scholarly journals Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors

Photonics ◽  
2021 ◽  
Vol 8 (12) ◽  
pp. 582
Author(s):  
Anastasiia Kior ◽  
Vladimir Sukhov ◽  
Ekaterina Sukhova
Keyword(s):  
Remote Sensing ◽  
Environmental Conditions ◽  
Plant Biomass ◽  
Optical Methods ◽  
Physiological And Biochemical Characteristics ◽  
Physiological Processes ◽  
Measurement And Analysis ◽  
Plant Monitoring ◽  
Vegetation Water ◽  
Reflectance Indices

Environmental conditions are very changeable; fluctuations in temperature, precipitation, illumination intensity, and other factors can decrease a plant productivity and crop. The remote sensing of plants under these conditions is the basis for the protection of plants and increases their survivability. This problem can be solved through measurements of plant reflectance and calculation of reflectance indices. Reflectance indices are related to the vegetation biomass, specific physiological processes, and biochemical compositions in plants; the indices can be used for both short-term and long-term plant monitoring. In our review, we considered the applications of reflectance indices in plant remote sensing. In Optical Methods and Platforms of Remote Sensing of Plants, we briefly discussed multi- and hyperspectral imaging, including descriptions of multispectral and hyperspectral cameras with different principles and their efficiency for the remote sensing of plants. In Main Reflectance Indices, we described the main reflectance indices, including vegetation, water, and pigment reflectance indices, as well as the photochemical reflectance index and its modifications. We focused on the relationships of leaf reflectance and reflectance indices to plant biomass, development, and physiological and biochemical characteristics. In Problems of Measurement and Analysis of Reflectance Indices, we discussed the methods of the correction of the reflectance indices that can be used for decreasing the influence of environmental conditions (mainly illumination, air, and soil) and plant characteristics (orientation of leaves, their thickness, and others) on their measurements and the analysis of the plant remote sensing. Additionally, the variability of plants was also considered as an important factor that influences the results of measurement and analysis.

scholarly journals Relation of Photochemical Reflectance Indices Based on Different Wavelengths to the Parameters of Light Reactions in Photosystems I and II in Pea Plants

2020 ◽  
Vol 12 (8) ◽  
pp. 1312
Author(s):  
Ekaterina Sukhova ◽  
Vladimir Sukhov
Keyword(s):  
Remote Sensing ◽  
Pulse Amplitude ◽  
Ecological Monitoring ◽  
Photochemical Quenching ◽  
Photosynthetic Parameters ◽  
Pam Fluorometer ◽  
Physiological Processes ◽  
Non Photochemical Quenching ◽  
Induced Changes ◽  
Reflectance Indices

Measurement and analysis of the numerous reflectance indices of plants is an effective approach for the remote sensing of plant physiological processes in agriculture and ecological monitoring. A photochemical reflectance index (PRI) plays an important role in this kind of remote sensing because it can be related to early changes in photosynthetic processes under the action of stressors (excess light, changes in temperature, drought, etc.). In particular, we previously showed that light-induced changes in PRIs could be strongly related to the energy-dependent component of the non-photochemical quenching in photosystem II. The aim of the present work was to undertake comparative analysis of the efficiency of using light-induced changes in PRIs (ΔPRIs) based on different wavelengths for the estimation of the parameters of photosynthetic light reactions (including the parameters of photosystem I). Pea plants were used in the investigation; the photosynthetic parameters were measured using the pulse-amplitude-modulated (PAM) fluorometer Dual-PAM-100 and the intensities of the reflected light were measured using the spectrometer S100. The ΔPRIs were calculated as ΔPRI(band,570), where the band was 531 nm for the typical PRI and 515, 525, 535, 545, or 555 nm for modified PRIs; 570 nm was the reference wavelength for all PRIs. There were several important results: (1) ∆PRI(525,570), ∆PRI(531,570), ∆PRI(535,570), and ∆PRI(545,570) could be used for estimation of most of the photosynthetic parameters under light only or under dark only conditions. (2) The combination of dark and light conditions decreased the efficiency of ∆PRIs for the estimation of the photosynthetic parameters; ∆PRI(535,570) and ∆PRI(545,570) had maximal efficiency under these conditions. (3) ∆PRI(515,570) and ∆PRI(525,570) mainly included the slow-relaxing component of PRI; in contrast, ∆PRI(531,570), ∆PRI(535,570), ∆PRI(545,570), and ∆PRI(555,570) mainly included the fast-relaxing component of PRI. These components were probably caused by different mechanisms.

Optical Remote Sensing of Vegetation Water Content

2018 ◽  
pp. 183-200
Author(s):  
Colombo Roberto ◽  
Busetto Lorenzo ◽  
Meroni Michele ◽  
Rossini Micol ◽  
Panigada Cinzia
Keyword(s):  
Remote Sensing ◽  
Water Content ◽  
Optical Remote Sensing ◽  
Vegetation Water Content ◽  
Vegetation Water

scholarly journals Deep Learning Based Fossil-Fuel Power Plant Monitoring in High Resolution Remote Sensing Images: A Comparative Study

2019 ◽  
Vol 11 (9) ◽  
pp. 1117 ◽  
Author(s):  
Haopeng Zhang ◽  
Qin Deng
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Power Plants ◽  
North China ◽  
Fossil Fuel ◽  
Environment Monitoring ◽  
Remote Sensing Images ◽  
Plant Monitoring ◽  
Working Status ◽  
Fossil Fuel Power Plants

The frequent hazy weather with air pollution in North China has aroused wide attention in the past few years. One of the most important pollution resource is the anthropogenic emission by fossil-fuel power plants. To relieve the pollution and assist urban environment monitoring, it is necessary to continuously monitor the working status of power plants. Satellite or airborne remote sensing provides high quality data for such tasks. In this paper, we design a power plant monitoring framework based on deep learning to automatically detect the power plants and determine their working status in high resolution remote sensing images (RSIs). To this end, we collected a dataset named BUAA-FFPP60 containing RSIs of over 60 fossil-fuel power plants in the Beijing-Tianjin-Hebei region in North China, which covers about 123 km 2 of an urban area. We compared eight state-of-the-art deep learning models and comprehensively analyzed their performance on accuracy, speed, and hardware cost. Experimental results illustrate that our deep learning based framework can effectively detect the fossil-fuel power plants and determine their working status with mean average precision up to 0.8273, showing good potential for urban environment monitoring.

scholarly journals Weathering the impacts of climate change

2020 ◽  
pp. 227-238
Author(s):  
Brian Helmuth
Keyword(s):  
Climate Change ◽  
Environmental Conditions ◽  
Quantitative Methods ◽  
Heat Waves ◽  
Heat Budget ◽  
Spatial Scales ◽  
Physiological Tolerance ◽  
Physiological Processes ◽  
Local Environments ◽  
Sessile Organisms

Ectothermic organisms experience their local environments in ways that humans can have difficulty conceptualizing. Physics-based (ecomechanical) approaches, for example heat budget models, can lend insights into how an organism’s very local environmental conditions (microclimate) can drive niche-level conditions such as body temperature; these in turn drive physiological processes. Quantitative methods also allow insights into the temporal and spatial scales that may ultimately determine responses to larger-scale environmental change. For example, for small, sessile organisms, microhabitats such as crevices in rocks may provide microrefugia that allow survival during heat waves. As a result, larger-scale recovery following heat waves (rescue effects) may ultimately be influenced by much smaller-scale processes. Ecomechanics techniques also facilitate the use of interventions such as shading that can maintain environmental conditions within physiological tolerance levels.

Active Microwave Systems for Monitoring Drought Stress

2005 ◽  
Author(s):  
Anne M. Smith
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Value Added ◽  
Agricultural Drought ◽  
Sensor System ◽  
Spatial And Temporal Scales ◽  
Satellite Systems ◽  
Radio Detection ◽  
Vegetation Water ◽  
Radar Satellite

Remote sensing can provide timely and economical monitoring of large areas. It provides the ability to generate information on a variety of spatial and temporal scales. Generally, remote sensing is divided into passive and active depending on the sensor system. The majority of remote-sensing studies concerned with drought monitoring have involved visible–infrared sensor systems, which are passive and depend on the sun’s illumination. Radar (radio detection and ranging) is an active sensor system that transmits energy in the microwave region of the electromagnetic spectrum and measures the energy reflected back from the landscape target. The energy reflected back is called backscatter. The attraction of radar over visible– infrared remote sensing (chapters 5 and 6) is its independence from the sun, enabling day/night operations, as well as its ability to penetrate cloud and obtain data under most weather conditions. Thus, unlike visible–infrared sensors, radar offers the opportunity to acquire uninterrupted information relevant to drought such as soil moisture and vegetation stress. Drought conditions manifest in multiple and complex ways. Accordingly, a large number of drought indices have been defined to signal abnormally dry conditions and their effects on crop growth, river flow, groundwater, and so on (Tate and Gustard, 2000). In the field of radar remote sensing, much work has been devoted to developing algorithms to retrieve geophysical parameters such as soil moisture, crop biomass, and vegetation water content. In principle, these parameters would be highly relevant for monitoring agricultural drought. However, despite the existence of a number of radar satellite systems, progress in the use of radar in environmental monitoring, particularly in respect to agriculture, has been slower than anticipated. This may be attributed to the complex nature of radar interactions with agricultural targets and the suboptimal configuration of the satellite sensors available in the 1990s (Ulaby, 1998; Bouman et al., 1999). Because most attention is still devoted to the problem of deriving high-quality soil moisture and vegetation products, there have been few investigations on how to combine such radar products with other data and models to obtain value-added agricultural drought products.

scholarly journals Effects of exogenous nitric oxide on cadmium toxicity in black poplar (Populus nigra): physiological approaches

2019 ◽  
Vol 78 (2) ◽  
pp. 116-124 ◽  
Author(s):  
Yakup Cikili ◽  
Semsettin Kulac ◽  
Halil Samet ◽  
Ertugrul Filiz
Keyword(s):  
Nitric Oxide ◽  
Plant Biomass ◽  
Cadmium Toxicity ◽  
Populus Nigra ◽  
No Donor ◽  
Cd Toxicity ◽  
Physiological Processes ◽  
Black Poplar ◽  
Randomized Design ◽  
Metallic Contaminant

Abstract Cadmium (Cd) is a highly toxic metallic contaminant that negatively affects plant metabolism and causes reductions in productivity. Nitric oxide (NO) is a signaling molecule that regulates various physiological processes and is involved in response to biotic/abiotic stresses. This work investigated the effects of exogenous sodium nitroprusside (SNP), a nitric oxide (NO) donor, application on Cd toxicity in black poplar (Populus nigra). Black poplars were exposed to individual/combined CdCl2 and SNP treatments for 21 days by complete randomized design with three replications. Cd concentrations increased in leaves, bark, and roots at Cd treatments, whereas Cd + SNP applications had alleviative effects on Cd exposures, except for leaves. Photosynthetic pigments (chlorophyll a, b, a + b and carotenoids) reduced with Cd treatments in leaves, while they increased in Cd + SNP applications. Similarly, plant biomass was reduced with Cd treatments, but Cd + SNP application prevented these reductions. SNP also alleviated malondialdehyde (MDA) and hydrogen peroxide (H2O2) accumulation in leaves under Cd treatments. Catalase (CAT, EC 1.11.1.6) and ascorbate peroxidase (APX, EC 1.11.1.11) activities were also affected by Cd and Cd + SNP applications. Cd exposure also decreased Zn2+, Fe2+ and Mn2+ levels in leaves, bark and roots, while it increased Cu2+ level in leaves and roots. This study concludes that Cd toxicity caused a reduction of plant growth and mineral nutrition parameters. However, SNP indicates great potentials for improving the growth under Cd toxicity in P. nigra.

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