Browsing by Subject "NDVI"
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Item Normalized Difference Vegetation Index, 2015-2018(2024-02-26) Johnson, Kris; kristofj@d.umn.edu; Johnson, Kris; Natural Resources Research InstituteA Normalized Difference Vegetation Index (NDVI) is an indicator of the live vegetation on the landscape. It was developed at a 10 meter resolution using Google Earth Engine Sentinel-2 satellite imagery. For more information on the Sentinel-2 imagery: https://explorer.earthengine.google.com/#detail/COPERNICUS%2FS2 Script used to derive data: https://code.earthengine.google.com/774e4ed152e4651971dc6e9456998d17 Methods: This is a composite image where each pixel is selected based on the NDVI function (NIR-Red/NIR+Red). This composite thus represents the highest NDVI for every pixel for that location captured by Sentinel-2.Item Nursery Production Method Performance Evaluation Assessed With The Normalized Difference Vegetation Index Derived From An Unmanned Aircraft System Mounted Single-Imager Sensor(2020-03) Bahe, MichaelTrees provide many benefits to urban areas including enhanced human health, pollution mitigation, and reductions in residential energy consumption. The goal of urban forest managers is to develop mature trees with large crowns to maximize these benefits. Urban trees have the highest mortality rate during the initial years post planting, known as the establishment period. In an era of planting trees to reach quotas, the looming fact is many perish during establishment limiting goal achievement. Nursery production methods (NPM) are a controllable factor in practice that may have an impact on establishment success. In this study, urban trees planted in situ from four common NPM’s (balled and burlapped, smooth plastic containers, spring planted bareroot, and gravelbed bareroot) were monitored for three years post planting using the normalized difference vegetation index (NDVI). This data was derived from high-resolution imagery collected with an unmanned aircraft system (UAS). First, the single-imager multispectral sensor selected for this project was evaluated for effectiveness in determining tree health. This was done in a controlled growth chamber environment. Results showed the single-imager sensor derived NDVI values were effective indicators of tree stress within species groups. Second, a novel technique to isolate tree crowns for spectral data analysis with UAS derived imagery was utilized to compare the health of newly planted trees in situ from the four NPM’s. Analysis of the effect NPM’s had on tree health during the establishment period showed minimal differences between the study groups thus providing evidence that each is a viable option for practitioners in urban areas.Item QTL mapping of iron deficiency chlorosis tolerance in soybean using connected populations(2014-03) Jones, Ilene LouiseSoybean iron deficiency chlorosis or IDC is a yield limiting, abiotic stress condition common to calcareous soil types present in the Upper Midwest. Complex interactions among soil chemical and physical properties within these calcareous soils limit the amount of ferrous iron available to soybean plants. The subsequent nutrient deficiency leads to the classic chlorotic phenotype characterized by interveinal yellowing of new growth trifoliates. IDC is responsible for yield losses up to 0.8 Mg ha-1 amounting to an estimated economic loss of $120 million per annum. To mitigate yield losses, growers prefer to plant IDC tolerant cultivars; however, IDC tolerant cultivars have been known to yield less on non-chlorotic soils. In order to improve IDC tolerance without an associated reduction in yield, we evaluated yield and IDC performance using a network of 13 F4-derived recombinant inbred line (RIL) populations connected by common parents. Chlorosis severity was evaluated using two methods: visual chlorosis ratings and remote sensing via normalized difference vegetative index (NDVI) values collected from the GreenSeeker® RT100 System. NDVI values correlated strongly with visual chlorosis ratings with the largest negative Pearson's correlation coefficient of -0.89 (p-value < 0.0001) captured at the V4 growth stage. NDVI values collected at V4 were moderately correlated to yield with a Pearson's correlation coefficient of -0.61 (p-value < 0.0001), indicating that IDC tolerant lines yield less than IDC susceptible lines on non-chlorotic soils. Co-localization of IDC and yield QTL detected on linkage groups A1/5, J/16, and L/19 confirm that the correlations are in part due to genetically linked loci or pleiotropic effects of a single locus.Item Small UAV Position and Attitude, Raw Sensor, and Aerial Imagery Data Collected over Farm Field with Surveyed Markers(2015-02-25) Mokhtarzadeh, Hamid; Colten, Todd; mokh0006@umn.edu; Mokhtarzadeh, HamidImagery and sensor data from a commercial small Uninhabited Aerial Vehicle flown over an agriculture field on the morning of October 22, 2014 have been logged and documented. The field includes 16 surveyed ground control points laid out in a 4x4 square serving as known ground control points. This data set serves to study both challenges and opportunities of UAV-based remote sensing for precision agriculture applications. The raw sensor data can be used for navigation system design and analysis. The imagery and logged aircraft state can be used for image processing as well as remote sensing analysis. It is being shared to served as a documented data set for testing new concepts and ideas.