1 ABSTRACT The work deals with the use of vegetation indices to study temporal variation in vegetation phenology. The first part was devoted to detailed analysis of domestic and foreign literature, which deals with the work processed in this field. The main research questions were if changed start, end and length of growing period during the analysis period. Other research theme was comparision with ground phenological data. Another objective of this work was search dependencies computed data phenological variables from vegetation indicies with phenological ground data. As a basic data set was used GIMMS set, which distributes the vegetation index NDVI. Other data sets were MERIS MTCI, data MODIS with vegetation indices NDVI, EVI a LAI. The results of analyzes of vegetation phenology show trends in most shifts at the beginning of growing season, where was a shift to an earlier time. Results of the analysis of vegetation remote sensing data with ground-based phenological data ČHMÚ were unfolding always according to the specific forest phenological stations. Interesting results were at the phenological station Svoboda nad Úpou, where the results of trends directives were consistent in almost all data sets. Comparison of process curves vegetation indicies with ground data corresponded most curves at selected...

Este trabalho teve como objetivo principal analisar, em condições de campo, o efeito de lâminas de irrigação e de doses de nitrogênio sobre o comportamento espectral da cultura do feijão, a partir de medidas de reflectância hiperespectral, na faixa de 350 a 1150 nm, e da correlação entre os parâmetros biofísicos e as variáveis agronômicas da cultura, nessas condições. O experimento foi conduzido na área experimental de Agricultura Irrigada da Fazenda Areão, sob a responsabilidade do Departamento de Engenharia Rural da Escola Superior de Agricultura Luiz de Queiroz DER/ESALQ/USP, localizada no município de Piracicaba, SP, de setembro a dezembro de 2007. O delineamento experimental foi em blocos casualizados com parcelas subdivididas, alocando-se, nas parcelas, os tratamentos relacionados aos níveis de irrigação ((179,5; 256,5; 357,5 e 406,2 mm) e nas subparcelas os tratamentos relativos às doses de nitrogênio (0; 80 e 160 kg ha-1), totalizando doze tratamentos em cada bloco. Como os tratamentos foram dispostos em quatro blocos (repetições), o total de parcelas experimentais foi de 48. As medidas radiométricas de campo foram adquiridas por meio do espectrorradiômetro SPECTRON SE-590, em sete datas, durante o ciclo da cultura. Foram coletados, ainda, a altura das plantas, o índice de área foliar, a produtividade de grãos, o número de grãos por vagem e o número de vagens por planta. Os índices espectrais calculados foram o NDVI e o NB_NDVI. Foi aplicada a remoção do contínuo nos espectros de reflectância para analisar a profundidade da banda e a área da banda de absorção, centrada em 665 nm. O fator água influenciou as variáveis biofísicas (IAF, altura da planta, produtividade, número de vagens por planta e número de grãos por vagem), sendo maiores os valores encontrados nas plantas que receberam maiores quantidades de água. O aumento da dose de nitrogênio não influenciou as variações das variáveis biofísicas, devido, possivelmente, ao processo de lixiviação desse nutriente para as camadas abaixo do sistema radicular da cultura. As análises dos índices de vegetação e dos parâmetros da remoção do contínuo (área e profundidade da banda de absorção) mostraram que as lâminas de irrigação afetaram o comportamento espectral do feijão no comprimento de onda de 665 nm, e que esses parâmetros não foram influenciados pelas doses de N. Os índices de vegetação (NDVI e NB_NDVI) e os parâmetros da remoção do contínuo (a profundidade da banda e a área da banda de absorção) foram eficientes na estimativa do IAF, da altura de plantas e da produtividade de grãos. Durante todo o desenvolvimento da cultura, as melhores correlações entre as variáveis biofísicas e as variáveis espectrais foram observadas nos estádios V4 e R6 de acordo com a variável analisada, sendo, portanto, estes os melhores estádios para monitorar espectralmente a cultura. Diante das metodologias utilizadas, as variáveis biofísicas foram mais bem estimadas pelo índice NB_NDVI, nos estádios V4 e R6, quando comparado ao índice NDVI e aos parâmetros da remoção do contínuo (profundidade e área da banda de absorção). / The objective of this research was to analyze the effect of irrigation levels and nitrogen fertilization on the spectral behavior, in the wavelength range of 350 to 1150 nm, of bean crop and the correlation between biophysical parameters and agronomical variables. The experiment was carried on at the Fazenda Areão, located at the University of São Paulo (ESALQ/USP) campus, Piracicaba, São Paulo, Brazil, from September to December, 2007. The experimental design was the randomized blocks, with split plots, with 12 treatments, 4 irrigation levels (179,5; 256,5; 357,5 e 406,2 mm) and 3 nitrogen rates (0; 80 e 160 kg ha-1), and four blocks. The field radiometric data were acquired with the SPECTRON-SE 590 spectroradiometer for seven dates during the crop growing season. Plant height, leaf area index (LAI), grain yield, the pod number per plant and the grain number per pod were also acquired. The spectral indices used were NDVI and NB_NDVI. The continuum removal was applied for the reflectance spectrum, on visible region centered on 665 nm, to analyze the band area and band depth. The irrigation also influenced the biophysical variables, so the largest medium values were observed in the treatments that used the largest irrigation levels. The nitrogen fertilization did not interfere on the biophysical variables, probably due to the leaching process. The vegetation indices and continuum removal analysis showed that irrigation levels affected the spectral behavior of bean crop on 665 nm and this parameters had not been influenced by nitrogen levels. The vegetation indices (NDVI and NB_NDVI) and the continuum removal parameters (band area and band depth) were efficient in the estimate of IAF, plant height and grain yield. During all the crop development, the best correlations between biophysical variables and spectral variables were observed on V4 and R6 stages, according to the variable analized. In face of the methodologies used, the biophysical variables were better estimated by NB_NDVI, on V4 and R6 stages, when compared with NDVI and the continuum removal parameters.

Arid lands cover approximately 30% of the earth’s surface. Due to the broadness, remoteness, and harsh condition of these lands, land condition assessment and monitoring using ground-based techniques appear to be limited. Remote sensing imagery with its broad areal coverage, repeatability, cost and time-effectiveness has been suggested and used as an alternative approach for more than three decades. This thesis evaluated the potential of different remote sensing techniques for assessing and monitoring land condition of southern arid lands of South Australia. There were four specific objectives: 1) to evaluate vegetation indices derived from multispectral satellite imagery for prediction of vegetation cover; 2) to compare vegetation indices and field measurements for detecting vegetation changes and assessing land condition; 3) to examine the potential of hyperspectral imagery for discriminating vegetation components that are important in land management using unmixing techniques; and 4) to test whether spatial heterogeneity in land surface reflectance can provide additional information about land condition and effects of management on land condition. The study focused on Kingoonya and Gawler Soil Conservation Districts that were dominated by chenopod shrublands and low open woodlands over sand plains and dunes. The area has been grazed predominately by sheep for more than 100 years and land degradation or desertification due to overgrazing is evident in some parts of the region, especially around stock watering points. Grazing is the most important factor that influences land condition. Four full scenes of Landsat TM and ETM+ multispectral and Hyperion hyperspectral data were acquired over the study area. The imagery was acquired in dry seasons to highlight perennial vegetation cover that has an important role in land condition assessment and monitoring. Slope-based, distance-based, orthogonal transformation and plant-water sensitive vegetation indices were compared with vegetation cover estimates at monitoring points made by state government agency staff during the first Pastoral Lease assessments in 1991. To examine the performance of vegetation indices, they were tested at two scales: within two contrasting land systems and across broader regional landscapes. Of the vegetation indices evaluated, selected Stress Related Vegetation Indices using red, nearinfrared and mid-infrared bands consistently showed significant relationships with vegetation cover at both land system and landscape scales. Estimation of vegetation cover was more accurate within land systems than across broader regions. Total perennial and ephemeral plant cover was predicted best within land systems (R2=0.88), while combined vegetation, plant litter and soil cryptogam crust cover was predicted best at landscape scale (R2=0.39). The results of applying one of the stress related vegetation indices (STVI-4) to 1991 TM and 2002 ETM+ Landsat imagery to detect vegetation changes and to 2005 Landsat TM imagery to discriminate Land Condition Index (LCI) classes showed that it is an appropriate vegetation index for both identifying trends in vegetation cover and assessing land condition. STVI-4 highlighted increases and decreases in vegetation in different parts of the study area. The vegetation change image provided useful information about changes in vegetation cover resulting from variations in climate and alterations in land management. STVI-4 was able to differentiate all three LCI classes (poor, fair and good condition) in low open woodlands with 95% confidence level. In chenopod shrubland and Mount Eba country only poor and good conditions were separable spectrally. The application of spectral mixture analysis to Hyperion hyperspectral imagery yielded five distinct end-members: two associated with vegetation cover and the remaining three associated with different soils, surface gravel and stone. The specific identity of the image end-members was determined by comparing their mean spectra with field reflectance spectra collected with an Analytical Spectral Devices (ASD) Field Spec Pro spectrometer. One vegetation end-member correlated significantly with cottonbush vegetation cover (R2=0.89), distributed as patches throughout the study area. The second vegetation end-member appeared to map green and grey-green perennial shrubs (e.g. Mulga) and correlated significantly with total vegetation cover (R2=0.68). The soil and surface gravel and stone end-members that mapped sand plains, sand dunes, and surface gravel and stone did not show significant correlations with the field estimates of these soil surface components. I examined the potential of a spatial heterogeneity index, the Moving Standard Deviation Index (MSDI), around stock watering points and nearby ungrazed reference sites. One of the major indirect effects of watering points in a grazed landscape is the development around them of a zone of extreme degradation called a piosphere. MSDI was applied to Landsat red band for detection and assessment of these zones. Results showed watering points had significantly higher MSDI values than non-degraded reference areas. Comparison of two vegetation indices, the Normalized Difference Vegetation Index (NDVI) and Perpendicular Distance vegetation index (PD54), which were used as reference indices, showed that the PD54 was more sensitive than NDVI for assessing land condition in this perennial-dominated arid environment. Piospheres were found to be more spatially heterogeneous in land surface reflectance. They had higher MSDI values compared to non-degraded areas, and spatial heterogeneity decreased with increasing distance from water points. The study has demonstrated overall that image-based indices derived from Landsat multispectral and Hyperion hyperspectral imagery can be used with field methods to assess and monitor vegetation cover (and consequently land condition) of southern arid lands of South Australia in a quick and efficient way. Relationships between vegetation indices, end-members and field measurements can be used to estimate vegetation cover and monitor its variation with time in broad areas where field-based methods are not effective. Multispectral vegetation indices can be used to assess and discriminate ground-based land condition classes. The sandy-loam end-member extracted from Hyperion imagery has high potential for monitoring sand dunes and their movement over time. The MSDI showed that spatial heterogeneity in land surface reflectance can be used as a good indicator of land degradation. It differentiated degraded from nondegraded areas successfully and detected grazing gradients slightly better than widely used vegetation indices. Results suggest further research using these remote sensing techniques is warranted for arid land condition assessment and monitoring in South Australia. / http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1295218 / Thesis (Ph.D.) -- School of Earth and Environmental Science, 2007

Este trabalho teve como objetivo principal analisar, em condições de campo, o efeito de lâminas de irrigação e de doses de nitrogênio sobre o comportamento espectral da cultura do feijão, a partir de medidas de reflectância hiperespectral, na faixa de 350 a 1150 nm, e da correlação entre os parâmetros biofísicos e as variáveis agronômicas da cultura, nessas condições. O experimento foi conduzido na área experimental de Agricultura Irrigada da Fazenda Areão, sob a responsabilidade do Departamento de Engenharia Rural da Escola Superior de Agricultura Luiz de Queiroz DER/ESALQ/USP, localizada no município de Piracicaba, SP, de setembro a dezembro de 2007. O delineamento experimental foi em blocos casualizados com parcelas subdivididas, alocando-se, nas parcelas, os tratamentos relacionados aos níveis de irrigação ((179,5; 256,5; 357,5 e 406,2 mm) e nas subparcelas os tratamentos relativos às doses de nitrogênio (0; 80 e 160 kg ha-1), totalizando doze tratamentos em cada bloco. Como os tratamentos foram dispostos em quatro blocos (repetições), o total de parcelas experimentais foi de 48. As medidas radiométricas de campo foram adquiridas por meio do espectrorradiômetro SPECTRON SE-590, em sete datas, durante o ciclo da cultura. Foram coletados, ainda, a altura das plantas, o índice de área foliar, a produtividade de grãos, o número de grãos por vagem e o número de vagens por planta. Os índices espectrais calculados foram o NDVI e o NB_NDVI. Foi aplicada a remoção do contínuo nos espectros de reflectância para analisar a profundidade da banda e a área da banda de absorção, centrada em 665 nm. O fator água influenciou as variáveis biofísicas (IAF, altura da planta, produtividade, número de vagens por planta e número de grãos por vagem), sendo maiores os valores encontrados nas plantas que receberam maiores quantidades de água. O aumento da dose de nitrogênio não influenciou as variações das variáveis biofísicas, devido, possivelmente, ao processo de lixiviação desse nutriente para as camadas abaixo do sistema radicular da cultura. As análises dos índices de vegetação e dos parâmetros da remoção do contínuo (área e profundidade da banda de absorção) mostraram que as lâminas de irrigação afetaram o comportamento espectral do feijão no comprimento de onda de 665 nm, e que esses parâmetros não foram influenciados pelas doses de N. Os índices de vegetação (NDVI e NB_NDVI) e os parâmetros da remoção do contínuo (a profundidade da banda e a área da banda de absorção) foram eficientes na estimativa do IAF, da altura de plantas e da produtividade de grãos. Durante todo o desenvolvimento da cultura, as melhores correlações entre as variáveis biofísicas e as variáveis espectrais foram observadas nos estádios V4 e R6 de acordo com a variável analisada, sendo, portanto, estes os melhores estádios para monitorar espectralmente a cultura. Diante das metodologias utilizadas, as variáveis biofísicas foram mais bem estimadas pelo índice NB_NDVI, nos estádios V4 e R6, quando comparado ao índice NDVI e aos parâmetros da remoção do contínuo (profundidade e área da banda de absorção). / The objective of this research was to analyze the effect of irrigation levels and nitrogen fertilization on the spectral behavior, in the wavelength range of 350 to 1150 nm, of bean crop and the correlation between biophysical parameters and agronomical variables. The experiment was carried on at the Fazenda Areão, located at the University of São Paulo (ESALQ/USP) campus, Piracicaba, São Paulo, Brazil, from September to December, 2007. The experimental design was the randomized blocks, with split plots, with 12 treatments, 4 irrigation levels (179,5; 256,5; 357,5 e 406,2 mm) and 3 nitrogen rates (0; 80 e 160 kg ha-1), and four blocks. The field radiometric data were acquired with the SPECTRON-SE 590 spectroradiometer for seven dates during the crop growing season. Plant height, leaf area index (LAI), grain yield, the pod number per plant and the grain number per pod were also acquired. The spectral indices used were NDVI and NB_NDVI. The continuum removal was applied for the reflectance spectrum, on visible region centered on 665 nm, to analyze the band area and band depth. The irrigation also influenced the biophysical variables, so the largest medium values were observed in the treatments that used the largest irrigation levels. The nitrogen fertilization did not interfere on the biophysical variables, probably due to the leaching process. The vegetation indices and continuum removal analysis showed that irrigation levels affected the spectral behavior of bean crop on 665 nm and this parameters had not been influenced by nitrogen levels. The vegetation indices (NDVI and NB_NDVI) and the continuum removal parameters (band area and band depth) were efficient in the estimate of IAF, plant height and grain yield. During all the crop development, the best correlations between biophysical variables and spectral variables were observed on V4 and R6 stages, according to the variable analized. In face of the methodologies used, the biophysical variables were better estimated by NB_NDVI, on V4 and R6 stages, when compared with NDVI and the continuum removal parameters.

The Green Swamp Preserve is a large geographic area that has sustained many changes since Europeans settled in Florida. There has been little published research on the impacts of anthropogenic activity on this system. This thesis research seeks to document more recent changes in the Green Swamp and to evaluate the effects of various human activities on the system. The study period is from 1985 to 2015. For this time period changes in land use and landcover were examined using neural network classifications. Changes in vegetation health were evaluated by examining Normalized Difference Vegetation Index (NDVI) and Green Vegetation Index differences. Field site visits were made to document current conditions at thirty sample locations within the study area. Changes in land use and landcover and vegetation health were evaluated in relation to anthropogenic activities such as proximity to pollution sources, conservation lands and restoration sites. Statistical analysis was conducted to determine if statistically significant clustering occurred in these changes and if present geographically weighted regressions were performed to determine if a significant spatial relationship existed between the clustering and the various human activities. WAP data showed an overall decline in wetland health at the assessment sites and showed a trend of lower wetland health at sites within 2 Km of pollution sources, specifically petroleum tank contamination sites and state roads. The statistically significant clustering identified in land use landcover changes from 1985 to 2015 were in relation to changes from field, forested and wetland landcover types to built environments. Spatial relationships were identified between the proximity of petroleum tank contamination sites, state roads and solid waste facilities and clustering of NDVI decreases from 1985 to 2015. NDVI increases in the study area from 1985 to 2015 also showed statistically significant clustering in relation to conservation lands and lands purchased by the Southwest Florida Water Management District for environmental protection. These preliminary findings suggest that human activities may have influenced changes in the health of the Green Swamp. Further, more extensive research is suggested to confirm these findings.

<div>Increasing the capability of maize hybrids to use nitrogen (N) more efficiently is a common goal that contributes to reducing farmer costs and limiting negative environmental impacts. However, development of such hybrids is costly and arduous due to the repeated need for laborious field and laboratory measurements of whole-plant biomass and N uptake in large early-stage breeding programs. This research evaluated alternative in-season methodologies, including field-based physiological measurements and hyperspectral remote imagery, as surrogate or predictive measures of important end-of-season N efficiency parameters. </div><div><br></div><div>Differences in the genetic potential of 285 hybrids (derived from test crosses to a single tester) with respect to N Internal Efficiency (NIE, grain yield per unit of accumulated plant N) were investigated at two Indiana locations in 2015. The hybrids (representing both early and late maturity groups) were grown at one low N rate and a single plant density. Germplasm sources included USDA, Dow AgroSciences, and “control” checks. Various secondary traits (plant height, stalk diameter, LAI, green leaf counts, and SPAD measurements) were evaluated for their potential role as surrogate measurements for N metrics at maturity (R6) such as plant N content or NIE. Four band (RGB, NIR) multispectral airborne remote sensing imagery at R1 and R3/R4 was also collected. The key findings were: 1) identification of the 10 highest and 10 lowest ranked hybrids for each maturity group in both grain yield and NIE categories, 2) the discovery of 5 top performing hybrids which had both high NIE and high yield, 3) strong correlations of leaf SPAD (at R1 and R2/R3) to grain yield or plant N at R6, 4) none of the surrogate measurements were significantly correlated to NIE, and 5) vegetation indices (NDVI and SR) from the remote imaging were not predictive of hybrid differences in yields or whole plant N content at R6. From these results we concluded genetic potential exists among current maize germplasm for NIE breeding improvements, but that more in-depth investigations were needed into other surrogate measures of relevant N efficiency traits in hybrid comparisons. </div><div><br></div><div>Next, hyperspectral imaging was investigated as a potential predictor of agronomic parameters related to N Use Efficiency (NUE, understood here as grain yield relative to applied N fertilizer input). Hyperspectral vegetation indices (HSI) were used to extract the image features for predicting N concentration (whole plant N at R6, %N), Nitrogen Conversion Efficiency (biomass per unit of plant N at R6, NCE), and NIE. Images were collected at V16/V18 and R1/R2 by manned aircraft and unmanned aerial vehicles (UAVs) at 50 cm spatial resolution. Nine maize hybrids, or a subset, were grown under N stress conditions with two plant densities over three site years in either 2014 or 2017. Forty HSI-based mixed models were analyzed for their predictability relative to the ground reference values of %N, NCE, and NIE. Two biomass and structural indices (HBSI1_682,855 and HBSI2_682,910 at R1) were predictive of NCE values and capably differentiated the highest and lowest ranked NCE hybrids. The highest prediction accuracy for NIE was achieved by two biochemical indices (HBCI_8515,550 at both V16 and R1, and HBCI9_490,550 at R1). These also allowed for hybrid differentiation of the highest and lowest ranked NIE hybrids. From these results, we concluded that accurate end-season prediction of hybrid differences in NCE and NIE was possible from mid-season hyperspectral imaging (yet not for %N). Furthermore, the quality of the predictions was dependent on image timing, actual HSI, and the targeted N parameter at maturity. </div><div><br></div><div>One benefit to hyperspectral imaging is that it can provide greater discrimination of imaged materials through its narrow, contiguous bands. However, the data are highly correlated in some ranges. This problem was mitigated through the use of partial least squares regressions (PLSR) to predict the three N parameters from the field data described previously. Data were divided into train and test; then ten-fold cross validation was performed. The twelve models evaluated included those with 89 image bands of 5 nm widths and a selected, reduced set of hyperspectral bands as predictors. The key findings were that PLSR models based on V16 and R1 images provided accurate predictions for final whole-plant %N (R² = 0.73, CV = 11%; R² = 0.68, CV = 10%) and NCE at R6 (R² = 0.71, CV = 10%; R² = 0.73, CV = 12%) but not NIE. Additionally, accurate hybrid differentiation was possible with the combination of hyperspectral imaging and PLSR at R1 to predict %N and NCE values at R6 stage. </div><div><br></div><div>The PLSR and HSI results from this research showed that hyperspectral imaging has the potential for prediction of NUE parameters through non-destructive remote sensing at a broad scale. Additional validation is needed through the study of other genotypes and locations. Nevertheless, practical application of these methods through the integration into early stage breeding programs may allow the early identification of the highest and lowest ranked hybrids providing data-driven decisions for selecting genotypes. Implementation of improved imaging approaches may drive the increased development of maize hybrids with superior NUE. </div><div><br></div>

Southern Appalachian wetlands have yet to be studied in terms of net primary production (NPP), thus few studies have been conducted to examine what environmental factors have relationships with NPP. To that end, this research investigates several facets of southern Appalachian wetland production. The research was divided into three studies. The first study was conducted to answer the question of what environmental factors have relationships with NPP. It appears that stream discharge and annual precipitation had the strongest relationships with NPP (r = 0.91, p <0.05 and r = 0.81, p <0.05, respectively), yet both factors showed multicolinearity (r = 0.97, p <0.05). The strong relationships between hydrologic factors and NPP is similar to montane wetlands in the western United States. The second study was conducted to examine the relationship between water chemistry and NPP. Calcium (Ca), Magnesium (Mg), and pH were examined in order to determine if any of the aforementioned factors had a relationship with NPP. Neither Ca (r = -0.34, p = 0.0835) nor Mg (r =-0.38, p = 0.0535) had strong relationships with NPP, though pH (r = -0.66, p <0.05) had a strong negative relationship with NPP. The acidity of the stream water, driven by the acid rain in the southern Appalachians, creates enhanced conditions for wetland plants to grow. The third study was conducted to establish which vegetation index was best for estimating NPP from proximally and remotely sensed data. The findings suggest that VARIRed Edge was best for examining NPP at the in situ level, NDVI was best for examining NPP at the airborne level, and the DVI was the best for examining NPP at the satellite level. NPP in southern Appalachian wetlands is driven by the chemistry, specifically the pH, of stream discharge and annual precipitation and can be monitored by NDVI using NAIP data or DVI using Landsat data. The examination of NPP in southern Appalachians in response to environmental factors and water chemistry along with the examination of vegetation indices at three levels of platforms will help to monitor and manage these rare and unique ecosystems in the future.

No description available.

Forests have the ability to sequester carbon from our atmosphere. Soil respiration (Rs) plays a role in a forest's ability to do so as it is a significant source of carbon dioxide back to the atmosphere. Therefore, understanding the process of Rs under varying conditions is gaining more attention. As of now we have a relatively good understanding of Rs under managed forest ecosystems such as pine plantations. This particular study examined Rs under different overstories and understories in a high elevation Southern Appalachian forest in order to get a better understanding of Rs under a natural hardwood system. The four vegetation types under consideration were an eastern hemlock (Tsuga canadensis L. Carriere) dominated overstory, a hardwood overstory with little to no understory, a mountain laurel (Kalmia latifolia L.) dominated understory, and a cinnamon fern (Osmundastrum cinnamomeum (L.) C.Presl) dominated understory. Differing temporal variations of Rs were observed under the vegetation types. We found monthly differences in rates among vegetation type however, an overall annual difference in Rs rates between vegetation types was not observed. This simply indicates the importance of observing Rs under different time scales to get a better understanding of its variation. We also calculated vegetation indices from remotely-sensed data to explore any relationships to Rs as well as if the indices themselves could improve out model. A vegetation index is a number that is calculated for every pixel in a remotely sensed image and represents plant vigor or abundance. Few significant relationships were found between the indices and Rs. Future work may want to better understand vegetation indices' spatial extent and accuracy in order to find whether they may be beneficial in Rs estimation. Understanding the influence of varying vegetation type and soil temperature and moisture on Rs will ultimately improve our ability to predict what drives changes in carbon fluxes. / Master of Science / Forests have the ability to sequester carbon from our atmosphere. Soil respiration (Rs) plays a role in a forest’s ability to do so as it is a significant source of carbon dioxide back to the atmosphere. Therefore, understanding the process of Rs under varying conditions is gaining more attention. As of now we have a relatively good understanding of Rs under managed forest ecosystems such as pine plantations. This particular study examined Rs under different overstories and understories in a high elevation Southern Appalachian forest in order to get a better understanding of Rs under a natural hardwood system. The four vegetation types under consideration were an eastern hemlock (Tsuga canadensis L. Carriere) dominated overstory, a hardwood overstory with little to no understory, a mountain laurel (Kalmia latifolia L.) dominated understory, and a cinnamon fern (Osmundastrum cinnamomeum (L.) C.Presl) dominated understory. Differing temporal variations of Rs were observed under the vegetation types. We found monthly differences in rates among vegetation type however, an overall annual difference in Rs rates between vegetation types was not observed. This simply indicates the importance of observing Rs under different time scales to get a better understanding of its variation. We also calculated vegetation indices from remotely-sensed data to explore any relationships to Rs as well as if the indices themselves could improve out model. A vegetation index is a number that is calculated for every pixel in a remotely sensed image and represents plant vigor or abundance. Few significant relationships were found between the indices and Rs. Future work may want to better understand vegetation indices’ spatial extent and accuracy in order to find whether they may be beneficial in Rs estimation. Understanding the influence of varying vegetation type and soil temperature and moisture on Rs will ultimately improve our ability to predict what drives changes in carbon fluxes.

Cover crops are widely planted in the mid-Atlantic region for their environmental and agronomic benefits, but incomplete or delayed termination can lead to cover crops becoming weeds in the subsequent cash crop, known as volunteers. Studies were conducted from 2020-2022 to evaluate the effect of four commonly grown cover crop species, winter wheat (Triticum aestivum L.), cereal rye (Secale cereal L.), hairy vetch (Vicia villosa Roth), and rapeseed (Brassica napus L.), and four termination timings; 28, 14, 5, and 1 days before corn planting (DBP). Results indicated volunteerism was only an issue with rapeseed. Delaying rapeseed termination resulted in 0, 5, 12, and 22 volunteer plants m-2 at 28, 14, 5, and 1 DBP in corn. In order to manage these rapeseed volunteers, herbicide evaluations were conducted and indicated that atrazine, isoxaflutole, metribuzin, and pyroxasulfone resulted in 92-94% control when applied preemergence. Similarly, atrazine and glyphosate provided 99% rapeseed control and glufosinate resulted in 89% control when applied postemergence. Therefore, volunteers can easily be controlled with commonly used herbicides in corn. Studies were also conducted to evaluate the benefits of these cover crops, which have the potential to overcome the aforementioned risks. Results indicate that hairy vetch produced the most biomass and provided greater control of summer annual grasses, small-seeded broadleaf and large-seeded broadleaf weeds than other cover crops. Biomass accumulation and extent of weed control increased with delaying cover crop termination. Corn yield was greatest following hairy vetch and was least in rapeseed plots. Termination of cover crops 14 DBP planting increased corn yield by 12%; whereas termination at 1 DBP decreased corn yield by 15% as compared to no cover crop-no till plots. Effective termination of cover crops is an important management consideration and information on termination efficiency can help in devising management plans. In order to assist managers by evaluating cover crop termination efficiency, studies were conducted to evaluate selective and non-selective herbicides and a roller crimper for correlating vegetative indices with visible termination efficiency. Among vegetative indices, the Green Leaf Index had the highest Pearson correlation coefficient for wheat (r = -0.79, p = <0.0001) and cereal rye (r = -0.80, p = <0.0001) with visible termination efficiency. Whereas, for rapeseed, Normalized Difference Vegetation Index (NDVI) had the highest correlation coefficient (r = -0.66, p = <0.0001). However, for hairy vetch none of the vegetative indices correlated significantly with visible termination efficiency. While further research is necessary, remote sensing technologies may help in devising management plans by increasing crop scouting efficiency. / Master of Science in Life Sciences / Cover crops reduce soil erosion, leaching of soil nutrients in the water bodies, and provide benefits like weed suppression and improving the cash crop yield. Cover crops are generally planted in fall after the harvest of cash crop and are killed (terminated) before or after planting of next cash crop in the spring. Cover crop plants can also become weedy when they grow as volunteer plants in cash crops and if not terminated effectively. Therefore, effective termination of cover crops is also an important management consideration. Keeping these aspects in view, field experiments were conducted to evaluate different cover crops, winter wheat, cereal rye, hairy vetch, and rapeseed and four termination timings, 28, 14, 5, and 1 days before corn planting (DBP) for biomass accumulation, weed control, and impact on corn yield. Among cover crops, hairy vetch was found to be the best in terms of biomass production, weed control, and improving corn yield, whereas rapeseed had least biomass accumulation and reduced corn yield. Among termination timing, 1 and 5 DBP resulted in the most biomass production and weed control, but corn yield was greatest when terminated at 14 DBP. Delaying rapeseed termination from 28 DBP to 14, 5, and 1 DBP increased volunteer rapeseed in corn by 5, 12, and 22 plants m-2. Preemergence (PRE) and postemergence (POST) herbicides were evaluated for volunteer rapeseed control in corn. Among preemergence (PRE) herbicides, mesotrione, rimsulfuron and flumioxazin provided more than 95% volunteer rapeseed control, whereas atrazine, isoxaflutole, metribuzin, and pyroxasulfone provided 92-94% control. Among postemergence (POST) herbicides, atrazine and glyphosate provided 99% visible control of rapeseed, followed by glufosinate (89%). Various selective and non-selective herbicides were also evaluated for the termination of wheat, cereal rye, hairy vetch, and rapeseed. Non-selective herbicides like glyphosate, glufosinate and paraquat were found more effective for termination of cover crops as compared to non-selective herbicides. Vegetative indices (VI) were evaluated and correlated with visible termination efficiency (ground truth data) and found that VI can be used for estimating termination efficiency and these estimates can help in devising plans for management operations. Among VI, Green Leaf Index had the highest correlation coefficient for wheat and cereal rye visible termination ratings. Whereas for rapeseed, Normalized Difference Vegetation Index (NDVI) had the highest correlation coefficient value. However, for hairy vetch none of the vegetative indices correlated significantly with visible termination efficiency. Overall, hairy vetch was found to be the best cover crop for biomass accumulation, weed control and corn yield improvement. Delayed termination of rapeseed plants resulted in infestation of volunteer rapeseed in corn and reduced corn yield. However, volunteer rapeseed plants can be effectively managed by effective termination and use of proper PRE and POST herbicides in corn. Furthermore, remote sensing-based VIs have shown potential to estimate cover crop termination efficiency.