. Specifically, we investigated differences in relative chlorophyll content (RCC), leaf water content (LWC), leaf nitrogen content (LNC), and hyperspectral spectrum among healthy, damaged (mildly damage, moderately damage, severely damage), and off-year bamboo leaves. Then, the hyperspectral indices sensitive to pest damage were selected by recursive feature elimination (RFE). The PPC damage identification model was constructed using the light gradient boosting machine (LightGBM) algorithm. We designed two different scenarios, without (A) and with (B) off-year samples, to evaluate the impact of off-year leaves on identification results. The RCC, the LWC, and the LNC of damaged leaves generally showed clear declined trends with the deterioration of damaged severity. The RCC and the LNC of off-year leaves were significantly lower than those of healthy and damaged leaves, whereas the LWC of off-leaves was significantly different from that of damaged leaves. The pest infestation caused noticeable distortion of leaf spectrum, increases in red and shortwave infrared bands, and decreases in green and near-infrared bands. The magnitude of reflectance change increased with the pest severity. The reflectance of off-year leaves in visible and near-infrared regions was distinguishably higher than that of healthy and damaged leaves. The overall accuracy (OA) of the constructed model for the identification of leaves with different degrees of damage severity reached 81.51%. When off-year, healthy, and damaged leaves were lumped together, the OA of the constructed model decreased by 5%. About half of the off-year leaf samples were misclassified into the damaged group. The identification of off-year leaves is a challenge for monitoring PPC damage using hyperspectral data. These results can provide practical guidance for monitoring PPC using remote sensing methods.

scholarly journals Monitoring the Impact of Environmental Manipulation on Peatland Surface by Simple Remote Sensing Indices

2018 ◽  
Vol 23 ◽  
pp. 00030 ◽  
Author(s):  
Anshu Rastogi ◽  
Subhajit Bandopadhyay ◽  
Marcin Stróżecki ◽  
Radosław Juszczak
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Vegetation Index ◽  
Near Infrared ◽  
Near Infrared Reflectance ◽  
Area Index ◽  
Environmental Manipulation ◽  
Chlorophyll Index ◽  
The Impact ◽  
Remote Sensing Indices

The behaviour of nature depends on the different components of climates. Among these, temperature and rainfall are two of the most important components which are known to change plant productivity. Peatlands are among the most valuable ecosystems on the Earth, which is due to its high biodiversity, huge soil carbon storage, and its sensitivity to different environmental factors. With the rapid growth in industrialization, the climate change is becoming a big concern. Therefore, this work is focused on the behaviour of Sphagnum peatland in Poland, subjected to environment manipulation. Here it has been shown how a simple reflectance based technique can be used to assess the impact of climate change on peatland. The experimental setup consists of four plots with two kind of manipulations (control, warming, reduced precipitation, and a combination of warming and reduced precipitation). Reflectance data were measured twice in August 2017 under a clear sky. Vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI), near-infrared reflectance of vegetation (NIRv), MERIS terrestrial chlorophyll index (MTCI), Green chlorophyll index (CIgreen), Simple Ration (SR), and Water Band Index (WBI) were calculated to trace the impact of environmental manipulation on the plant community. Leaf Area Index of vascular plants was also measured for the purpose to correlate it with different VIs. The observation predicts that the global warming of 1°C may cause a significant change in peatland behaviour which can be tracked and monitored by simple remote sensing indices.

scholarly journals MAPPING OF FOLIAR CONTENT USING RADIATIVE TRANSFER MODELING AND VIS-NIR HYPERSPECTRAL CLOSE-RANGE REMOTE-SENSING

2015 ◽  
Vol XL-3/W3 ◽  
pp. 467-472 ◽  
Author(s):  
S. Jay ◽  
R. Bendoula ◽  
X. Hadoux ◽  
N. Gorretta
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Spatial Resolution ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Radiative Transfer Model ◽  
Integrating Sphere ◽  
Leaf Angle ◽  
Transfer Model

Most methods for retrieving foliar content from hyperspectral data are well adapted either to remote-sensing scale, for which each spectral measurement has a spatial resolution ranging from a few dozen centimeters to a few hundred meters, or to leaf scale, for which an integrating sphere is required to collect the spectral data. In this study, we present a method for estimating leaf optical properties from hyperspectral images having a spatial resolution of a few millimeters or centimeters. In presence of a single light source assumed to be directional, it is shown that leaf hyperspectral measurements can be related to the directional hemispherical reflectance simulated by the PROSPECT radiative transfer model using two other parameters. The first one is a multiplicative term that is related to local leaf angle and illumination zenith angle. The second parameter is an additive specular-related term that models BRDF effects. <br><br> Our model was tested on visible and near infrared hyperspectral images of leaves of various species, that were acquired under laboratory conditions. Introducing these two additional parameters into the inversion scheme leads to improved estimation results of PROSPECT parameters when compared to original PROSPECT. In particular, the RMSE for local chlorophyll content estimation was reduced by 21% (resp. 32%) when tested on leaves placed in horizontal (resp. sloping) position. Furthermore, inverting this model provides interesting information on local leaf angle, which is a crucial parameter in classical remote-sensing.

scholarly journals A Systematic Review of Hardware-Accelerated Compression of Remotely Sensed Hyperspectral Images

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 263
Author(s):  
Amal Altamimi ◽  
Belgacem Ben Ben Youssef
Keyword(s):  
Remote Sensing ◽  
Systematic Review ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Sensor Technology ◽  
Future Research ◽  
Sensing Applications ◽  
Remote Sensing Applications ◽  
The Impact

Hyperspectral imaging is an indispensable technology for many remote sensing applications, yet expensive in terms of computing resources. It requires significant processing power and large storage due to the immense size of hyperspectral data, especially in the aftermath of the recent advancements in sensor technology. Issues pertaining to bandwidth limitation also arise when seeking to transfer such data from airborne satellites to ground stations for postprocessing. This is particularly crucial for small satellite applications where the platform is confined to limited power, weight, and storage capacity. The availability of onboard data compression would help alleviate the impact of these issues while preserving the information contained in the hyperspectral image. We present herein a systematic review of hardware-accelerated compression of hyperspectral images targeting remote sensing applications. We reviewed a total of 101 papers published from 2000 to 2021. We present a comparative performance analysis of the synthesized results with an emphasis on metrics like power requirement, throughput, and compression ratio. Furthermore, we rank the best algorithms based on efficiency and elaborate on the major factors impacting the performance of hardware-accelerated compression. We conclude by highlighting some of the research gaps in the literature and recommend potential areas of future research.

Towards Remote Sensing of Atmospheric Trace Gases in the UV spectral range using Dual-Comb spectroscopy

2021 ◽  
Author(s):  
Clément Pivard ◽  
Sandrine Galtier ◽  
Patrick Rairoux
Keyword(s):  
Remote Sensing ◽  
Spectral Range ◽  
Near Infrared ◽  
Frequency Comb ◽  
Trace Gases ◽  
In Situ Monitoring ◽  
Atmospheric Trace Gases ◽  
The Impact ◽  
Uv Range

&lt;p&gt;The development of increasingly sensitive and robust instruments and new methodologies are essential to improve our understanding of the Earth&amp;#8217;s climate and air pollution. In this context, Dual-Comb spectroscopy (DCS) appears as an emerging spectroscopy methodology to detect in situ, without air-sampling, atmospheric trace-gases.&lt;/p&gt;&lt;p&gt;DCS is a Fourier-transform type experiment that takes advantage of mode-locked femtosecond (fs) pulses. This methodology appears highly relevant for atmosphere remote-sensing studies because of its very fast acquisition rate (&gt;kHz) that reduces the impact of atmospheric turbulences on the retrieved spectra. DCS has been successfully applied in near-infrared (NIR) spectral ranges for atmospheric greenhouse gas monitoring (water vapor, carbon dioxide, and methane) [1-2].&lt;/p&gt;&lt;p&gt;Its implementation in the UV range would offer a new spectroscopic intrumentation to target the most reactive species of the atmosphere (OH, HONO, BrO...) as they have their greatest absorption cross-sections in the UV range. UV-DCS would therefore be an answer to the lack of variability of today operationnal and in situ monitoring instrument for those reactive molecules.&lt;/p&gt;&lt;p&gt;We will present a potential light source for remote sensing UV-DCS and discuss the degree of immunity of UV-DCS to atmospheric turbulences. We will show to which extent the characteristics of the currently available UV sources are compatible with the unambiguous identification of UV absorbing gases by UV-DCS. We will finally present the performances of UV-DCS in terms of concentration detection limit for several UV absorbing molecules (OH, BrO, NO&lt;sub&gt;2&lt;/sub&gt;, OClO, HONO, CH&lt;sub&gt;2&lt;/sub&gt;O, SO&lt;sub&gt;2&lt;/sub&gt;). This sensitivity study has been recently published [3] and the main results will be presented.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;[1] Rieker, G.B.; Giorgetta, F.R.; Swann, W.C.; Kofler, J.; Zolot, A.M.; Sinclair, L.C.; Baumann, E.; Cromer, C.;Petron, G.; Sweeney, C.; et al. &amp;#171; Frequency-comb-based remote sensing of greenhouse gases over kilometer air Paths&amp;#160;&amp;#187;. Optica 1, p. 290&amp;#8211;298 (2014)&lt;/p&gt;&lt;p&gt;[2] Oudin, J.; Mohamed, A.K.; H&amp;#233;bert, P.J. &quot;IPDA LIDAR measurements on atmospheric CO2 and H2O using dual comb spectroscopy,&quot; Proc. SPIE 11180, International Conference on Space Optics &amp;#8212; ICSO 2018, p. 111802N (12 July 2019)&lt;/p&gt;&lt;p&gt;[3] Galtier, S.; Pivard, C.; Rairoux, P. Towards DCS in the UV Spectral Range for Remote Sensing of Atmospheric Trace Gases.&amp;#160;Remote Sens.,&amp;#160;12, p.3444 (2020)&lt;/p&gt;

Wildfire detection and monitoring by using PRISMA hyperspectral data and convolutional neural networks

2021 ◽  
Author(s):  
Dario Spiller ◽  
Luigi Ansalone ◽  
Nicolas Longépé ◽  
James Wheeler ◽  
Pierre Philippe Mathieu
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Near Infrared ◽  
Fire Detection ◽  
Input Image ◽  
Hyperspectral Data ◽  
Observation Data ◽  
Hyperspectral Camera

&lt;p&gt;Over the last few years, wildfires have become more severe and destructive, having extreme consequences on local and global ecosystems. Fire detection and accurate monitoring of risk areas is becoming increasingly important. Satellite remote sensing offers unique opportunities for mapping, monitoring, and analysing the evolution of wildfires, providing helpful contributions to counteract dangerous situations.&lt;/p&gt;&lt;p&gt;Among the different remote sensing technologies, hyper-spectral (HS) imagery presents nonpareil features in support to fire detection. In this study, HS images from the Italian satellite PRISMA (PRecursore IperSpettrale della Missione Applicativa) will be used. The PRISMA satellite, launched on 22 March 2019, holds a hyperspectral and panchromatic&amp;#160; payload which is able to acquire images with a worldwide coverage. The hyperspectral camera works in the spectral range of 0.4&amp;#8211;2.5 &amp;#181;m, with 66 and 173 channels in the VNIR (Visible and Near InfraRed) and SWIR (Short-Wave InfraRed) regions, respectively. The average spectral resolution is less than 10 nm on the entire range with an accuracy of &amp;#177;0.1 nm, while the ground sampling distance of PRISMA images is about 5 m and 30 m for panchromatic and hyperspectral camera, respectively.&lt;/p&gt;&lt;p&gt;This work will investigate how PRISMA HS images can be used to support fire detection and related crisis management. To this aim, deep learning methodologies will be investigated, as 1D convolutional neural networks to perform spectral analysis of the data or 3D convolutional neural networks to perform spatial and spectral analyses at the same time. Semantic segmentation of input HS data will be discussed, where an output image with metadata will be associated to each pixels of the input image. The overall goal of this work is to highlight how PRISMA hyperspectral data can contribute to remote sensing and Earth-observation data analysis with regard to natural hazard and risk studies focusing specially on wildfires, also considering the benefits with respect to standard multi-spectral imagery or previous hyperspectral sensors such as Hyperion.&lt;/p&gt;&lt;p&gt;The contributions of this work to the state of the art are the following:&lt;/p&gt;&lt;ul&gt;&lt;li&gt;Demonstrating the advantages of using PRISMA HS data over using multi-spectral data.&lt;/li&gt; &lt;li&gt;Discussing the potentialities of deep learning methodologies based on 1D and 3D convolutional neural networks to catch spectral (and spatial for the 3D case) dependencies, which is crucial when dealing with HS images.&lt;/li&gt; &lt;li&gt;Discussing the possibility and benefit to integrate HS-based approach in future monitoring systems in case of wildfire alerts and disasters.&lt;/li&gt; &lt;li&gt;Discussing the opportunity to design and develop future missions for HS remote sensing specifically dedicated for fire detection with on-board analysis.&lt;/li&gt; &lt;/ul&gt;&lt;p&gt;To conclude, this work will raise awareness in the potentialities of using PRISMA HS data for disasters monitoring with specialized focus on wildfires.&lt;/p&gt;

Using remote sensing to monitor peatland fire occurrence and recovery

2020 ◽  
Author(s):  
Kirsten Lees ◽  
Josh Buxton ◽  
Chris Boulton ◽  
Tim Lenton
Keyword(s):  
Remote Sensing ◽  
Great Britain ◽  
Management Practice ◽  
Google Earth ◽  
Small Scale ◽  
Fire Occurrence ◽  
Wildfire Occurrence ◽  
Visual Record ◽  
Remote Sensing Methods ◽  
The Impact

&lt;p&gt;Many peatland areas in Great Britain are managed as grouse moors, with regular burns as part of management practice to encourage heather growth. Remote sensing has the potential to monitor the size, location, and impact of these burns using new fine resolution satellites such as Sentinel-2. Google Earth Engine allows large areas to be analysed at small scale over several years, building up a visual record of fire occurrence. This study uses satellite data to map managed burns on several areas of moorland around Great Britain, and uses remote sensing methods to assess the impact of this management strategy on vegetation cover. The project also considers how areas subject to managed burns react to wildfire occurrence, with the 2018 Saddleworth wildfire as a case study.&lt;/p&gt;

scholarly journals Spektroskopi Reflektansi Sampel Tanah dan Batuan yang Mengandung Mineral Pembawa Unsur Tanah Jarang dan Radioaktif

EKSPLORIUM ◽  
2019 ◽  
Vol 40 (2) ◽  
pp. 89
Author(s):  
Arie Naftali Hawu Hede ◽  
Muhammad Anugrah Firdaus ◽  
Yogi La Ode Prianata ◽  
Mohamad Nur Heriawan ◽  
Syafrizal Syafrizal ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Rare Earth ◽  
Optical Sensors ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Infrared Range ◽  
Basic Principles ◽  
Spectral Signatures ◽  
Shortwave Infrared ◽  
Remote Sensing Methods

ABSTRAKSpektroskopi reflektansi merupakan salah satu metode nondestruktif untuk identifikasi mineral dan sebagai dasar dalam analisis pengindraan jauh (indraja) sensor optik. Penelitian ini bertujuan melakukan kajian penerapan spektroskopi reflektansi pada panjang gelombang 350–2.500 nm untuk sampel tanah dan batuan pembawa unsur tanah jarang (rare earth element-REE) dan radioaktif. Sampel diambil dari beberapa lokasi di Bangka Selatan dan Mamuju yang sebelumnya telah diidentifikasi memiliki potensi REE dan unsur radioaktif. Kurva reflektansi hasil analisis sampel dari Bangka Selatan menunjukan adanya kenampakan absorpsi yang menjadi karakteristik untuk kehadiran REE, dalam bentuk mineral monasit, zirkon, dan xenotime khususnya pada sampel yang berasal dari material tailing dan konsentrat bijih timah. Panjang gelombang yang menjadi kunci khususnya berada pada rentang visible-near infrared (VNIR; 400–1.300 nm). Sedangkan untuk sampel yang berasal dari Mamuju, yang merupakan daerah prospeksi mineral radioaktif, karakteristik spektral memperlihatkan beberapa panjang gelombang kunci terutama pada rentang shortwave infrared (1.300–2.500 nm). Hasil interpretasi menunjukkan mineral mayor berupa mineral lempung, sulfat, spesies NH4, dan mineral yang mengandung Al-OH lainnya, sedangkan untuk beberapa sampel pada panjang gelombang VNIR diidentifikasi mengandung mineral besi oksida/hidroksida. Hasil penelitian ini diharapkan dapat berguna untuk pemetaan eksplorasi REE dan radioaktif dengan menggunakan metode indraja.ABSTRACTReflectance spectroscopy is one of the nondestructive methods of mineral identification and is one of the basic principles in the remote sensing analysis using optical sensors. This research aimed at applying reflectance spectroscopy at 350–2,500 nm wavelength range for samples containing rare earth elements (REE) and radioactive minerals. Samples were taken from several locations in South Bangka and Mamuju that had previously been identified as potential location of REE and radioactive-bearing minerals. Reflectance data shows that there are absorption characteristics for REE-bearing minerals; monazite, zircon, and xenotime minerals especially from tailings and tin ore concentrate for the samples from South Bangka. The key wavelengths are specifically in the visible-near infrared range (VNIR; 400–1300 nm). For the samples from Mamuju, which is known as radioactive mineral prospecting areas, spectral characteristics provide information that there are spectral signatures in the shortwave infrared range (1,300–2,500 nm). The results of major mineral interpretations include clay minerals, sulfates, NH4 species, and other minerals containing Al-OH. However, some samples at the VNIR wavelength identified as iron oxide/hydroxide minerals. It is hoped that these results can be useful for REE and radioactive exploration mapping using remote sensing methods.

scholarly journals CAAS: an atmospheric correction algorithm for the remote sensing of complex waters

2012 ◽  
Vol 30 (1) ◽  
pp. 203-220 ◽  
Author(s):  
P. Shanmugam
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Near Infrared ◽  
Atmospheric Correction ◽  
Atmospheric Effects ◽  
Correction Algorithm ◽  
Ocean Colour ◽  
In Situ Data ◽  
Correction Scheme ◽  
The Impact

Abstract. The current SeaDAS atmospheric correction algorithm relies on the computation of optical properties of aerosols based on radiative transfer combined with a near-infrared (NIR) correction scheme (originally with assumptions of zero water-leaving radiance for the NIR bands) and several ancillary parameters to remove atmospheric effects in remote sensing of ocean colour. The failure of this algorithm over complex waters has been reported by many recent investigations, and can be attributed to the inadequate NIR correction and constraints for deriving aerosol optical properties whose characteristics are the most difficult to evaluate because they vary rapidly with time and space. The possibility that the aerosol and sun glint contributions can be derived in the whole spectrum of ocean colour solely from a knowledge of the total and Rayleigh-corrected radiances is developed in detail within the framework of a Complex water Atmospheric correction Algorithm Scheme (CAAS) that makes no use of ancillary parameters. The performance of the CAAS algorithm is demonstrated for MODIS/Aqua imageries of optically complex waters and yields physically realistic water-leaving radiance spectra that are not possible with the SeaDAS algorithm. A preliminary comparison with in-situ data for several regional waters (moderately complex to clear waters) shows encouraging results, with absolute errors of the CAAS algorithm closer to those of the SeaDAS algorithm. The impact of the atmospheric correction was also examined on chlorophyll retrievals with a Case 2 water bio-optical algorithm, and it was found that the CAAS algorithm outperformed the SeaDAS algorithm in terms of producing accurate pigment estimates and recovering areas previously flagged out by the later algorithm. These findings suggest that the CAAS algorithm can be used for applications focussing in quantitative assessments of the biological and biogeochemical properties in complex waters, and can easily be extended to other sensors such as OCM-2, MERIS and GOCI.

Stable isotopes as early indicators of high impact after plant invasion: A remote sensing perspective

2020 ◽  
Author(s):  
André Große-Stoltenberg ◽  
Christine Hellmann ◽  
Jan Thiele ◽  
Jens Oldeland ◽  
Christiane Werner
Keyword(s):  
Remote Sensing ◽  
Stable Isotopes ◽  
Native Species ◽  
Invasive Plant ◽  
Leaf Traits ◽  
High Impact ◽  
Hyperspectral Data ◽  
Water Cycling ◽  
Leaf N Content ◽  
The Impact

&lt;p&gt;High impact invasive plant species, such as the N-fixing and water-spending tree &lt;em&gt;Acacia longifolia&lt;/em&gt;, are a major threat to ecosystem functioning worldwide. For example, &lt;em&gt;Acacia'&lt;/em&gt;s impact on nutrient and water-cycling in Mediterranean dune ecosystems is well understood. However, early detection of such impacts remains challenging. Therefore, novel approaches are required to map functional indicators of high invader impact. Here, we tested in a real world context if the stable isotopes &amp;#948;&lt;sup&gt;13&lt;/sup&gt;C and &amp;#948;&lt;sup&gt;15&lt;/sup&gt;N could be such mappable indicators. First, we show that &lt;em&gt;A. longifolia &lt;/em&gt;differs regarding its biochemical leaf traits from the native species of the same growth form particularly regarding leaf N content as well as &amp;#948;&lt;sup&gt;13&lt;/sup&gt;C and &amp;#948;&lt;sup&gt;15&lt;/sup&gt;N. This may indicate a high impact on N and water cycling, and can be retrieved from hyperspectral data. Second, the impact of the invader on N cycling was mapped joining the spatial distribution of &amp;#948;&lt;sup&gt;15&lt;/sup&gt;N with airborne laserscanning data. Foliar &amp;#948;&lt;sup&gt;15&lt;/sup&gt;N of a non-fixing, native species increased in vicinity of invasive stands indicating an uptake of N previously fixed by the invader. Finally, those impacts possibly result in an increase of productivity of the whole dune ecosystem even when invader cover is low. This increase can be mapped integrating hyperspectral imagery with LiDAR data. Thus, there is potential to retrieve functional indicators of high impact including stable isotopes using remote sensing.&lt;/p&gt;

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