Linking annual tree growth with remotely-sensed terrestrial vegetation indices provides a basis for using tree rings as proxies for ecosystem primary productivity over large spatial and long temporal scales. In contrast with most previous tree ring/remote sensing studies that have focused on temperature-limited boreal and taiga environments, here we compare the normalized difference vegetation index (NDVI) with a network of Pinus brutia tree ring width chronologies collected along ecological gradients in semiarid Cyprus, where both radial tree growth and broader vegetation activity are controlled by drought. We find that the interaction between precipitation, elevation, and land-cover type generate a relationship between radial tree growth and NDVI. While tree ring chronologies at higher-elevation forested sites do not exhibit climatedriven linkages with NDVI, chronologies at lower-elevation dry sites are strongly correlated with NDVI during the winter precipitation season. At lower-elevation sites, land cover is dominated by grasslands and shrublands and tree ring widths operate as a proxy for ecosystem-scale vegetation activity. Tree rings can therefore be used to reconstruct productivity in water-limited grasslands and shrublands, where future drought stress is expected to alter the global carbon cycle, biodiversity, and ecosystem functioning in the 21st century.

Nitrogen (N) is critical to the quantity and quality of agricultural yields. Excess N fertilization is costly, both economically and environmentally (nitrate leaching, eutrophication, greenhouse gas release, soil degradation). This research identifies zones that could substitute the field-long N-rich strips by using spatial analysis of the nitrogen sufficiency index (NSI) and the relation with Apparent Electrical Conductivity (ECa), Elevation, Slope and Soil. NSI calculated from ECa grouped into three classes was capable of minimizing the effects on NDVI. Correlation coefficients (R) between three-class NSI and NSI calculated from the nearest ECA values were very high for all the fields with values between 0.82< R <0.94, with the highest coefficients associated with fields in 2005 and 2007. Meanwhile, three-class NSI coefficients were consistently significant in relation to the NSI reference, with an average of R=0.79 for all the fields. The highest coefficient was detected for 2007, with R=0.89, whereas the lowest values were associated with 2006 (R=0.67). In the case of elevation grouped into four classes, the correlation results were not statistically significant, with overall average values of R<0.70. The maps elaborated from the NSI for ECa grouped into three classes show a high level of accuracy compared to the NSI reference map. The new N-rich zones not only can contribute to mitigating the environmental impact of agricultural practices (reducing 77% of N inputs) but also be an accurate source of data for the analysis of NSI and within-field N variability.

The resolution of spatial data has increased over the past decade making them more accurate in depicting landform features. From using a 60m resolution Landsat imagery to resolution close to a meter provided by data from Unmanned Aerial Systems, the number of pixels per area has increased drastically. Topographic features derived from high resolution remote sensing is relevant to measuring agricultural yield. However, conventional algorithms in Geographic Information Systems (GIS) used for processing digital elevation models (DEM) have severe limitations. Typically, 3-by-3 window sizes are used for evaluating the slope, aspect and curvature. Since this window size is very small compared to the resolution of the DEM, they are mostly resampled to a lower resolution to match the size of typical topographic features and decrease processing overheads. This results in low accuracy and limits the predictive ability of any model using such DEM data. In this dissertation, the landform attributes were derived over multiple scales using the concept of sliding window-based aggregation. Using aggregates from previous iteration increases the efficiency from linear to logarithmic thereby addressing scalability issues. The usefulness of DEM-derived topographic features within Random Forest models that predict agricultural yield was examined. The model utilized these derived topographic features and achieved the highest accuracy of 95.31% in predicting Normalized Difference Vegetation Index (NDVI) compared to a 51.89% for window size 3-by-3 in the conventional method. The efficacy of partial dependence plots (PDP) in terms of interpretability was also assessed. This aggregation methodology could serve as a suitable replacement for conventional landform evaluation techniques which mostly rely on reducing the DEM data to a lower resolution prior to data processing. / National Science Foundation (Award OIA-1355466)

Swanton Pacific Ranch is an approximately 1,300 ha working ranch and forest in northern Santa Cruz County, California, managed by California Polytechnic State University, San Luis Obispo (Cal Poly). On August 12, 2009, the Lockheed Fire burned 300 ha of forestland, 51% of the forested area on the property, with variable fire intensity and mortality. This study used existing inventory data from 47 permanent 0.08 ha (1/5 ac) plots to compare the accuracy of classifying mortality resulting from the fire using digital multispectral imagery and LiDAR. The percent mortality of trees at least 25.4 cm (10”) DBH was aggregated to three classes (0-25, 25-50, and 50-100%). Three separate Classification Analysis and Regression Tree (CART) models were created to classify plot mortality. The first used the best imagery predictor variable of those considered, the Normalized Difference Vegetation Index (NDVI) calculated from 2010 National Agricultural Imagery Program (NAIP) aerial imagery, with shadowed pixel values adjusted, and non-canopy pixels removed. The second used the same NDVI in combination with selected variables from post-fire LiDAR data collected in 2010. The third used the same NDVI in combination with selected variables from differenced LiDAR data calculated using post-fire LiDAR and pre-fire LiDAR collected in 2008. The imagery alone was 74% accurate; the imagery and post-fire LiDAR model was 85% accurate, while the imagery and differenced LiDAR model was 83% accurate. These findings indicate that remote sensing data can accurately estimate post-fire mortality, and that the addition of LiDAR data to imagery may yield only modest improvement.

Master thesis is focused on the evaluation of cereal crop stands with the use of spec-tral measurements and its implementation by crop management practices. In the literature review the basics of yield formation and crop survey are described as well as the im-portance of crop monitoring by spectral measurement for decision support in precision farming practices. Practical part of the thesis includes statistical evaluation of field expe-riment in 2017 and 2018, which were focused on the mapping of crop heterogeneity within the fields with winter wheat. In this experiment, plant samples were taken and ana-lyzed for estimation of nitrogen content and amount of above-ground biomass in various part of the field. This sampling was extended by proximal measurement of solar radiation using spectroradiometer in visible and near infrared part of electromagnetic spectrum. Da-ta processing was performed using descriptive statistics and correlation and regression analysis.The results showed high spatial variability of observed fields.Correlation and re-gression analysis evaluated the relationship between spectral measurement of the stand, represented by vegetation indices, and its laboratory estimation. As the conclusion, spectral measurements was be recommended as a complement to traditional methods of agrobiological control of cereals and could be used for delineation of management zones in site specific crop management.

No description available.

This study examined the severity of wind damage created by Hurricane Katrina in southeast Mississippi to determine how the disturbance was influenced by fragmentation based on different forest ownership groups (Non-corporate private forest, corporate private forest and public forest). MODIS-NDVI percent change products were coupled with ownership, rainfall, and Landsat based thematic maps depicting forest age and forest types using GIS techniques to examine potential contributing factors to possible damage for the study area. Multiple linear and binary logistic regression methods were used to explain the relationship between severity of damage and forest age, forest type, ownership, and rainfall. Results indicate that the NDVI percent change had a negative relationship with forest age diversity and a positive relationship with forest type diversity and rainfall. There was no clear and direct consistent relationship between NDVI percent change and forest ownership.

The forest ecosystem is a dominant landscape in the Gulf of Mexico (GOM) coastal region. Currently, many studies have been carried out to identify factors that drive forest dynamics. Changes in meteorological conditions have been considered as the main factors affecting the forest dynamics. For this study, the statistical regression analysis was used for modeling forest dynamics. Meteorological impact analysis was driven by observed data from PRISM (parameter-elevation regressions on independent slopes model) climate dataset. The forest dynamics was characterized by an indicator, the normalized difference vegetation index (NDVI). The objectives of this study are to 1) to specify and estimate statistical regression models that account for forest dynamics in the Golf of Mexico coastal region, 2) to assess which model used to capture the relationship between forest dynamics and its explanatory variables with the best explanatory power, and 3) to use the best fitted regression model to explain forest dynamics. By using fixed-effects regression methods: ordinary least squares (OLS) and geographically weighted regression (GWR), the sample-point-based regression analysis showed that meteorological factors could generally explain more than half of variation in forest dynamics. In respect of the unexplained variation of forest dynamics, the necessity of using soil to explain forest dynamics was then discussed. The result suggested that the forest dynamics could be explained by both meteorological parameters and soil texture. One of the basic considerations in this study is to include the spatiotemporal heterogeneity caused by seasonality and forest types. The model explanatory power was found differ among forest types (spatially) and seasons (temporally). By constructing regression models with randomly varying intercepts and varying slopes, the linear mixed-effects model (LMM) was fitted on composite county-based data (e.g., precipitation, temperature and NDVI). The use of LMMs was proved to be appropriate for describing forest dynamics to mixed-effects induced by meteorological changes. Based on this finding, I concluded that meteorological changes could play a significant role in forest dynamics through both fixed-effects and random-effects.

Cotton (Gossypium hirsutum L.) reflectance has potential to drive variable rate N (VRN) applications, but more precise definitions of relationships between sensor-observed reflectance, plant height, and N status are necessary. The objectives of this study were to define effectiveness and relationships between three commercially available sensors, and examine relationships of wavelengths and indices obtained by a spectrometer to plant height and N status. Field trials were conducted during 2008-2010 growing seasons at Mississippi State, MS. Fertilizer N rates ranged from 0-135 kg N ha-1 to establish growth differences. Sensor effects were significant, but sensors monitoring Normalized Difference Vegetation Index (NDVI) failed to correlate well with early-season N status. Wavelengths and indices utilizing the red-edge correlated most strongly with N status. Both Guyot’s Red Edge Index (REI) and Canopy Chlorophyll Content Index (I) correlated consistently with N status independent of biomass status early enough in the growing season to drive VRN.

This study looks at the changes to NDVI value in the Weeks Bay Reserve following the impact by Hurricane Katrina. Four Landsat images from March 24, 2005 (Pre-Katrina), September 16, 2005/ April 26, 2006 (Post-Katrina) and August 7, 2002 (Control) were classified into different landcover types and run with the NDVI vegetation index. Those images were compared against each other and showed that the September image had a NDVI value drop of 49% and the April image had a 47% drop as compared to the previous March. The emergent vegetation surrounding the shoreline was most susceptible to changes in NDVI value and recovered the slowest of the tested landcover types. Swift tracks, bay areas, and rivers in the study area where tested and showed that the rivers are the most susceptible change in NDVI value and recovered the slowest.