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Journal articles on the topic 'Multispectral predictors'

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

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1

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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Casanova, Joaquin, Garett Heineck, and David Huggins. "A Comparison of Yield Prediction Approaches Using Long-Term Multi-Crop Site-Specific Data." Journal of the ASABE 67, no. 3 (2024): 601–15. http://dx.doi.org/10.13031/ja.15216.

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Highlights Long-term site-specific multiple-crop yield data is used in two modeling approaches. Different sources of data, including soil properties, topography, weather, and multispectral data, are tested. Linear modeling and Bayesian hierarchical modeling (BHM) are evaluated by examining predictions of relative yield. BHM with spatio-temporal effects provides the best estimates, handles missing data, and provides uncertainty estimates in time and space. Abstract. Growers in the inland Pacific Northwest face numerous challenges in managing cropping systems. Climate variability, soil degradati
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Hernandez, Alexander, Shaun Bushman, Paul Johnson, Matthew D. Robbins, and Kaden Patten. "Prediction of Turfgrass Quality Using Multispectral UAV Imagery and Ordinal Forests: Validation Using a Fuzzy Approach." Agronomy 14, no. 11 (2024): 2575. http://dx.doi.org/10.3390/agronomy14112575.

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Protocols to evaluate turfgrass quality rely on visual ratings that, depending on the rater’s expertise, can be subjective and susceptible to positive and negative drifts. We developed seasonal (spring, summer and fall) as well as inter-seasonal machine learning predictive models of turfgrass quality using multispectral and thermal imagery collected using unmanned aerial vehicles for two years as a proof-of-concept. We chose ordinal regression to develop the models instead of conventional classification to account for the ranked nature of the turfgrass quality assessments. We implemented a fuz
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Kolbe, Christine, Boris Thies, Sebastian Egli, Lukas Lehnert, Hans Schulz, and Jörg Bendix. "Precipitation Retrieval over the Tibetan Plateau from the Geostationary Orbit—Part 1: Precipitation Area Delineation with Elektro-L2 and Insat-3D." Remote Sensing 11, no. 19 (2019): 2302. http://dx.doi.org/10.3390/rs11192302.

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The lack of long term and well distributed precipitation observations on the Tibetan Plateau (TiP) with its complex terrain raises the need for other sources of precipitation data for this area. Satellite-based precipitation retrievals can fill those data gaps. Before precipitation rates can be retrieved from satellite imagery, the precipitating area needs to be classified properly. Here, we present a feasibility study of a precipitation area delineation scheme for the TiP based on multispectral data with data fusion from the geostationary orbit (GEO, Insat-3D and Elektro-L2) and a machine lea
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Barbon, Sylvio, Ana Paula Ayub da Costa Barbon, Rafael Gomes Mantovani, and Douglas Fernandes Barbin. "Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification." Journal of Spectroscopy 2018 (August 7, 2018): 1–12. http://dx.doi.org/10.1155/2018/8949741.

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Identification of chicken quality parameters is often inconsistent, time-consuming, and laborious. Near-infrared (NIR) spectroscopy has been used as a powerful tool for food quality assessment. However, the near-infrared (NIR) spectra comprise a large number of redundant information. Determining wavelengths relevance and selecting subsets for classification and prediction models are mandatory for the development of multispectral systems. A combination of both attribute and wavelength selection for NIR spectral information of chicken meat samples was investigated. Decision Trees and Decision Ta
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Schucknecht, Anne, Bumsuk Seo, Alexander Krämer, Sarah Asam, Clement Atzberger, and Ralf Kiese. "Estimating dry biomass and plant nitrogen concentration in pre-Alpine grasslands with low-cost UAS-borne multispectral data – a comparison of sensors, algorithms, and predictor sets." Biogeosciences 19, no. 10 (2022): 2699–727. http://dx.doi.org/10.5194/bg-19-2699-2022.

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Abstract. Grasslands are an important part of pre-Alpine and Alpine landscapes. Despite the economic value and the significant role of grasslands in carbon and nitrogen (N) cycling, spatially explicit information on grassland biomass and quality is rarely available. Remotely sensed data from unmanned aircraft systems (UASs) and satellites might be an option to overcome this gap. Our study aims to investigate the potential of low-cost UAS-based multispectral sensors for estimating above-ground biomass (dry matter, DM) and plant N concentration. In our analysis, we compared two different sensors
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Sagang, Le Bienfaiteur T., Pierre Ploton, Bonaventure Sonké, Hervé Poilvé, Pierre Couteron, and Nicolas Barbier. "Airborne Lidar Sampling Pivotal for Accurate Regional AGB Predictions from Multispectral Images in Forest-Savanna Landscapes." Remote Sensing 12, no. 10 (2020): 1637. http://dx.doi.org/10.3390/rs12101637.

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Precise accounting of carbon stocks and fluxes in tropical vegetation using remote sensing approaches remains a challenging exercise, as both signal saturation and ground sampling limitations contribute to inaccurate extrapolations. Airborne LiDAR Scanning (ALS) data can be used as an intermediate level to radically increase sampling and enhance model calibration. Here we tested the potential of using ALS data for upscaling vegetation aboveground biomass (AGB) from field plots to a forest-savanna transitional landscape in the Guineo–Congolian region in Cameroon, using either a design-based app
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Baloloy, Alvin Balidoy, Ariel Conferido Blanco, Christian Gumbao Candido, et al. "ESTIMATION OF MANGROVE FOREST ABOVEGROUND BIOMASS USING MULTISPECTRAL BANDS, VEGETATION INDICES AND BIOPHYSICAL VARIABLES DERIVED FROM OPTICAL SATELLITE IMAGERIES: RAPIDEYE, PLANETSCOPE AND SENTINEL-2." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-3 (April 23, 2018): 29–36. http://dx.doi.org/10.5194/isprs-annals-iv-3-29-2018.

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Aboveground biomass estimation (AGB) is essential in determining the environmental and economic values of mangrove forests. Biomass prediction models can be developed through integration of remote sensing, field data and statistical models. This study aims to assess and compare the biomass predictor potential of multispectral bands, vegetation indices and biophysical variables that can be derived from three optical satellite systems: the Sentinel-2 with 10 m, 20 m and 60 m resolution; RapidEye with 5m resolution and PlanetScope with 3m ground resolu
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Vargas, Charot M., Muditha K. Heenkenda, and Kerin F. Romero. "Estimating the Aboveground Fresh Weight of Sugarcane Using Multispectral Images and Light Detection and Ranging (LiDAR)." Land 13, no. 5 (2024): 611. http://dx.doi.org/10.3390/land13050611.

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This study aimed to develop a remote sensing method for estimating the aboveground fresh weight (AGFW) of sugarcane using multispectral images and light detection and ranging (LiDAR). Remotely sensed data were acquired from an unmanned aerial vehicle (drone). Sample plots were harvested and the AGFW of each plot was measured. Sugarcane crown heights and volumes were obtained by isolating individual tree crowns using a LiDAR-derived digital surface model of the area. Multiple linear regression (MLR) and partial least-squares regression (PLSR) models were tested for the field-sampled AGFWs (depe
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Sarkar, Sayantan, Javier M. Osorio Leyton, Efrain Noa-Yarasca, Kabindra Adhikari, Chad B. Hajda, and Douglas R. Smith. "Integrating Remote Sensing and Soil Features for Enhanced Machine Learning-Based Corn Yield Prediction in the Southern US." Sensors 25, no. 2 (2025): 543. https://doi.org/10.3390/s25020543.

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Efficient and reliable corn (Zea mays L.) yield prediction is important for varietal selection by plant breeders and management decision-making by growers. Unlike prior studies that focus mainly on county-level or controlled laboratory-scale areas, this study targets a production-scale area, better representing real-world agricultural conditions and offering more practical relevance for farmers. Therefore, the objective of our study was to determine the best combination of vegetation indices and abiotic factors for predicting corn yield in a rain-fed, production-scale area, identify the most s
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Leroux, Denis, Manuel Petit, Corinne Davenas, and and Corine Fulchiron. "Multispectral imaging via feature selection: A frugal innovation approach for pathogen identification." EPJ Web of Conferences 287 (2023): 03016. http://dx.doi.org/10.1051/epjconf/202328703016.

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In order to develop an affordable clinical diagnostic instrument for use in more decentralized settings, we have assessed the feasibility to move from hyperspectral to multispectral imaging via parsimonious feature selection. The targeted application is the label-free identification at the species-level of uropathogens from images of bacterial colonies on their growth support. We show that the number of predictors (i.e., discrete spectral channels), can be dramatically reduced from 240 to less than 10 channels with limited performance loss. The impact of bandwidth is also investigated to consi
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Barker, Matthew I., Jonathan D. Burnett, Ivan Arismendi, and Michael G. Wing. "Assessing Heterogeneity of Surface Water Temperature Following Stream Restoration and a High-Intensity Fire from Thermal Imagery." Remote Sensing 17, no. 7 (2025): 1254. https://doi.org/10.3390/rs17071254.

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Thermal heterogeneity of rivers is essential to support freshwater biodiversity. Salmon behaviorally thermoregulate by moving from patches of warm water to cold water. When implementing river restoration projects, it is essential to monitor changes in temperature and thermal heterogeneity through time to assess the impacts to a river’s thermal regime. Lightweight sensors that record both thermal infrared (TIR) and multispectral data carried via unoccupied aircraft systems (UASs) present an opportunity to monitor temperature variations at high spatial (<0.5 m) and temporal resolution, facili
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Bolaños, Julian, Juan Carlos Corrales, and Liseth Viviana Campo. "Feasibility of Early Yield Prediction per Coffee Tree Based on Multispectral Aerial Imagery: Case of Arabica Coffee Crops in Cauca-Colombia." Remote Sensing 15, no. 1 (2023): 282. http://dx.doi.org/10.3390/rs15010282.

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Crop yield is an important factor for evaluating production processes and determining the profitability of growing coffee. Frequently, the total number of coffee beans per area unit is estimated manually by physically counting the coffee cherries, the branches, or the flowers. However, estimating yield requires an investment in time and work, so it is not usual for small producers. This paper studies a non-intrusive and attainable alternative to predicting coffee crop yield through multispectral aerial images. The proposal is designed for small low-tech producers monitored by capturing aerial
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Antonopoulos, Constantine N., Andreas Lazaris, Maarit Venermo, and George Geroulakos. "Predictors of Wound Healing Following Revascularization for Chronic Limb-Threatening Ischemia." Vascular and Endovascular Surgery 53, no. 8 (2019): 649–57. http://dx.doi.org/10.1177/1538574419868863.

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Objectives: After surgical or endovascular revascularization, some ischemic lesions will not heal, while some others will heal at a variable period of time from the intervention, indicating a multifactorial interaction between local and systematic “wound healing–promoting” factors. Our objective was to identify predictors of wound healing following revascularization for chronic limb-threatening ischemia (CLTI). Methods: A literature review was performed to identify published research concerning clinical, biochemical, and noninvasive methods as predictors of wound healing time and wound-free pe
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Shen, Yulin, Benoît Mercatoris, Zhen Cao, et al. "Improving Wheat Yield Prediction Accuracy Using LSTM-RF Framework Based on UAV Thermal Infrared and Multispectral Imagery." Agriculture 12, no. 6 (2022): 892. http://dx.doi.org/10.3390/agriculture12060892.

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Yield prediction is of great significance in agricultural production. Remote sensing technology based on unmanned aerial vehicles (UAVs) offers the capacity of non-intrusive crop yield prediction with low cost and high throughput. In this study, a winter wheat field experiment with three levels of irrigation (T1 = 240 mm, T2 = 190 mm, T3 = 145 mm) was conducted in Henan province. Multispectral vegetation indices (VIs) and canopy water stress indices (CWSI) were obtained using an UAV equipped with multispectral and thermal infrared cameras. A framework combining a long short-term memory neural
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Garofalo, Simone Pietro, Francesca Ardito, Nicola Sanitate, et al. "Robustness of Actual Evapotranspiration Predicted by Random Forest Model Integrating Remote Sensing and Meteorological Information: Case of Watermelon (Citrullus lanatus, (Thunb.) Matsum. & Nakai, 1916)." Water 17, no. 3 (2025): 323. https://doi.org/10.3390/w17030323.

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Water scarcity, exacerbated by climate change and increasing agricultural water demands, highlights the necessity for efficient irrigation management. This study focused on estimating actual evapotranspiration (ETa) in watermelons under semi-arid Mediterranean conditions by integrating high-resolution satellite imagery and agro-meteorological data. Field experiments were conducted in Rutigliano, southern Italy, over a 2.80 ha area. ETa was measured with the eddy covariance (EC) technique and predicted using machine learning models. Multispectral reflectance data from Planet SuperDove satellite
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Giannico, Vincenzo, Simone Pietro Garofalo, Luca Brillante, et al. "Temporal Vine Water Status Modeling Through Machine Learning Ensemble Technique and Sentinel-2 Multispectral Images Under Semi-Arid Conditions." Remote Sensing 16, no. 24 (2024): 4784. https://doi.org/10.3390/rs16244784.

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New challenges will be experienced by the agriculture sector in the near future, especially due to the effects of climate change. For example, rising temperatures could result in increased evapotranspiration demand, causing difficulties in the management of irrigation practices. Generally, an important predictor of plant water status to be taken into account for irrigation monitoring and management is the stem water potential. However, it requires a huge amount of time-consuming fieldwork, particularly when an adequate data amount is necessary to fully investigate the spatial and temporal vari
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Garamszegi, Balázs, Christopher Jung, and Dirk Schindler. "Multispectral Spaceborne Proxies of Predisposing Forest Structure Attributes to Storm Disturbance—A Case Study from Germany." Forests 13, no. 12 (2022): 2114. http://dx.doi.org/10.3390/f13122114.

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Windstorms are among the primary drivers of forest disturbances. Although they are inherent part of the natural ecosystem processes, they severely impact managed forests. Modeling approaches serve as key tools for the evaluation of disturbance risk and different predisposing factors. However, data availability on relevant forest attributes can be problematic on a larger scale. While spaceborne remote sensing has already proven itself as a tool for disturbance detection, its use in relation to predisposing forest attributes remains underexploited. The present work explores multispectral object-
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Pittman, R., and B. Hu. "IMPROVING THE BINARY CLASSIFICATION OF PEAT LOCALITIES FROM MULTI-SOURCE REMOTELY-SENSED DATA USING CNN." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 30, 2022): 983–88. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-983-2022.

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Abstract. Neural networks were explored to achieve a binary classification for determining land corresponding to peat for a study area in the boreal forest of northern Ontario, Canada. Environmental covariates were employed as predictors and obtained from multiple sources, which included multispectral imagery, LiDAR, SAR, and aeromagnetic data. A dense neural network (DNN), as well as a convolutional neural network (CNN), were each implemented. Logistic regression, support vector machine (SVM) and random forest (RF) approaches were also modelled. Neighboring pixels surrounding the soil samplin
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Condro, Aryo Adhi, Yudi Setiawan, Lilik Budi Prasetyo, Rahmat Pramulya, and Lasriama Siahaan. "Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform." Land 9, no. 10 (2020): 377. http://dx.doi.org/10.3390/land9100377.

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Indonesia has the most favorable climates for agriculture because of its location in the tropical climatic zones. The country has several commodities to support economics growth that are driven by key export commodities—e.g., oil palm, rubber, paddy, cacao, and coffee. Thus, identifying the main commodities in Indonesia using spatially-explicit tools is essential to understand the precise productivity derived from the agricultural sectors. Many previous studies have used predictions developed using binary maps of general crop cover. Here, we present national commodity maps for Indonesia based
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Lussem, U., A. Bolten, J. Menne, M. L. Gnyp, and G. Bareth. "ULTRA-HIGH SPATIAL RESOLUTION UAV-BASED IMAGERY TO PREDICT BIOMASS IN TEMPERATE GRASSLANDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 4, 2019): 443–47. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-443-2019.

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<p><strong>Abstract.</strong> Monitoring biomass yield in grassland is of key importance to support sustainable management decisions. Especially the high spatio-temporal variety in grasslands requires rapid and cost-efficient data acquisition with a high spatial and temporal resolution. Therefore, this study aims to evaluate the comparability of UAV-based simultaneously acquired vegetation indices from a consumer-grade RGB-camera (Sony Alpha 6000) and a well-calibrated narrow-band multispectral camera (MicaSense RedEdge-M) to estimate dry matter biomass yield. The study site
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Liu, Mingbo, Chunxiang Cao, Wei Chen, and Xuejun Wang. "Mapping Canopy Heights of Poplar Plantations in Plain Areas Using ZY3-02 Stereo and Multispectral Data." ISPRS International Journal of Geo-Information 8, no. 3 (2019): 106. http://dx.doi.org/10.3390/ijgi8030106.

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Forest canopy height plays an important role in forest management and ecosystem modeling. There are a variety of techniques employed to map forest height using remote sensing data but it is still necessary to explore the use of new data and methods. In this study, we demonstrate an approach for mapping canopy heights of poplar plantations in plain areas through a combination of stereo and multispectral data from China’s latest civilian stereo mapping satellite ZY3-02. First, a digital surface model (DSM) was extracted using photogrammetry methods. Then, canopy samples and ground samples were s
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Heriza, Dewinta, Chih-Da Wu, Muhammad Aldila Syariz, and Chao-Hung Lin. "Analysis of Spatial–Temporal Variability of PM2.5 Concentrations Using Optical Satellite Images and Geographic Information System." Remote Sensing 15, no. 8 (2023): 2009. http://dx.doi.org/10.3390/rs15082009.

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Particulate matter less than 2.5 microns in diameter (PM2.5) is an air pollutant that has become a major environmental concern for governments around the world. Management and control require air quality monitoring and prediction. However, previous studies did not fully utilize the spectral information in multispectral satellite images and land use data in geographic datasets. To alleviate these problems, this study proposes the extraction of land use information not only from geographic inventory but also from satellite images with a machine learning-based classification. In this manner, near
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Chinembiri, Tsikai Solomon, Onisimo Mutanga, and Timothy Dube. "Landsat-8 and Sentinel-2 Based Prediction of Forest Plantation C Stock Using Spatially Varying Coefficient Bayesian Hierarchical Models." Remote Sensing 14, no. 22 (2022): 5676. http://dx.doi.org/10.3390/rs14225676.

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This study sought to establish the performance of Spatially Varying Coefficient (SVC) Bayesian Hierarchical models using Landsat-8, and Sentinel-2 derived auxiliary information in predicting plantation forest carbon (C) stock in the eastern highlands of Zimbabwe. The development and implementation of Zimbabwe’s land reform program undertaken in the year 2000 and the subsequent redistribution and resizing of large-scale land holdings are hypothesized to have created heterogeneity in aboveground forest biomass in plantation ecosystems. The Bayesian hierarchical framework, accommodating residual
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Lopez-Fornieles, Eva, Guilhem Brunel, Florian Rancon, et al. "Potential of Multiway PLS (N-PLS) Regression Method to Analyse Time-Series of Multispectral Images: A Case Study in Agriculture." Remote Sensing 14, no. 1 (2022): 216. http://dx.doi.org/10.3390/rs14010216.

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Recent literature reflects the substantial progress in combining spatial, temporal and spectral capacities for remote sensing applications. As a result, new issues are arising, such as the need for methodologies that can process simultaneously the different dimensions of satellite information. This paper presents PLS regression extended to three-way data in order to integrate multiwavelengths as variables measured at several dates (time-series) and locations with Sentinel-2 at a regional scale. Considering that the multi-collinearity problem is present in remote sensing time-series to estimate
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Owers, Christopher J., Rodney P. Kavanagh, and Eleanor Bruce. "Remote sensing can locate and assess the changing abundance of hollow-bearing trees for wildlife in Australian native forests." Wildlife Research 41, no. 8 (2014): 703. http://dx.doi.org/10.1071/wr14168.

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Context Hollow-bearing trees are an important breeding and shelter resource for wildlife in Australian native forests and hollow availability can influence species abundance and diversity in forest ecosystems. A persistent problem for forest managers is the ability to locate and survey hollow-bearing trees with a high level of accuracy at low cost over large areas of forest. Aims The aim of this study was to determine whether remote-sensing techniques could identify key variables useful in classifying the likelihood of a tree to contain hollows suitable for wildlife. Methods The data were high
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Haumont, Jérémie, Peter Lootens, Simon Cool, et al. "Multispectral UAV-Based Monitoring of Leek Dry-Biomass and Nitrogen Uptake across Multiple Sites and Growing Seasons." Remote Sensing 14, no. 24 (2022): 6211. http://dx.doi.org/10.3390/rs14246211.

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Leek farmers tend to apply too much nitrogen fertilizer as its cost is relatively low compared to the gross value of leek. Recently, several studies have shown that proximal sensing technologies could accurately monitor the crop nitrogen content and biomass. However, their implementation is impeded by practical limitations and the limited area they can cover. UAV-based monitoring might alleviate these issues. Studies on UAV-based vegetable crop monitoring are still limited. Because of the economic importance and environmental impact of leeks in Flanders, this study aimed to investigate the abi
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Parida, Pradosh Kumar, Eagan Somasundaram, Ramanujam Krishnan, et al. "Unmanned Aerial Vehicle-Measured Multispectral Vegetation Indices for Predicting LAI, SPAD Chlorophyll, and Yield of Maize." Agriculture 14, no. 7 (2024): 1110. http://dx.doi.org/10.3390/agriculture14071110.

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Predicting crop yield at preharvest is pivotal for agricultural policy and strategic decision making. Despite global agricultural targets, labour-intensive surveys for yield estimation pose challenges. Using unmanned aerial vehicle (UAV)-based multispectral sensors, this study assessed crop phenology and biotic stress conditions using various spectral vegetation indices. The goal was to enhance the accuracy of predicting key agricultural parameters, such as leaf area index (LAI), soil and plant analyser development (SPAD) chlorophyll, and grain yield of maize. The study’s findings demonstrate
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Dlamini, Mandla, George Chirima, Mbulisi Sibanda, Elhadi Adam, and Timothy Dube. "Characterizing Leaf Nutrients of Wetland Plants and Agricultural Crops with Nonparametric Approach Using Sentinel-2 Imagery Data." Remote Sensing 13, no. 21 (2021): 4249. http://dx.doi.org/10.3390/rs13214249.

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In arid environments of the world, particularly in sub-Saharan Africa and Asia, floodplain wetlands are a valuable agricultural resource. However, the water reticulation role by wetlands and crop production can negatively impact wetland plants. Knowledge on the foliar biochemical elements of wetland plants enhances understanding of the impacts of agricultural practices in wetlands. This study thus used Sentinel-2 multispectral data to predict seasonal variations in the concentrations of nine foliar biochemical elements in plant leaves of key floodplain wetland vegetation types and crops in the
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Lachgar, Abdelkrim, David Mulla, and Viacheslav Adamchuk. "Implementation of Proximal and Remote Soil Sensing, Data Fusion and Machine Learning to Improve Phosphorus Spatial Prediction for Farms in Ontario, Canada." Agronomy 14, no. 4 (2024): 693. http://dx.doi.org/10.3390/agronomy14040693.

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One of the challenges in site-specific phosphorus (P) management is the substantial spatial variability in plant available P across fields. To overcome this barrier, emerging sensing, data fusion, and spatial predictive modeling approaches are needed to accurately reveal the spatial heterogeneity of P. Seven spatially variable fields located in Ontario, Canada are clustered into two zones; four fields are located in eastern Ontario and three others are located in western Ontario. This study compares Bayesian Additive Regression Trees (BART), Support Vector Machine regressor (SVM), and Ordinary
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Beaudoin, André, Ronald J. Hall, Guillermo Castilla, et al. "Improved k-NN Mapping of Forest Attributes in Northern Canada Using Spaceborne L-Band SAR, Multispectral and LiDAR Data." Remote Sensing 14, no. 5 (2022): 1181. http://dx.doi.org/10.3390/rs14051181.

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Satellite forest inventories are the only feasible way to map Canada’s vast, remote forest regions, such as those in the Northwest Territories (NWT). A method used to create such inventories is the k-nearest neighbour (k-NN) algorithm, which spatially extends information from forest inventory (FI) plots to the entire forest land base using wall-to-wall features typically derived from Landsat data. However, the benefits of integrating L-band synthetic aperture radar (SAR) data, strongly correlated to forest biomass, have not been assessed for Canadian northern boreal forests. Here we describe a
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Costanza, Leonardo, Beatriz Lorente, Francisco Pedrero Salcedo, et al. "Predicting Olive Tree Chlorophyll Fluorescence Using Explainable AI with Sentinel-2 Imagery in Mediterranean Environment." Applied Sciences 15, no. 5 (2025): 2746. https://doi.org/10.3390/app15052746.

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Chlorophyll fluorescence is a useful indicator of a plant’s physiological status, particularly under stress conditions. Remote sensing is an increasingly adopted technology in modern agriculture, allowing the acquisition of crop information (e.g., chlorophyll fluorescence) without direct contact, reducing fieldwork. The objective of this study is to improve the monitoring of olive tree fluorescence (Fv′/Fm′) via remote sensing in a Mediterranean environment, where the frequency of stress factors, such as drought, is increasing. An advanced approach combining explainable artificial intelligence
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Qin, Yaozu, Li Cao, Ali Darvishi Boloorani, and Weicheng Wu. "High-Resolution Mining-Induced Geo-Hazard Mapping Using Random Forest: A Case Study of Liaojiaping Orefield, Central China." Remote Sensing 13, no. 18 (2021): 3638. http://dx.doi.org/10.3390/rs13183638.

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Mining-induced geo-hazard mapping (MGM) is a critical step for reducing and avoiding tremendous losses of human life, mine production, and property that are caused by ore mining. Due to the restriction of the survey techniques and data sources, high-resolution MGM remains a big challenge. To overcome this problem, in this research, such an MGM was conducted using detailed geological exploration and topographic survey data as well as Gaofen-1 satellite imagery as multi-source geoscience datasets and machine learning technique taking Liaojiaping Orefield, Central China as an example. First, usin
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Zakharov, K. V., A. A. Medvedkov, and V. F. Borisov. "Using vegetation indices for extrapolating results of heavy metals elements analysis in forest arrays." Geodesy and Cartography 962, no. 8 (2020): 49–57. http://dx.doi.org/10.22389/0016-7126-2020-962-8-49-57.

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Accumulating heavy metals in urban and suburban forest areas is becoming a challenge. Usually analytical methods are employed for assessment of elements concentration in absorbing environments. They are related to some difficulties for characteristic of the whole forest area. The index images were used for solving this task. Multispectral Santinel 2A satellite pictures made the base data for vegetation indices. More than 20 indices were chosen as aid to diagnose the increase of heavy metal concentration, especially photosynthetic apparatus alteration. The element analysis was done by atom-abso
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Wei, Hsiang-En, Miles Grafton, Mike Bretherton, Matthew Irwin, and Eduardo Sandoval. "Evaluation of the Use of UAV-Derived Vegetation Indices and Environmental Variables for Grapevine Water Status Monitoring Based on Machine Learning Algorithms and SHAP Analysis." Remote Sensing 14, no. 23 (2022): 5918. http://dx.doi.org/10.3390/rs14235918.

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Monitoring and management of grapevine water status (GWS) over the critical period between flowering and veraison plays a significant role in producing grapes of premium quality. Although unmanned aerial vehicles (UAVs) can provide efficient mapping across the entire vineyard, most commercial UAV-based multispectral sensors do not contain a shortwave infrared band, which makes the monitoring of GWS problematic. The goal of this study is to explore whether and which of the ancillary variables (vegetation characteristics, temporal trends, weather conditions, and soil/terrain data) may improve th
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Kumbula, Samuel, Paramu Mafongoya, Kabir Peerbhay, Romano Lottering, and Riyad Ismail. "Using Sentinel-2 Multispectral Images to Map the Occurrence of the Cossid Moth (Coryphodema tristis) in Eucalyptus Nitens Plantations of Mpumalanga, South Africa." Remote Sensing 11, no. 3 (2019): 278. http://dx.doi.org/10.3390/rs11030278.

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Coryphodema tristis is a wood-boring insect, indigenous to South Africa, that has recently been identified as an emerging pest feeding on Eucalyptus nitens, resulting in extensive damage and economic loss. Eucalyptus plantations contributes over 9% to the total exported manufactured goods of South Africa which contributes significantly to the gross domestic product. Currently, the distribution extent of the Coryphodema tristis is unknown and estimated to infest Eucalyptus nitens compartments from less than 1% to nearly 80%, which is certainly a concern for the forestry sector related to the qu
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Noa-Yarasca, Efrain, Javier M. Osorio Leyton, Chad B. Hajda, Kabindra Adhikari, and Douglas R. Smith. "Leveraging Spectral Neighborhood Information for Corn Yield Prediction with Spatial-Lagged Machine Learning Modeling: Can Neighborhood Information Outperform Vegetation Indices?" AI 6, no. 3 (2025): 58. https://doi.org/10.3390/ai6030058.

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Accurate and reliable crop yield prediction is essential for optimizing agricultural management, resource allocation, and decision-making, while also supporting farmers and stakeholders in adapting to climate change and increasing global demand. This study introduces an innovative approach to crop yield prediction by incorporating spatially lagged spectral data (SLSD) through the spatial-lagged machine learning (SLML) model, an enhanced version of the spatial lag X (SLX) model. The research aims to show that SLSD improves prediction compared to traditional vegetation index (VI)-based methods.
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Dash, Jonathan P., Michael S. Watt, Thomas S. H. Paul, Justin Morgenroth, and Grant D. Pearse. "Early Detection of Invasive Exotic Trees Using UAV and Manned Aircraft Multispectral and LiDAR Data." Remote Sensing 11, no. 15 (2019): 1812. http://dx.doi.org/10.3390/rs11151812.

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Exotic conifers can provide significant ecosystem services, but in some environments, they have become invasive and threaten indigenous ecosystems. In New Zealand, this phenomenon is of considerable concern as the area occupied by invasive exotic trees is large and increasing rapidly. Remote sensing methods offer a potential means of identifying and monitoring land infested by these trees, enabling managers to efficiently allocate resources for their control. In this study, we sought to develop methods for remote detection of exotic invasive trees, namely Pinus sylvestris and P. ponderosa. Cri
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Arifin, M. D., Y. Koesmaryono, and Impron. "Rice brown planthopper monitoring and detection by spectral reflectance: a review." IOP Conference Series: Earth and Environmental Science 1230, no. 1 (2023): 012088. http://dx.doi.org/10.1088/1755-1315/1230/1/012088.

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Abstract Brown planthopper (BPH) has been one of the main pests of rice worldwide. Monitoring is important factor for determining attacks and estimating their effects. The traditional monitoring approach is usually conducted through visual observation and field scouting, with limitations such as subjectivity and time consumption. Remote sensing is an alternative pest monitoring method that covers a larger area in a shorter time. This paper discusses a remote-sensing method that uses a spectral approach to detect BPH attacks. Literature was filtered and processed using the PRISMA method. Accord
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Morisio, Maurizio, Emanuela Noris, Chiara Pagliarani, et al. "Characterization of Hazelnut Trees in Open Field Through High-Resolution UAV-Based Imagery and Vegetation Indices." Sensors 25, no. 1 (2025): 288. https://doi.org/10.3390/s25010288.

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The increasing demand for hazelnut kernels is favoring an upsurge in hazelnut cultivation worldwide, but ongoing climate change threatens this crop, affecting yield decreases and subject to uncontrolled pathogen and parasite attacks. Technical advances in precision agriculture are expected to support farmers to more efficiently control the physio-pathological status of crops. Here, we report a straightforward approach to monitoring hazelnut trees in an open field, using aerial multispectral pictures taken by drones. A dataset of 4112 images, each having 2Mpixel resolution per tree and covering
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Xi, Jing, Qigang Jiang, Huaxin Liu, and Xin Gao. "Lithological Mapping Research Based on Feature Selection Model of ReliefF-RF." Applied Sciences 13, no. 20 (2023): 11225. http://dx.doi.org/10.3390/app132011225.

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Multispectral satellite data allow experts to discriminate rock units based on their spectral signature characteristics. Here, Sentinel-2, ASTER and the Landsat-8 Operational Land Imager (OLI) were assessed for lithological mapping by using a random forest (RF) classifier for a study area located in Xitieshan, Northwest China. The classification accuracy of Sentinel-2 was 60.71%, which was 5.24% and 4.77% higher than the accuracies for ASTER and the Landsat-8 OLI, respectively. Three image enhancement techniques, namely, principal component analysis (PCA), independent component analysis (ICA)
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