Validierung von NDVI-Messungen mit einer modifizierten Digitalkamera – Fernerkundung von Vegetation als Thema des Physikunterrichts

Die Satellitenfernerkundung ist eine wichtige und überaus vielseitige Disziplin der angewandten Raumfahrtforschung mit großem Einfluss auf unseren Alltag. Während in den letzten Jahren einige Bemühungen zu beobachten waren, das Thema im Geographieunterricht zu etablieren, blieb die Physik der Fernerkundung auf Grund zu teurer und zu komplexer Technik einem direkten Zugang bislang verwehrt. In diesem Beitrag soll daher eine kostengünstige Kameratechnik, welche sich dazu eignet eigene Messungen zur Fernerkundung von Vegetation durchzuführen, vorgestellt und mit Hilfe von Vergleichsmessungen einer Feldkampagne validiert werden. Abschließend wird auch auf konkrete Möglichkeiten zum Einsatz der Technik in Unterrichtsprojekten eingegangen

Validierung von NDVI-Messungen mit einer modifizierten Digitalkamera – Fernerkundung von Vegetation als Thema des Physikunterrichts

Die Satellitenfernerkundung ist eine wichtige und überaus vielseitige Disziplin der angewandten Raumfahrtforschung mit großem Einfluss auf unseren Alltag. Während in den letzten Jahren einige Bemühungen zu beobachten waren, das Thema im Geographieunterricht zu etablieren, blieb die Physik der Fernerkundung auf Grund zu teurer und zu komplexer Technik einem direkten Zugang bislang verwehrt. In diesem Beitrag soll daher eine kostengünstige Kameratechnik, welche sich dazu eignet eigene Messungen zur Fernerkundung von Vegetation durchzuführen, vorgestellt und mit Hilfe von Vergleichsmessungen einer Feldkampagne validiert werden. Abschließend wird auch auf konkrete Möglichkeiten zum Einsatz der Technik in Unterrichtsprojekten eingegangen

A Neural Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery

(1) Background: Information rich hyperspectral sensing, together with robust image analysis, is providing new research pathways in plant phenotyping. This combination facilitates the acquisition of spectral signatures of individual plant organs as well as providing detailed information about the physiological status of plants. Despite the advances in hyperspectral technology in field-based plant phenotyping, little is known about the characteristic spectral signatures of shaded and sunlit components in wheat canopies. Non-imaging hyperspectral sensors cannot provide spatial information; thus, they are not able to distinguish the spectral reflectance differences between canopy components. On the other hand, the rapid development of high-resolution imaging spectroscopy sensors opens new opportunities to investigate the reflectance spectra of individual plant organs which lead to the understanding of canopy biophysical and chemical characteristics. (2) Method: This study reports the development of a computer vision pipeline to analyze ground-acquired imaging spectrometry with high spatial and spectral resolutions for plant phenotyping. The work focuses on the critical steps in the image analysis pipeline from pre-processing to the classification of hyperspectral images. In this paper, two convolutional neural networks (CNN) are employed to automatically map wheat canopy components in shaded and sunlit regions and to determine their specific spectral signatures. The first method uses pixel vectors of the full spectral features as inputs to the CNN model and the second method integrates the dimension reduction technique known as linear discriminate analysis (LDA) along with the CNN to increase the feature discrimination and improves computational efficiency. (3) Results: The proposed technique alleviates the limitations and lack of separability inherent in existing pre-defined hyperspectral classification methods. It optimizes the use of hyperspectral imaging and ensures that the data provide information about the spectral characteristics of the targeted plant organs, rather than the background. We demonstrated that high-resolution hyperspectral imagery along with the proposed CNN model can be powerful tools for characterizing sunlit and shaded components of wheat canopies in the field. The presented method will provide significant advances in the determination and relevance of spectral properties of shaded and sunlit canopy components under natural light conditions

Unmanned Aerial Systems for Estimating Canopy Structure and Assessing Vegetation Health in Willow Crops

Off-the-shelf, low cost small Unmanned Aerial Systems (sUASs) have been increasingly applied for conservation and management of natural resources. Using sUASs to monitor vegetation health and estimate canopy structure in willow crops could provide an avenue for rapid, quality data collection, similar to applications in precision agriculture. Multispectral imagery was collected at two altitudes over three willow sites in Upstate New York in October 2017. Individual bands were calibrated to account for minor variations in weather conditions during acquisition, stitched into orthomosaics and then used to calculate Normalized Difference Vegetation Index (NDVI). Field measurements including canopy depth, Leaf Area Index (LAI), and location were collected in summer 2017. Significant positive, linear relationships between mean NDVI and field LAI measurements were obtained at East Ava and Solvay with r=0.610 (p\u3e.001) and r=0.432 (p=.004), respectively. An analysis determining differences in computed NDVI values using calibrated and uncalibrated imagery was also performed

ESTIMACIÓN DE LA INFLUENCIA DEL FACTOR SUELO EN EL CULTIVO DE LA PIÑA (Ananas comosus (L.) Merr) UTILIZANDO LOS ÍNDICES NDVI Y SAVI

Esta investigación se fundamenta en los principios en la agricultura de precisión, la cual posibilita el monitoreo de las plantaciones agrícolas mediante la observación e interpretación de la interacción de los fenómenos en la superficie terrestre a través de imágenes satelitales. En el ámbito del monitoreo agrícola, una de las técnicas aplicadas consiste en el empleo de índices de vegetación, siendo el NDVI el más reconocido. Sin embargo, el NDVI se ve afectado por la reflectancia del suelo, generando problemáticas en cultivos poco densos o en etapas iniciales, lo que resulta en inconsistencias en los valores y, por ende, interpretaciones deficientes de la actividad fotosintética de la plantación. Las imágenes satelitales utilizadas en este estudio fueron obtenidas del satélite Sentinel 2A, durante el periodo comprendido entre noviembre de 2022 a enero del 2023, tomando como criterio las etapas fenológicas F1 y F2 del cultivo de la piña. Partiendo de esta situación se realizó la estimación de la influencia de la reflectancia del suelo en el cultivo de piña para el monitoreo agrícola utilizando los índices NDVI y SAVI. Los resultados obtenidos en las correlaciones de las variables biométricas (altura, largo, ancho, ø planta) con los IV, presentando mejores valores con SAVI, 0.81, 0.67, 0.59, 0.61 respectivamente en comparación con los obtenidos mediante el NDVI. Asimismo, se evidenció la influencia de la reflectancia del suelo en la correlación que se logran entre el IAFSAVI (0.648) presentando un mejor valor que la correlación de IAF-NDVI (0.624)

Global quasi-daily fractional vegetation cover estimated from the DSCOVR EPIC directional hotspot dataset

Published versio

Multiresolution digital soil mapping of permafrost soils using a random forest classifier: an investigation along the Dalton Highway corridor, Alaska

Thesis (M.S.) University of Alaska Fairbanks, 2018In order to complete soil inventories in the remote permafrost zones of Alaska, there is a need to develop efficient digital soil mapping tools that can be applied over large areas using a minimum of ground truth data. This investigation first used a random forest classifier to test combinations of environmental input data at multiple resolutions (10m, 30m, and 100m). Five tiers of soil taxonomic units were predicted: Order, Suborder, Great Group, "Series Concept", and Particle Size Class. Model outputs are compared quantitatively via estimated out-of-bag accuracy, and qualitatively via visual inspection by soil scientists. Estimated out-of-bag accuracy ranged from ~45% to ~75%, with results improving when fewer classes were modeled. Model runs at 10m and 30m resolution performed comparably, with 100m resolution performing ~5-10% worse in most cases. Increasing the number of trees used, including categorical environmental input data (e.g. landforms), and replacement of environmental covariates with principal component analysis (PCA) bands did not significantly improve model performance. The random forest classifier was then used in a digital soil mapping pilot study along the Dalton Highway in northern Alaska. Parameters suggested in the initial study were used to predict multiple soil taxonomic classes from a basic collection of environmental covariates generated using high resolution (10m) satellite images and sparsely sampled pedon data. Covariates included maximum curvature, multiresolution valley bottom flatness, normalized height, potential incoming solar radiation, slope, terrain ruggedness index, and modified soil and vegetation index. Five tiers of soil taxonomic units were predicted: Order, Suborder, Great Group, "Series Concept", and Particle Size Class. Model outputs are compared quantitatively via estimated out-of-bag accuracy. Estimated out-of-bag accuracy ranged from ~45% to ~75%, with results improving when fewer classes were modeled. We suggest future research into optimized sampling to ensure an adequate distribution of samples across the feature space, and the incorporation of expert knowledge into accuracy assessments. Overall, digital soil mapping with random forest classifiers appears to be a promising method for completing the soil survey of Alaska.USDA Natural Resources Conservation Service, UADA NIF

Remote and Ground-based Methods for Monitoring Vegetation Health Parameters in a Bioretention Facility

The growing contamination of surface water by stormwater runoff parallels increasing urban development. Heavy metals, polycyclic aromatic hydrocarbons (PAHs), persistent organic pollutants (POPs), and contaminants of emerging concern (CECs) are discharged from point sources and washed from impervious surfaces into surface waters, impacting the ecology of these systems, food supplies, and the source waters for drinking water. Green stormwater infrastructure reduces peak runoff and removes contaminants, while providing the structure to support microbial communities and stabilize the soil. Vegetation health plays a large role in the effectiveness of green infrastructure installations, with unhealthy vegetation reducing uptake and transpiration rates, as well as filtration effectiveness. Monitoring the health of vegetation in stormwater green infrastructure can indicate signs of water stress, disease, as well as pollutant induced toxicity. Early detection of stress in vegetation can inform management and maintenance decisions. This study examines one remote and one ground-based method for monitoring biomass and primary production in two cells of a bioretention facility, one cell (Cell 2) containing sedges, rushes and grasses and the other (Cell 3) containing a mix of sedges, rushes, grasses and broadleaf vegetation. Ground-based measurements of the fraction of intercepted photosynthetically active radiation (fIPAR) produced unreliable results with challenges leading to over and under-estimates of intercepted PAR. fIPAR and the normalized difference vegetation index (NDVI) were poorly correlated for both bioretention cells, with coefficients of determination for Cell 2 (0.13) being lower than Cell 3 (0.49). However, radiometric calibration of UAS data from inexpensive sensors using the empirical line method (ELM) and three inexpensive ethylene-vinyl acetate (EVA) foam panels produced reasonable results. Linear regression equations were derived for the red and NIR bands of imagery with coefficients of determination from the three image sets ranging from 0.636 to 0.999. To increase the accuracy of the method, additional calibration targets should be used. The resulting NDVI data was tracked over a two-month period during the transition from spring to summer. The NDVI data was useful in exploring the spatial distribution of NDVI and how NDVI value coverage areas change over time

CHARACTERIZING FOREST STANDS USING UNMANNED AERIAL SYSTEMS (UAS) DIGITAL PHOTOGRAMMETRY: ADVANCEMENTS AND CHALLENGES IN MONITORING LOCAL SCALE FOREST COMPOSITION, STRUCTURE, AND HEALTH

Present-day forests provide a wide variety of ecosystem services to the communities that rely on them. At the same time, these environments face routine and substantial disturbances that direct the need for site-specific, timely, and accurate monitoring/management (i.e., precision forestry). Unmanned Aerial Systems (UAS or UAV) and their associated technologies offer a promising tool for conducting such precision forestry. Now, even with only natural color, uncalibrated, UAS imagery, software workflows involving Structure from Motion (SfM) (i.e., digital photogrammetry) modelling and segmentation can be used to characterize the features of individual trees or forest communities. In this research, we tested the effectiveness of UAS-SfM for mapping local scale forest composition, structure, and health. Our first study showed that digital (automated) methods for classifying forest composition that utilized UAS imagery produced a higher overall accuracy than those involving other high-spatial-resolution imagery (7.44% - 16.04%). The second study demonstrated that natural color sensors could provide a highly efficient estimate of individual tree diameter at breast height (dbh) (± 13.15 cm) as well as forest stand basal area, tree density, and stand density. In the final study, we join a growing number of researchers examining precision applications in forest health monitoring. Here, we demonstrate that UAS, equipped with both natural color and multispectral sensors, are more capable of distinguishing forest health classes than freely available high-resolution airborne imagery. For five health classes, these UAS data produced a 14.93% higher overall accuracy in comparison to the airborne imagery. Together, these three chapters present a wholistic approach to enhancing and enriching precision forest management, which remains a critical requirement for effectively managing diverse forested landscapes