Doctor of Philosophy / Department of Biological & Agricultural Engineering / Stacy L. Hutchinson / The Soil Conservation Service-Curve Number (SCS-CN) method is widely used to estimate direct runoff from rainfall events; however, the method does not account for the dynamic rainfall-runoff relationship. This study used back-calculated curve numbers (CNs) and Normalized Difference Vegetation Index (NDVI) to develop NDVI-based CNs (CN[subscript]NDV) using four small northeastern Kansas grassland watersheds with average areas of 1 km² and twelve years (2001–2012) of daily precipitation and runoff data. Analysis indicated that the CN[subscript]NDVI model improved runoff predictions compared to the SCS-CN method. The CN[subscript]NDVI also showed greater variability in CNs, especially during growing season, thereby increasing the model’s ability to estimate relatively accurate runoff from rainfall events since most rainfall occurs during the growing season. The CN[subscript]NDVI model was applied to small, disturbed grassland watersheds to assess the model’s ability to detect land cover change impact for military maneuver damage and large, diverse land use/cover watersheds to assess the impact of scaling up the model. CN[subscript]NDVI application was assessed using a paired watershed study at Fort Riley, Kansas. Paired watersheds were identified through k-means and hierarchical-agglomerative clustering techniques. At the large watershed scale, Daymet precipitation was used to estimate runoff, which was compared to direct runoff extracted from stream flow at gauging points for Chapman (grassland dominated) and Upper Delaware (agriculture dominated) watersheds. In large, diverse watersheds, CN[subscript]NDVI performed better in moderate and overall flow years. Overall, CN[subscript]NDVI more accurately simulated runoff compared to SCS-CN results: The calibrated model increased by 0.91 for every unit increase in observed flow (r = 0.83), while standard CN-based flow increased by 0.506 for every unit increase in observed flow (r = 0.404). Therefore, CN[subscript]NDVI could help identify land use/cover changes and disturbances and spatiotemporal changes in runoff at various scales. CN[subscript]NDVI could also be used to accurately estimate runoff from precipitation events in order to instigate more timely land management decisions.

Master of Public Health / Department of Diagnostic Medicine/Pathobiology / Michael W. Sanderson / A zoonotic disease is any disease or infection that is naturally transmissible from vertebrate animals to humans. Over 200 zoonoses have been described (Zoonoses and the Human-Animal-Ecosystems Interface, 2013). Many zoonotic viruses are arboviruses, viruses transmitted by an infected, blood-sucking, arthropod vector (Hunt, 2010). There are several endemic arboviruses in the United States; some foreign arboviruses, such as Rift Valley fever (RVF) virus, are potential bioterrorism agents (Dar, 2013). Arboviruses, both endemic and foreign, threaten public health (Gubler, 2002) and therefore disease surveillance, vector control and public education are all vital steps in minimizing arboviral disease impact in the United States. Mosquito-borne disease threats, such as West Nile virus and Rift Valley fever, are constant concerns in the United States and globally. Current strategies to prevent and control mosquito-borne diseases utilize vector distribution, seasonal and daylight timing, and variation in population numbers. Climate factors, such as availability of still water for development of immature mosquitoes, shade, and rainfall, are known to influence population dynamics of mosquitoes. Using 1995-2011 mosquito population surveillance data from Fort Riley, Kansas, we compared population numbers of Culex tarsalis (Diptera: Culicidae), a vector of several arboviruses including West Nile virus and potentially Rift Valley fever, to a satellite-derived index of climate, the Normalized Difference Vegetation Index (NDVI) anomaly. No correlation between the population numbers and NDVI anomaly was observed, which contrasts with results from similar analyses in other locations. These findings suggest a need for continued investigation into mosquito population dynamics in additional ecological regions of the United States to better describe the heterogeneity of environment-population relationships within and among mosquito species.

Apesar de a vegetação ser considerada fator controlador de escorregamentos, os estudos que a discutem como indicador destes processos são escassos. O sensoriamento remoto, por meio dos índices de vegetação, apresenta potencial ainda não explorado para subsidiar os estudos entre vegetação e escorregamentos. Assim, o objetivo deste trabalho é avaliar a vegetação como indicador de áreas suscetíveis a escorregamentos. A área escolhida para a pesquisa é o trecho da Serra do Mar no município de Caraguatatuba, litoral norte do Estado de São Paulo. A escolha desta área remete ao evento de 1967 no qual houve escorregamentos generalizados nas escarpas da Serra do Mar que culminaram em inúmeras mortes e perdas materiais. Para a realização da pesquisa foram gerados os índices de vegetação NDVI, Simple Ratio, ReNDVI, VIg e PSRI. Os índices de vegetação foram correlacionados às cicatrizes de escorregamentos e com os seguintes parâmetros topográficos: declividade, hipsometria, orientação de vertentes e curvatura em planta. Para isso foi utilizada a distribuição da razão de área afetada por cicatrizes em cada índice de vegetação (Vcic). Os resultados demonstraram que os índices de vegetação foram eficientes ao identificar as áreas com cicatrizes além de aludir que a maior densidade de vegetação visualizada pelo NDVI e, o menor estresse hídrico indicado pelo ReNDVI, podem ter refletido fatores controladores dos escorregamentos que podem ou não estar associados às características da vegetação. Para os índices de vegetação NDVI, Simple Ratio e ReNDVI foi identificada uma relativa independência da variação do Vcic em relação aos parâmetros topográficos ao contrário do VIg e do PSRI. Este novo instrumental pode subsidiar e aprofundar as análises para além da interpretação do relevo por modelos digitais de terreno para estudos de suscetibilidade a escorregamentos contribuindo para aumentar a acurácia dos resultados. / Although the vegetation is considered a landslides factor controller, there are few studies that discuss it as an indicator of these processes. Remote sensing from vegetation indexes has been appointed as under explored to support the studies about vegetation and landslides. The aim of this study is to evaluate the vegetation as an indicator of landslides susceptible areas. The chosen research area is a section of the Serra do Mar mountain range in Caraguatatuba city, in the northern coast of São Paulo. The choice of this area refers to the 1967 event in which there were widespread landslides on the slopes of the Serra do Mar with numerous deaths and material losses. The following vegetation indexes were generated: NDVI, Simple Ratio, ReNDVI, VIg and PRSI. Vegetation indexes were correlated with the landslide scars and with the topographic parameters: slope angle, elevation, slope aspect and curvature in plan. For this reason it was used the distribution of the area affected by scars in each vegetation index (Vcic). The results showed that the vegetation indexes were effective in identifying areas with landslide scars, as well as alluding to the highest density of vegetation recognized from NDVI and the lower water stress indicated by ReNDVI may have reflected as controlling factors of landslides that may or may not be associated with the characteristics of the vegetation. It was noticed for the vegetation indexes NDVI, Simple Ratio and ReNDVI a relative independence of Vcic variation in relation between topographic parameters unlike VIg and PRSI.

Uma das alternativas bastante abordada na literatura para a melhoria do gerenciamento da adubação nitrogenada nas culturas é o sensoriamento remoto, tendo destaque a utilização de sensores espectrais na região do visível e infravermelho. Neste trabalho, buscou-se estabelecer as relações existentes entre variações no teor foliar de nitrogênio (TFN) e a resposta espectral da folha de cana-de-açúcar, utilizando um sensor hiperespectral, com avaliações em três áreas experimentais do estado de São Paulo, com diferentes solos e variedades. Cada experimento foi alocado em blocos ao acaso, com parcelas subdividas e quatro repetições. Foram aplicadas doses de 0, 50, 100 e 150 kg de nitrogênio por hectare. A análise espectral foi realizada na folha \"+1\" em laboratório, sendo coletadas 10 folhas por subparcela, estas foram posteriormente submetidas a análise química para o TFN. Observou-se que existe correlação significativa entre o TFN e as variações na resposta espectral da cana-de-açúcar, sendo que a região do verde e de transição entre o vermelho e o infravermelho próximo (\"red-edge\") foram as mais consistentes e estáveis entre as áreas em estudo e safras avaliadas. A análise de componentes principais permitiu reforçar estes resultados, uma vez que as pontuações (\"scores\") dos componentes que apresentaram correlações significativas com o TFN, tiveram maiores pesos (\"loadings\") nas regiões espectrais citadas anteriormente. A partir das curvas espectrais foram também realizados os cálculos dos índices de vegetação já descritos em literatura, e estes submetidos a análise de regressão simples para predição do TFN, sendo os modelos calibrados com dados da safra 2012/13 e validados com os dados da safra 2013/14. Índices espectrais calculados com a combinação dos comprimentos de onda do verde e/ou \"red-edge\" com comprimentos de onda do infravermelho próximo tiveram bom desempenho na fase de validação, sendo que os cinco mais estáveis foram os índices BNi (500, 705 e 750 nm), GNDVI (550 e 780 nm), NDRE (790 e 720 nm), RI-1db (735 e 720 nm) e VOGa (740 e 720 nm). A variedade SP 81 3250 foi cultivada nas três áreas experimentais, o que permitiu a comparação do potencial de modelos calibrados por área, com um modelo generalista para uma mesma variedade cultivada em diferentes condições edáficas. Observou-se que embora o modelo generalista apresente parâmetros estatísticos significativos, existe redução expressiva da sensibilidade de predição quando comparado aos modelos calibrados por área experimental. Empregou-se também nesta pesquisa a análise de regressão linear múltipla por \"stepwise\" (RLMS) que gerou modelos com boa precisão na estimativa do TFN, mesmo quando calibrados por área experimental, independentes da variedade, utilizando de 5 a 6 comprimentos de onda. Concluímos com a presente pesquisa que comprimentos de onda específicos estão associados a variação do TFN em cana-de-açúcar, e estes são reportados na região do verde (próximos a 550 nm) e na região de transição entre os comprimentos de onda do vermelho e infravermelho próximo (680 a 720 nm). Apesar da baixa correlação entre a região do infravermelho próximo com o TFN, índices de vegetação calculados a partir destes comprimentos de onda ou a inserção destes na geração de modelos lineares foram importantes para melhorar a precisão da predição. / An alternative method, quite cited in literature to improve nitrogen fertilization management on crops is the remote sensing, highlighted with the use of spectral sensors in the visible and infrared region. In this work, we sought to establish the relationship between variations in leaf nitrogen content and the spectral response of sugarcane leaf using a hyperspectral sensor, with assessments in three experimental areas of São Paulo state, Brazil, with evaluations in different soils and varieties. Each experimental area was allocated in randomized block, with splitted plots and four repetition, hence, receiving doses of 0, 50, 100 and 150 kg of nitrogen per hectare. Spectral analysis was performed on the \"+1\" leaf in laboratory; we collected 10 leaves per subplots; which were subsequently subjected to chemical analysis to leaf nitrogen content determination. We observed a significant correlation between leaf nitrogen content and variations in sugarcane spectral response, we noticed that the region of the green light and red-edge were the most consistent and stable among the studied area and the crop seasons evaluated. The principal component analysis allowed to reinforce these results, since that the scores for principal components showed significant correlations with the leaf nitrogen content, had higher loadings values for the previous spectral regions mentioned. From the spectral curves were also performed calculations of spectral indices previously described in literature, being these submitted to simple regression analysis to direct prediction of leaf nitrogen content. The models were calibrated with 2012/13 and validated with 2013/14 crop season data. Spectral indices that were calculated with green and/or red-edge, combined with near-infrared wavelengths performed well in the validation phase, and the five most stable were the BNi (500, 705 and 750 nm), GNDVI (550 and 780 nm), NDRE (790 and 720 nm), IR-1dB (735 and 720 nm) and VOGa (740 and 720 nm). The variety SP 81 3250 was cultured in the three experimental areas, allowing to compare the performance of a specific site model with a general model for the same variety growing on different soil conditions. Although the general model presents meaningful statistical parameters, there is a significant reduction in sensitivity to predict leaf nitrogen content of sugarcane when compared with specific site calibrated models. We also used on this research the stepwise multiple linear regression (SMLR) that generated models with good precision to estimate the leaf nitrogen content, even when models are calibrated for an experimental area, regardless of spectral differences between varieties, using 5 to 6 wavelengths. This study shows that specific wavelengths are associated with variation in leaf nitrogen content of sugarcane, and these are reported in the region of green (near to 550 nm) and red-edge (680 to 720nm). Despite the low correlation observed between the infrared wavelengths to the leaf nitrogen content of sugarcane, vegetation indices calculated from these wavelengths, or its insertion on linear models generation were important to improve prediction accuracy.

Apesar de a vegetação ser considerada fator controlador de escorregamentos, os estudos que a discutem como indicador destes processos são escassos. O sensoriamento remoto, por meio dos índices de vegetação, apresenta potencial ainda não explorado para subsidiar os estudos entre vegetação e escorregamentos. Assim, o objetivo deste trabalho é avaliar a vegetação como indicador de áreas suscetíveis a escorregamentos. A área escolhida para a pesquisa é o trecho da Serra do Mar no município de Caraguatatuba, litoral norte do Estado de São Paulo. A escolha desta área remete ao evento de 1967 no qual houve escorregamentos generalizados nas escarpas da Serra do Mar que culminaram em inúmeras mortes e perdas materiais. Para a realização da pesquisa foram gerados os índices de vegetação NDVI, Simple Ratio, ReNDVI, VIg e PSRI. Os índices de vegetação foram correlacionados às cicatrizes de escorregamentos e com os seguintes parâmetros topográficos: declividade, hipsometria, orientação de vertentes e curvatura em planta. Para isso foi utilizada a distribuição da razão de área afetada por cicatrizes em cada índice de vegetação (Vcic). Os resultados demonstraram que os índices de vegetação foram eficientes ao identificar as áreas com cicatrizes além de aludir que a maior densidade de vegetação visualizada pelo NDVI e, o menor estresse hídrico indicado pelo ReNDVI, podem ter refletido fatores controladores dos escorregamentos que podem ou não estar associados às características da vegetação. Para os índices de vegetação NDVI, Simple Ratio e ReNDVI foi identificada uma relativa independência da variação do Vcic em relação aos parâmetros topográficos ao contrário do VIg e do PSRI. Este novo instrumental pode subsidiar e aprofundar as análises para além da interpretação do relevo por modelos digitais de terreno para estudos de suscetibilidade a escorregamentos contribuindo para aumentar a acurácia dos resultados. / Although the vegetation is considered a landslides factor controller, there are few studies that discuss it as an indicator of these processes. Remote sensing from vegetation indexes has been appointed as under explored to support the studies about vegetation and landslides. The aim of this study is to evaluate the vegetation as an indicator of landslides susceptible areas. The chosen research area is a section of the Serra do Mar mountain range in Caraguatatuba city, in the northern coast of São Paulo. The choice of this area refers to the 1967 event in which there were widespread landslides on the slopes of the Serra do Mar with numerous deaths and material losses. The following vegetation indexes were generated: NDVI, Simple Ratio, ReNDVI, VIg and PRSI. Vegetation indexes were correlated with the landslide scars and with the topographic parameters: slope angle, elevation, slope aspect and curvature in plan. For this reason it was used the distribution of the area affected by scars in each vegetation index (Vcic). The results showed that the vegetation indexes were effective in identifying areas with landslide scars, as well as alluding to the highest density of vegetation recognized from NDVI and the lower water stress indicated by ReNDVI may have reflected as controlling factors of landslides that may or may not be associated with the characteristics of the vegetation. It was noticed for the vegetation indexes NDVI, Simple Ratio and ReNDVI a relative independence of Vcic variation in relation between topographic parameters unlike VIg and PRSI.

Made available in DSpace on 2017-07-10T19:24:23Z (GMT). No. of bitstreams: 1 Henrique_ Felipetto fevereiro 2016.pdf: 7587840 bytes, checksum: f69598c86208aad67f665e687c190146 (MD5) Previous issue date: 2016-01-22 / The use of orbital remote sensing (RS) techniques has come out as a tool for decision making in precision agriculture and has been intensified through time. However, sometimes the high cost of this technology, as well as low temporal and spatial resolution are unfeasible with such technique mainly in small areas. Thus, this study aimed at analyzing thoroughtly the use of an unmanned aerial vehicle UAV, at low cost of development and establishment to determine the green indices of crambe crop in the western region of Paraná. The applied methodology developed up from the purchase of equipment until the UAV setting to determine RNIR NDVI and SAVI green indices, based on the images produced by the equipment. It was carried out a comparative with terrestrial to validate data with an active GreenSeeker sensor and a passive Spectroradiometer. As a result, three indices of vegetation for the three sensors were created and statistically compared using Spearman's correlation coefficient at 5% significance level. The studied equipment showed some satisfactory performance and there was no anomaly, since it followed the National Civil Aviation Agency (ANAC) safety standards. According to the economic viability, it was also positive since the setting and development costs did not exceed 10% value of the most part of the equipment currently sold in Brazil. According to the index values generated by sensors entrained in the UAV, it was possible to determine each stage of cranberry plant development, totaling seven flights during it scropcycle. When the passive sensor coupled to the UAV was compared to active field sensors GreenSeeker and Spectroradiometer, the UAV showed a good performance to determine RNIR, NDVI and SAVI indices. Consequently, there was a significant correlation at 5%level only at haying period, which corresponded to blooming and graining start of crambe, for all indices / O uso de técnicas de sensoriamento remoto (SR) orbital como ferramenta de auxílio à tomada de decisão, na agricultura de precisão, vem se intensificando nos últimos tempos. Entretanto, os altos custos dessa tecnologia e a baixa resolução temporal e espacial acabam por vezes inviabilizando tal técnica, sobretudo em pequenas áreas. O objetivo do estudo foi fazer uma análise detalhada da utilização de um veículo aéreo não tripulado (VANT) de baixo custo de desenvolvimento e implantação, na determinação de índices de vegetação, da cultura de crambe, na região oeste do Paraná. Metodologicamente, o estudo se desenvolveu desde a compra dos equipamentos para a montagem do VANT até a determinação dos índices de vegetação RNIR, NDVI e SAVI, a partir das imagens geradas pelo equipamento. Para a validação dos dados, foi realizado um comparativo com sensores terrestres, sendo um sensor ativo Greenseeker e um sensor passivo Espectroradiômetro. Assim, os três índices de vegetação foram gerados para os três sensores e comparados estatisticamente pelo coeficiente de correlação de Spearman a nível de significância de 5%. O equipamento montado para este estudo apresentou um desempenho satisfatório sem apresentar nenhuma anomalia e atendendo às normas de segurança da ANAC. Do ponto de vista da viabilidade econômica, o resultado também foi positivo, uma vez que os custos de montagem e desenvolvimento não ultrapassaram 10% do valor de grande parte dos equipamentos comercializados atualmente no Brasil. A partir dos valores dos índices gerados pelos sensores embarcados no VANT, foi possível determinar cada etapa do desenvolvimento da planta do crambe, perfazendo sete voos durante o ciclo do cultivo. Quando comparados os sensores passivos acoplados ao VANT com os sensores de campo ativo Greenseeker e passivo Espectroradiômetro, o VANT apresentou um bom desempenho na determinação dos índices RNIR, NDVI e SAVI; entretanto, com correlação significativa ao nível de 5% somente na fase de fenologia correspondente ao florescimento e início da granação do crambe, para todos os índices.

Made available in DSpace on 2017-05-12T14:47:39Z (GMT). No. of bitstreams: 1 Henrique_ Felipetto fevereiro 2016.pdf: 7587840 bytes, checksum: f69598c86208aad67f665e687c190146 (MD5) Previous issue date: 2016-01-22 / The use of orbital remote sensing (RS) techniques has come out as a tool for decision making in precision agriculture and has been intensified through time. However, sometimes the high cost of this technology, as well as low temporal and spatial resolution are unfeasible with such technique mainly in small areas. Thus, this study aimed at analyzing thoroughtly the use of an unmanned aerial vehicle UAV, at low cost of development and establishment to determine the green indices of crambe crop in the western region of Paraná. The applied methodology developed up from the purchase of equipment until the UAV setting to determine RNIR NDVI and SAVI green indices, based on the images produced by the equipment. It was carried out a comparative with terrestrial to validate data with an active GreenSeeker sensor and a passive Spectroradiometer. As a result, three indices of vegetation for the three sensors were created and statistically compared using Spearman's correlation coefficient at 5% significance level. The studied equipment showed some satisfactory performance and there was no anomaly, since it followed the National Civil Aviation Agency (ANAC) safety standards. According to the economic viability, it was also positive since the setting and development costs did not exceed 10% value of the most part of the equipment currently sold in Brazil. According to the index values generated by sensors entrained in the UAV, it was possible to determine each stage of cranberry plant development, totaling seven flights during it scropcycle. When the passive sensor coupled to the UAV was compared to active field sensors GreenSeeker and Spectroradiometer, the UAV showed a good performance to determine RNIR, NDVI and SAVI indices. Consequently, there was a significant correlation at 5%level only at haying period, which corresponded to blooming and graining start of crambe, for all indices / O uso de técnicas de sensoriamento remoto (SR) orbital como ferramenta de auxílio à tomada de decisão, na agricultura de precisão, vem se intensificando nos últimos tempos. Entretanto, os altos custos dessa tecnologia e a baixa resolução temporal e espacial acabam por vezes inviabilizando tal técnica, sobretudo em pequenas áreas. O objetivo do estudo foi fazer uma análise detalhada da utilização de um veículo aéreo não tripulado (VANT) de baixo custo de desenvolvimento e implantação, na determinação de índices de vegetação, da cultura de crambe, na região oeste do Paraná. Metodologicamente, o estudo se desenvolveu desde a compra dos equipamentos para a montagem do VANT até a determinação dos índices de vegetação RNIR, NDVI e SAVI, a partir das imagens geradas pelo equipamento. Para a validação dos dados, foi realizado um comparativo com sensores terrestres, sendo um sensor ativo Greenseeker e um sensor passivo Espectroradiômetro. Assim, os três índices de vegetação foram gerados para os três sensores e comparados estatisticamente pelo coeficiente de correlação de Spearman a nível de significância de 5%. O equipamento montado para este estudo apresentou um desempenho satisfatório sem apresentar nenhuma anomalia e atendendo às normas de segurança da ANAC. Do ponto de vista da viabilidade econômica, o resultado também foi positivo, uma vez que os custos de montagem e desenvolvimento não ultrapassaram 10% do valor de grande parte dos equipamentos comercializados atualmente no Brasil. A partir dos valores dos índices gerados pelos sensores embarcados no VANT, foi possível determinar cada etapa do desenvolvimento da planta do crambe, perfazendo sete voos durante o ciclo do cultivo. Quando comparados os sensores passivos acoplados ao VANT com os sensores de campo ativo Greenseeker e passivo Espectroradiômetro, o VANT apresentou um bom desempenho na determinação dos índices RNIR, NDVI e SAVI; entretanto, com correlação significativa ao nível de 5% somente na fase de fenologia correspondente ao florescimento e início da granação do crambe, para todos os índices.

Near infrared reflectance spectroscopy (NIRS) and geostatistical techniques were used to predict diet quality of sub-Saharan African (SSA) livestock, and to create cokriged estimated diet quality maps for cattle across a landscape. Rations of native vegetation were stall-fed to cattle (Bos indicus), sheep (Ovis aries), and goats (Capra hircus) to generate diet-fecal pair data. Trials were conducted in Ethiopia, Kenya, Uganda, Tanzania, and Ghana. Historical data from Ethiopia, Nigeria, and Niger were included. Diet samples were analyzed for crude protein (CP%), and digestible organic matter (DOM%), while feces were scanned for NIR spectra. NIRS equations were developed from data using modified partial least square (MPLS) regression. Coefficients of determination (R2) of CP for cattle, sheep, and goats were 0.92, 0.95, and 0.97, with corresponding standard errors of calibration (SEC) being 0.90, 0.79, and 0.80, respectively. Standard errors of cross validation (SECV) for CP were 1.12%, 1.08%, and 1.03% for cattle, sheep, and goats, respectively. R2 and SEC values for DOM were 0.88, 0.94, 0.94 and 2.82%, 1.68%, and 2.65%, for cattle, sheep, and goats, respectively. Corresponding SECV values for DOM were 3.26%, 2.07%, and 3.30%, respectively. The statistics reported were within the acceptable limits for NIRS calibrations. The results indicate that dietary CP and DOM of free-ranging SSA livestock can be predicted with the same precision as that of conventional wet chemistry methods. The cattle equation was used to predict cattle fecal samples collected, from February to August 2000, from selected households located within the northern Ghana savanna. The predicted CP% and DOM% were used with Normalized Differential Vegetation Index (NDVI) data, and cokriging technique to create diet quality maps for March and July 2000 for the northern Ghana savanna. Cross validation results indicated a moderate capability of cokriging to estimate predicted CP% for March (r2 = 0.687, SEp = 1.736) and July (r2 = 0.513, SEp = 1.558). Cokriged-estimated DOM value for July was above average (r2 = 0.584, SEp = 3.611), while March DOM% estimation was rather poor (r2 = 0.132, SEp = 3.891). The techniques of cokriging and creation of diet quality maps were moderately successful in this study.

Near infrared reflectance spectroscopy (NIRS) and geostatistical techniques were used to predict diet quality of sub-Saharan African (SSA) livestock, and to create cokriged estimated diet quality maps for cattle across a landscape. Rations of native vegetation were stall-fed to cattle (Bos indicus), sheep (Ovis aries), and goats (Capra hircus) to generate diet-fecal pair data. Trials were conducted in Ethiopia, Kenya, Uganda, Tanzania, and Ghana. Historical data from Ethiopia, Nigeria, and Niger were included. Diet samples were analyzed for crude protein (CP%), and digestible organic matter (DOM%), while feces were scanned for NIR spectra. NIRS equations were developed from data using modified partial least square (MPLS) regression. Coefficients of determination (R2) of CP for cattle, sheep, and goats were 0.92, 0.95, and 0.97, with corresponding standard errors of calibration (SEC) being 0.90, 0.79, and 0.80, respectively. Standard errors of cross validation (SECV) for CP were 1.12%, 1.08%, and 1.03% for cattle, sheep, and goats, respectively. R2 and SEC values for DOM were 0.88, 0.94, 0.94 and 2.82%, 1.68%, and 2.65%, for cattle, sheep, and goats, respectively. Corresponding SECV values for DOM were 3.26%, 2.07%, and 3.30%, respectively. The statistics reported were within the acceptable limits for NIRS calibrations. The results indicate that dietary CP and DOM of free-ranging SSA livestock can be predicted with the same precision as that of conventional wet chemistry methods. The cattle equation was used to predict cattle fecal samples collected, from February to August 2000, from selected households located within the northern Ghana savanna. The predicted CP% and DOM% were used with Normalized Differential Vegetation Index (NDVI) data, and cokriging technique to create diet quality maps for March and July 2000 for the northern Ghana savanna. Cross validation results indicated a moderate capability of cokriging to estimate predicted CP% for March (r2 = 0.687, SEp = 1.736) and July (r2 = 0.513, SEp = 1.558). Cokriged-estimated DOM value for July was above average (r2 = 0.584, SEp = 3.611), while March DOM% estimation was rather poor (r2 = 0.132, SEp = 3.891). The techniques of cokriging and creation of diet quality maps were moderately successful in this study.

Landsat imagery has been used successfully to assess burn severity and monitor post-fire forest structure in a variety of ecosystems, but to date there are few documented studies on its application in the eastern deciduous forests of the eastern United States. The occurrence of a wildfire in the Daniel Boone National Forest in2010 provided a rare opportunity for research into the use of Landsat data for assessing burn severity and its ecological effects. We used differenced normalized burn ratio (∆NBR) to quantify burn severity. The ∆NBR based burn severity classification had 70% agreement with a qualitative ground-based burn severity assessment. We also examined the relationship between the presence of an invasive species (Paulownia tomentosa), and our assessment of burn severity, where we found a weak but statistically significant relationship (adj R2 0.13, p<0.0001). We also examined the relationship between the normalized difference vegetation index (NDVI) and forest structure measurements. The relationship between NDVI and basal area was strongly and significantly related (adj R2 0.41, p<0.0001). The relationship of NDVI with stem density was weak but significant (adj R2 0.23. p=0.004). These results indicate that data from Landsat imagery have great potential for quantifying burn severity, identifying potential hotspots for invasive species, and assessing post fire forest structure in the eastern deciduous forest.