Relevant bibliographies by topics / Multispectral predictors

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Multispectral predictors'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Multispectral predictors"

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Trouvé, Raphael, Ruizhu Jiang, Melissa Fedrigo, et al. "Combining Environmental, Multispectral, and LiDAR Data Improves Forest Type Classification: A Case Study on Mapping Cool Temperate Rainforests and Mixed Forests." Remote Sensing 15, no. 1 (2022): 60. http://dx.doi.org/10.3390/rs15010060.

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Predictive vegetation mapping is an essential tool for managing and conserving high conservation-value forests. Cool temperate rainforests (Rainforest) and cool temperate mixed forests (Mixed Forest, i.e., rainforest spp. overtopped by large remnant Eucalyptus trees) are threatened forest types in the Central Highlands of Victoria. Logging of these forest types is prohibited; however, the surrounding native Eucalyptus forests can be logged in some areas of the landscape. This requires accurate mapping and delineation of these vegetation types. In this study, we combine niche modelling, multisp
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Mury, Antoine, Antoine Collin, Thomas Houet, Emilien Alvarez-Vanhard, and Dorothée James. "Using Multispectral Drone Imagery for Spatially Explicit Modeling of Wave Attenuation through a Salt Marsh Meadow." Drones 4, no. 2 (2020): 25. http://dx.doi.org/10.3390/drones4020025.

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Offering remarkable biodiversity, coastal salt marshes also provide a wide variety of ecosystem services: cultural services (leisure, tourist amenities), supply services (crop production, pastoralism) and regulation services including carbon sequestration and natural protection against coastal erosion and inundation. The consideration of this coastal protection ecosystem service takes part in a renewed vision of coastal risk management and especially marine flooding, with an emerging focus on “nature-based solutions.” Through this work, using remote-sensing methods, we propose a novel drone-ba
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Lendzioch, Theodora, Jakub Langhammer, Lukáš Vlček, and Robert Minařík. "Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning." Remote Sensing 13, no. 5 (2021): 907. http://dx.doi.org/10.3390/rs13050907.

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One of the best preconditions for the sufficient monitoring of peat bog ecosystems is the collection, processing, and analysis of unique spatial data to understand peat bog dynamics. Over two seasons, we sampled groundwater level (GWL) and soil moisture (SM) ground truth data at two diverse locations at the Rokytka Peat bog within the Sumava Mountains, Czechia. These data served as reference data and were modeled with a suite of potential variables derived from digital surface models (DSMs) and RGB, multispectral, and thermal orthoimages reflecting topomorphometry, vegetation, and surface temp
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Hernandez, Alexander, Kevin Jensen, Steve Larson, et al. "Using Unmanned Aerial Vehicles and Multispectral Sensors to Model Forage Yield for Grasses of Semiarid Landscapes." Grasses 3, no. 2 (2024): 84–109. http://dx.doi.org/10.3390/grasses3020007.

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Forage yield estimates provide relevant information to manage and quantify ecosystem services in grasslands. We fitted and validated prediction models of forage yield for several prominent grasses used in restoration projects in semiarid areas. We used field forage harvests from three different sites in Northern Utah and Southern California, USA, in conjunction with multispectral, high-resolution UAV imagery. Different model structures were tested with simple models using a unique predictor, the forage volumetric 3D space, and more complex models, where RGB, red edge, and near-infrared spectra
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Pittman, R., and B. Hu. "ESTIMATION OF SOIL BULK DENSITY AND CARBON USING MULTI-SOURCE REMOTELY SENSED DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 541–48. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-541-2020.

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Abstract. Bulk density and soil carbon models were fitted for soil samples collected during field campaigns in 2018 and 2019 for the Kapuskasing region of the District of Cochrane in Ontario, Canada. Prediction maps for bulk density and soil carbon were generated for the 0–15 cm depth mineral soil layer. The application of multi-source remotely sensed data as environmental covariates for model predictors was implemented. Environmental covariates were obtained from multispectral satellite imagery, LiDAR (light detection and ranging) retrievals and airborne geomagnetic surveys, as well from a di
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Chen, Lin, Yeqiao Wang, Chunying Ren, Bai Zhang, and Zongming Wang. "Optimal Combination of Predictors and Algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data." Remote Sensing 11, no. 4 (2019): 414. http://dx.doi.org/10.3390/rs11040414.

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Accurate forest above-ground biomass (AGB) mapping is crucial for sustaining forest management and carbon cycle tracking. The Shuttle Radar Topographic Mission (SRTM) and Sentinel satellite series offer opportunities for forest AGB monitoring. In this study, predictors filtered from 121 variables from Sentinel-1 synthetic aperture radar (SAR), Sentinal-2 multispectral instrument (MSI) and SRTM digital elevation model (DEM) data were composed into four groups and evaluated for their effectiveness in prediction of AGB. Five evaluated algorithms include linear regression such as stepwise regressi
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Heim, René, Ian Wright, Peter Scarth, Angus Carnegie, Dominique Taylor, and Jens Oldeland. "Multispectral, Aerial Disease Detection for Myrtle Rust (Austropuccinia psidii) on a Lemon Myrtle Plantation." Drones 3, no. 1 (2019): 25. http://dx.doi.org/10.3390/drones3010025.

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Disease management in agriculture often assumes that pathogens are spread homogeneously across crops. In practice, pathogens can manifest in patches. Currently, disease detection is predominantly carried out by human assessors, which can be slow and expensive. A remote sensing approach holds promise. Current satellite sensors are not suitable to spatially resolve individual plants or lack temporal resolution to monitor pathogenesis. Here, we used multispectral imaging and unmanned aerial systems (UAS) to explore whether myrtle rust (Austropuccinia psidii) could be detected on a lemon myrtle (B
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Garms, Cory G., Lluvia Flores-Rentería, Kristen Waring, Amy Whipple, Michael G. Wing, and Bogdan M. Strimbu. "Augmenting size models for Pinus strobiformis seedlings using dimensional estimates from unmanned aircraft systems." Canadian Journal of Forest Research 50, no. 9 (2020): 890–904. http://dx.doi.org/10.1139/cjfr-2019-0325.

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In forestry, common garden experiments traditionally require manual measurements and visual inspections. Unmanned aircraft systems (UAS) are a newer method of monitoring plants that is potentially more efficient than traditional techniques. This study had two objectives: to assess the size and mortality of Pinus strobiformis Engelm. seedlings using UAS and to predict the second-year seedling size using manual measurements from the first year and from UAS size estimates. Raised boxes containing 150 seedlings were surveyed twice, one year apart, using multispectral UAS. Seedling heights and diam
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Alevizos, Evangelos. "A Combined Machine Learning and Residual Analysis Approach for Improved Retrieval of Shallow Bathymetry from Hyperspectral Imagery and Sparse Ground Truth Data." Remote Sensing 12, no. 21 (2020): 3489. http://dx.doi.org/10.3390/rs12213489.

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Mapping shallow bathymetry by means of optical remote sensing has been a challenging task of growing interest in recent years. Particularly, many studies exploit earlier empirical models together with the latest multispectral satellite imagery (e.g., Sentinel 2, Landsat 8). However, in these studies, the accuracy of resulting bathymetry is (a) limited for deeper waters (>15 m) and/or (b) is being influenced by seafloor type albedo. This study explores further the capabilities of hyperspectral satellite imagery (Hyperion), which provides several spectral bands in the visible spectrum, along
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Sakowska, K., L. Vescovo, B. Marcolla, R. Juszczak, J. Olejnik, and D. Gianelle. "Monitoring of carbon dioxide fluxes in a subalpine grassland ecosystem of the Italian Alps using a multispectral sensor." Biogeosciences Discussions 11, no. 3 (2014): 4729–69. http://dx.doi.org/10.5194/bgd-11-4729-2014.

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Abstract. The study investigates the potential of a multispectral sensor for monitoring mean midday gross ecosystem production (GEPm) in a dynamic subalpine grassland ecosystem of the Italian Alps equipped with an eddy covariance flux tower. Reflectance observations were collected for five consecutive years by means of a multispectral radiometer system. Spectral vegetation indices were calculated from reflectance measurements at particular wavelengths. Different models based on linear regression and on multiple regression were developed to estimate GEPm. Chlorophyll-related indices including r
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Dissertations / Theses on the topic "Multispectral predictors"

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(9833933), Tej Shahi. "Utilising Drone Imagery to Assess the Peanut Quantity and Quality with Machine Learning Approaches." Thesis, 2023. https://figshare.com/articles/thesis/Utilising_Drone_Imagery_to_Assess_the_Peanut_Quantity_and_Quality_with_Machine_Learning_Approaches/28976015.

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Precision agriculture relies on the efficient measurement of temporal and spatial variability in crop information for a smarter farming management strategy. Variability in crops in terms of their growth rates, size, and yield potentials can exist even within a single field, whereas traditional in-field sampling techniques are unable to represent such crop variability, thereby making precise decisions on crop management more challenging. Hence, alternative non-destructive ways of gathering crop information have been explored, including field-based sensors, satellite imagery and unmanned aerial
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Book chapters on the topic "Multispectral predictors"

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Boonprasert, Rattana, and Piyarat Vijuksungsith. "Agricultural UAVs in Recent Advances, Innovations and Applications." In Advances in Unmanned Aerial Vehicles - New Trends and Applications [Working Title]. IntechOpen, 2025. https://doi.org/10.5772/intechopen.1010104.

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This chapter explores the future trends and advancements of Agricultural Unmanned Aerial Vehicles (Ag-UAVs), highlighting their revolutionary impact on modern agriculture. The discussion is organized into five key sections. Firstly, it examines fundamental principles and emerging trends, focusing on innovations in remote sensing mechanisms, including the use of electromagnetic spectrum analysis and vegetation indices (VIs). Secondly, it delves into the principles and applications of advanced technologies, such as high-resolution, multispectral imaging, Light Detection and Ranging (LiDAR), and
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Conference papers on the topic "Multispectral predictors"

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Bayya, Shyam S., Daniel Gibson, Vinh Q. Nguyen, et al. "New moldable glasses for multispectral optics (Conference Presentation)." In Optical Modeling and Performance Predictions X, edited by Marie B. Levine-West and Mark A. Kahan. SPIE, 2018. http://dx.doi.org/10.1117/12.2321177.

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Hahn, Federico, and Sergio Sanchez. "Neural network predicts carrot volume with only three images." In International Symposium on Multispectral Image Processing, edited by Ji Zhou, Anil K. Jain, Tianxu Zhang, Yaoting Zhu, Mingyue Ding, and Jianguo Liu. SPIE, 1998. http://dx.doi.org/10.1117/12.323576.

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Kainerstorfer, Jana M., Mark N. Polizzotto, Thomas S. Uldrick, et al. "Multispectral imaging as a potential predictor of treatment efficacy for Kaposi's sarcoma skin lesions." In Biomedical Optics. OSA, 2012. http://dx.doi.org/10.1364/biomed.2012.bsu3a.58.

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DeMets, Sydney, Amanda K. Ziemann, Carrie Manore, and Curtis Russell. "Too big, too small, or just right? The influence of multispectral image size on mosquito population predictions in the greater Toronto area." In Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVI, edited by David W. Messinger and Miguel Velez-Reyes. SPIE, 2020. http://dx.doi.org/10.1117/12.2558128.

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Kalshoven, James E. "Plans for an Airborne Multispectral Laser Imaging Polarimeter System." In Laser and Optical Remote Sensing: Instrumentation and Techniques. Optica Publishing Group, 1987. http://dx.doi.org/10.1364/lors.1987.wc15.

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There has been difficulty in making effective use of the natural depolarization properties of the Earth’s surface as a parameter in land remote sensing. This arises from the problem in building a theoretical foundation upon which to interpret the data from reflected polarized solar radiation which is complicated by atmospheric effects and a diversity of viewing and phase angles. By designing an experiment that substantially eliminates the atmospheric and phase angle effects, a data base could be developed upon which theoretical models could be verified, refined, and predictions made. This is t
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Wen, Chang-Da. "Experimental Investigation of Emissivity of Aluminum Alloys and Application of Multispectral Radiation Thermometry." In 2010 14th International Heat Transfer Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/ihtc14-22538.

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Experiments were first conducted to measure the spectral normal emissivity values of a variety of aluminum alloys at 600, 700, and 800 K. Multispectral radiation thermometry (MRT) using linear emissivity models (LEM) and log-linear emissivity models (LLE) were then applied to predict surface temperature. Results show that the spectral emissivity decreases with increasing wavelength and increases with increasing temperature. Alloy effect becomes evident at higher temperature. The surface oxidation becomes fully-developed after the first hour heating and results in constant emissivity. Half of t
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Woodham, Robert J. "Determining scene properties from image properties." In OSA Annual Meeting. Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.tub2.

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The central task in image understanding is to determine scene properties from image properties. This is difficult because the problem, formally posed, is underconstrained. Methods that infer scene properties from image properties make assumptions about how the world determines what we see. In remote sensing, some of these assumptions can be dealt with explicitly. Available scene knowledge, in the form of a digital terrain model and a ground cover map, is used to synthesize an image for a given date and time. The scene radiance equation used assumes that the multispectral bidirectional reflecta
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