The monitoring of continental and global scale net primary production remains a major focus of satellite-based remote sensing. Potential benefits which follow are diverse and include contributions to, and improved scientific understanding of, ecological systems, rangeland management, famine warning, agricultural commodity trading, and the study of global climate change.A NOAA-AVHRR data set containing monthly observations of green vegetation cover over a ten year period was acquired and analysed, to extract information on seasonal conditions. The data were supplied as a vegetation index, commonly known as the Normalised Difference Vegetation Index (NDVI), with a spatial resolution of approximately five km. The data set was acquired from three different satellites, and calibration problems were known to exist. A new technique was developed to estimate, and subsequently remove, the calibration bias present in the data.Monthly rainfall measurements were used as surrogate ground truth to validate the NDVI data. For regions of native vegetation, linear models relating NDVI to previous rainfall were derived, using transfer function techniques in common use in systems engineering. The models demonstrate that, in mid-latitude regions, the NDVI is a linear function of rainfall recorded over the preceding seven or eight months.Annual summaries of the image data were developed to highlight the amount and timing of plant growth. Three fundamental questions were posed as an aid to the development of the summary technique: where, when and how much? These summaries highlight the extraordinary spatial and temporal variations in plant growth, and hence rainfall, over much of Western Australia each year.Standard analysis techniques used in time series analysis, such as classical decomposition, were successfully applied to the analysis of NDVI time series. These techniques highlighted ++ / structural differences in the image data, due to land use, climatic factors and vegetation type.Overall, the results of the research undertaken in this study, using NOAA-AVHRR data in Western Australia, demonstrate that vegetation indices acquired from satellite platforms can be used to monitor continental scale seasonal conditions in an effective manner. As a consequence of these results, further research using this type of data is proposed in rangeland management and climate change modelling.

Evapotranspiration (ET) is a significant hydrological process. It can be studied and estimated using remote sensing based methods at multiple spatial and temporal scales. Most commonly and widely used remote sensing based methods to estimate actual evapotranspiration (AET) are a) methods based on energy balance equations, b) vegetation coefficient based method and c) contextual methods. These three methods require reflectance and land surface temperature (LST) data measured at optical and thermal portion of the electromagnetic spectrum. However, these data are available only for clear sky conditions and fail to be retrieved under overcast conditions creating gaps in the data, which result in discontinuous of AET product. Moreover, energy balance equation based methods and evaporative fraction (EF) based contextual methods are difficult to apply over overcast conditions. In this context, vegetation coefficient based (Tasumi et al., 2005; Allen et al., 2005) and microwave remote sensing based methods can be applied under cloudy sky conditions (Sun et al., 2012), since microwave radiations can penetrate through clouds, but these data are available at coarse resolution. In the vegetation coefficient method temporal upscaling can be avoided. Therefore in this research vegetation coefficient based method is employed over Cauvery basin to estimate daily AET for clear and cloudy sky conditions. Required critical variables for this method such as reference evapotranspiration (ETo) and vegetation coefficients are obtained using LST and optical vegetation indices for all sky conditions. In this study, all sky conditions refer to both clear and cloudy sky conditions. Most important variable for estimation of ETo using radiation and temperature based models is air temperature (Ta). In this study, for better accuracy of Ta, two satellite based approaches namely, Temperature Vegetation Index (TVX) and Advance Statistical Approaches (ASA) were evaluated. In the TVX approach, in addition to traditional Normalized Difference Vegetation Index (NDVI), other vegetation indices such as Enhanced Vegetation Index (EVI) and Global Vegetation Moisture Index (GVMI) were also examined. In case of ASA, bootstrap technique was used to generate calibration and validation samples and Levenberg Marquardt algorithm was used to find the solution of the models. The better of the Ta results obtained out of these two approaches were employed in the ETo models and are referred as Ta based ETo models. Instead of Ta, processed LST data obtained directly from the satellite (Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS)) was applied in the ETo models and these are referred as LST based ETo models. These Ta and LST based Hargreaves-Samani (H-S), Makkink (Makk) and Penman Monteith Temperature (PMT) models were evaluated by comparing with the FAO56 PM model. Additionally, simple LST based equation (SLBE) proposed by Rivas et al. (2004) was also examined. Required solar radiation (Rs) data for ETo estimation was obtained from Kalpana1/VHRR satellite data. Results implied that, Ta based PMT model performed better than the Ta based H-S, Makk and SLBE with less RMSE, MAPE and MBE values for all land cover classes and for various climatic regions for clear sky conditions. LST based H-S, PMT, Makk and Ta based Makkink advection models predominantly overestimated ETo for the study region. In the case of TVX approach, to estimate maximum Ta (Tmax), GVMI performed better than NDVI and EVI. Nevertheless, TVX approach poorly estimated Tmax in comparison with statistical approach. ASA performed better for both Tmax and minimum Ta. This study demonstrates the applicability of satellite based Ta and ETo models by considering very few variables for clear sky conditions. Spatially distributed vegetation coefficients (Kv) data with high temporal resolution is another important variable in vegetation coefficient method for daily AET estimation and also it is in demand for crop condition assessment, irrigation scheduling, etc. But available Kv models application hinders because of two main reasons i.e 1) Spectral reflectance based Kv accounts only for transpiration factor but not evaporation, which fails to account for total AET. 2) Required optical spectral reflectances are available only during clear sky conditions, which creates gaps in the Kv data. Hence there is a necessity of a model which accounts for both transpiration and evaporation factors and also gap filling method, which produces accurate continuous quantification of Kv values. Therefore, different combinations of EVI, GVMI and temperature vegetation dryness index (TVDI) have been employed in linear and non linear regression techniques to obtain best model. This best Kv model had been compared with Guershman et al. (2009) Kv model. To fill the gaps in the data, initially, temporal fitting of Kv values have been examined using Savitsky-Goley (SG) filter for three years of data (2012 to 2014), but this fails when sufficient high quality Kv values were unavailable. In this regard, three gap filling techniques namely regression, Artificial Neural Networks (ANNs) and interpolation techniques have been analyzed. Microwave polarization difference index (MPDI) has been employed in ANN technique to estimate Kv values under cloudy sky conditions. The results revealed that the combination of GVMI and TVDI using linear regression technique performed better than other combinations and also yielded better results than Guershman et al. (2009) Kv model. Furthermore, the results indicated that SG filter can be used for temporal fitting and for filling the gaps, regression technique can be used as it performed better than other techniques for Berambadi station. Land Surface Temperature (LST) with high spatiotemporal resolution is required in the estimation of ETo to obtain AET. MODIS is one of the most commonly used sensors owing to its high spatial and temporal availability over the globe, but is incapable of providing LST data under cloudy conditions, resulting in gaps in the data. In contrast, microwave measurements have a capability to penetrate under clouds. The current study proposes a methodology by exploring this property to predict high spatiotemporal resolution LST under cloudy conditions during daytime and night time without employing in-situ LST measurements. To achieve this, ANN based models were employed for different land cover classes, utilizing MPDI at finer resolution with ancillary data. MPDI was derived using resampled (from 0.250 to 1 km) brightness temperatures (Tb) at 36.5 GHz channel of dual polarization from Advance Microwave Scanning Radiometer (AMSR)-Earth Observing System and AMSR2 sensors. The proposed methodology was quantitatively evaluated through three performance measures namely correlation coefficient (r), Nash Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE). Results revealed that during daytime, AMSR-E(AMSR2) derived LST under clear sky conditions corresponds well with MODIS LST resulting in values of r ranging from 0.76(0.78) to 0.90(0.96), RMSE from 1.76(1.86) K to 4.34(4.00) K and NSE from 0.58(0.61) to 0.81(0.90) for different land cover classes. For night time, r values ranged from 0.76(0.56) to 0.87(0.90), RMSE from 1.71(1.70) K to 2.43(2.12) K and NSE from 0.43 (0.28) to 0.80(0.81) for different land cover classes. RMSE values found between predicted LST and MODIS LST during daytime under clear sky conditions were within acceptable limits. Under cloudy conditions, results of microwave derived LST were evaluated with Ta which indicated that the approach performed well with RMSE values lesser than the results obtained under clear sky conditions for land cover classes for both day and nighttimes. These predicted LSTs can be applied for the estimation of soil moisture in hydrological studies, in climate studies, ecology, urban climate and environmental studies, etc. AET was estimated for all sky conditions using vegetation coefficient method. Essential parameter ETo under cloudy conditions was estimated using LST and Ta based PMT and H-S models and required solar radiation (Rs) in these two models estimated using equation proposed by Samani (2000). In this equation it was found that the differences between LSTmax or Tmax and LSTmin or Tmin could able to capture the variations due to cloudy sky conditions and hence can be used for estimating ETo under cloudy sky conditions. Results revealed that the estimated Rs correlated well with observed Rs for Berambadi station under cloudy conditions for the year 2013. PMT based ETo values were corresponded with observed ETo under cloudy sky condition. The difference between LST and Ta was less during cloudy conditions, therefore LST or Ta can be used as the only input in temperature based PMT model to estimate ETo. AET estimated correlated well with the observed AET values for clear and cloudy sky conditions. In addition, AET estimated using vegetation coefficient method was compared with two source energy balance (TSEB) method developed by Nishida et al. (2003) under clear sky conditions. It was found that the improved vegetation coefficient method performed better than the TSEB method for Berambadi station. Other microwave vegetation indices such as Microwave Vegetation Indices (MVIs) and Emissivity Difference Vegetation Index (EDVI) are available in literature. Therefore in this study, MVIs are used to predict LST under cloudy conditions using proposed methodology to check whether the MVIs could yield better LST values. Results showed that MPDI performed better than MVIs to predict LST under cloudy sky conditions. Furthermore, MPDI obtained using dual polarizations of 37 GHz channel Tb has advantage of having fine spatial resolution compared to MVIs, as it requires Tb of 19 GHz in addition to Tb of 37 GHz channel which is of coarse resolution and therefore uncertainties resulting from re-sampling technique can be minimized. x

Student Number : 0310612G - MSc research report - School of Geography, Archaeology and Environmental Studies - Faculty of Science / Wildfire prediction and management is an issue of safety and security for many rural communities in South Africa. Wildfire prediction and early warning systems can assist in saving lives, infrastructure and valuable resources in these communities. Timely and accurate data are required for accurate wildfire prediction on both weather conditions and the availability of fuels (vegetation) for wildfires. Wildfires take place in large remote areas in which land use practices and alterations to land cover cannot easily be modelled. Remote sensing offers the opportunity to monitor the extent and changes of land use practices and land cover in these areas. In order for effective fire prediction and management, data on the quantity and state of fuels is required. Traditional methods for detecting vegetation rely on the chlorophyll content and moisture of vegetation for vegetation mapping techniques. Fuels that burn in wildfires are however predominantly dry, and by implication are low in chlorophyll and moisture contents. As a result, these fuels cannot be detected using traditional indices. Other model based methods for determining above ground vegetation biomass using satellite data have been devised. These however require ancillary data, which are unavailable in many rural areas in South Africa. A method is therefore required for the detection and quantification of dry fuels that pose a fire risk. ASTER and MAS (MODIS Airborne Simulator) imagery were obtained for a study area within the Lowveld region of the Limpopo Province, South Africa. Two of the ASTER and two of the MAS images were dated towards the end of the dry season (winter) when the quantity of fuel (dry vegetation) is at its highest. The remaining ASTER image was obtained during the middle of the wet season (summer), against which the results could be tested. In situ measurements of above ground biomass were obtained from a large number of collection points within the image footprints. Normalised Difference Vegetation Index and Transformed Vegetation Index vegetation indices were calculated and tested against the above ground biomass for the dry and wet season images. Spectral response signatures of dry vegetation were evaluated to select wavelengths, which may be effective at detecting dry vegetation as opposed to green vegetation. Ratios were calculated using the respective bandwidths of the ASTER and MAS sensors and tested against above ground biomass to detect dry vegetation. The findings of this study are that it is not feasible, using ASTER and MAS remote sensing data, to estimate brown and green vegetation biomass for wildfire prediction purposes using the datasets and research methodology applied in this study. Correlations between traditional vegetation indices and above ground biomass were weak. Visual trends were noted, however no conclusive evidence could be established from this relationship. The dry vegetation ratios indicated a weak correlation between the values. The removal of background noise, in particular soil reflectance, may result in more effective detection of dry vegetation. Time series analysis of the green vegetation indices might prove a more effective predictor of biomass fuel loads. The issues preventing the frequent and quick transmission of the large data sets required are being solved with the improvements in internet connectivity to many remote areas and will probably be a more viable path to solving this problem in the near future.

A floresta ombrófila mista, representada principalmente pela presença de Araucaria angustifolia possui elevada importância para a região sul do Brasil e o interesse econômico nessa espécie ocasionou uma forte exploração principalmente durante a primeira metade do século XX. O Parque Nacional das Araucárias possui a finalidade de preservar remanescentes de florestas com a presença de Araucaria angustifolia; sendo assim, a compreensão do comportamento da dinâmica fenológica das coberturas florestais é uma forma de auxiliar na gestão e manejo destas áreas. Dessa forma, foi executado a aplicação de uma metodologia baseada em análises harmônicas de séries temporais EVI/MODIS para realizar a caracterização e mapeamento fenológico das diferentes coberturas vegetais presentes no Parque Nacional das Araucárias, por meio desta metodologia foi possível identificar os valores médios de EVI durante toda a série temporal para as diferentes coberturas de uso e ocupação do solo, analisando a relação entre as variações fenológicas com dados de precipitação e temperatura máxima, representando essas variações de amplitude, fase e termo aditivo para a série completa e individualmente para cada ano. Baseado no algoritmo HANTS, aplicou-se a análise harmônica para uma série temporal de dez anos, compreendidas entre os anos de 2006 a 2015. A partir desse processamento foram analisadas as imagens de fase, amplitude e termo aditivo por meio de quatro conjunto de amostras previamente selecionadas, representando as quatro principais coberturas de vegetação presentes no parque. Com o intuito de auxiliar na interpretação visual dos dados, as imagens foram convertidas de RGB para HLS. Uma vez gerados todos os dados, foi possível caracterizar como ocorre a variação dos valores de índices de vegetação ao longo do ano, bem como o período do ano onde acontecem as maiores variações; além de ser possível indicar as áreas onde houve indicativos de mudanças significativas de uso do solo, mudanças ocasionadas por algum evento climático ou pelo próprio desenvolvimento da vegetação. Através dos dados extraídos com a análise harmônica e a identificação das diferentes fenologias gerou-se também uma classificação sobre a série temporal, com o objetivo de identificar as áreas que ainda apresentam remanescentes de Araucaria angustifolia de forma predominante. Por fim, concluiu-se que a aplicação de uma metodologia baseada em séries harmônicas possibilita uma maior compreensão das coberturas florestais presentes nesta unidade de conservação gerando informações úteis para a gestão e possível revisão do plano de manejo. Para alguma aplicação futura, espera-se utilizar esta metodologia em uma série temporal com maior resolução espacial. / The Mixed Coniferous-Broadleaf forest, mainly represented by the presence of Araucaria angustifolia, is highly important to the southern region of Brazil, the economic interest in this species led to a heavy exploration during the first half of the 20th century. The purpose of the Araucárias National Park is to preserve remnants of the forests with great presence of Araucaria angustifolia; therefore, the comprehension of the behavior of the phenological dynamic of the forest covers is a way of assisting the management and handling of these areas. Thereby, the goal is to apply a methodology based on harmonic analysis of EVI / MODIS time series to perform characterization and phenological mapping of the different vegetation covers present in Araucarias National Park; for that, it is intended to identify the medial values of EVI during the whole time series for different types of coverage of soil use and occupation, analyzing the relation between the phenological variations with precipitation data and maximum temperature, representing these variations of amplitude, phase and additive term for a complete series and individually for each year. Based on the HANTS algorithm, the harmonic analysis was applied to a time series of ten years, comprised between 2006 and 2015. Starting from this processing, images of the phase, amplitude and additive term were analyzed by means of four previously selected samples, representing the four main vegetation covers present in the park. In order to assist the visual interpretation of data, the images were converted from RGB to HLS. When all data was generated, it was possible to characterize how the variation in the value of vegetation indices happen throughout the year, as well as the time of the year when the biggest variations occur. Besides, it is possible to indicate the areas with significant changes in the use of soil, or changes caused by climatic events or by the vegetation own development. Through the data extracted with the harmonic analysis and the identification of the different phenologies, a classification was also generated on the time series, in order to identify the areas that still present remnants of Araucaria angustifolia predominantly.Ultimately, it is concluded that the application of a methodology based on the harmonic series enables a better comprehension of the forest covers present in this unity of conservation, generating useful information for the management and possible review of the management plan. For future application, the use of this methodology in a time series with greater spatial resolution is expected.

A floresta ombrófila mista, representada principalmente pela presença de Araucaria angustifolia possui elevada importância para a região sul do Brasil e o interesse econômico nessa espécie ocasionou uma forte exploração principalmente durante a primeira metade do século XX. O Parque Nacional das Araucárias possui a finalidade de preservar remanescentes de florestas com a presença de Araucaria angustifolia; sendo assim, a compreensão do comportamento da dinâmica fenológica das coberturas florestais é uma forma de auxiliar na gestão e manejo destas áreas. Dessa forma, foi executado a aplicação de uma metodologia baseada em análises harmônicas de séries temporais EVI/MODIS para realizar a caracterização e mapeamento fenológico das diferentes coberturas vegetais presentes no Parque Nacional das Araucárias, por meio desta metodologia foi possível identificar os valores médios de EVI durante toda a série temporal para as diferentes coberturas de uso e ocupação do solo, analisando a relação entre as variações fenológicas com dados de precipitação e temperatura máxima, representando essas variações de amplitude, fase e termo aditivo para a série completa e individualmente para cada ano. Baseado no algoritmo HANTS, aplicou-se a análise harmônica para uma série temporal de dez anos, compreendidas entre os anos de 2006 a 2015. A partir desse processamento foram analisadas as imagens de fase, amplitude e termo aditivo por meio de quatro conjunto de amostras previamente selecionadas, representando as quatro principais coberturas de vegetação presentes no parque. Com o intuito de auxiliar na interpretação visual dos dados, as imagens foram convertidas de RGB para HLS. Uma vez gerados todos os dados, foi possível caracterizar como ocorre a variação dos valores de índices de vegetação ao longo do ano, bem como o período do ano onde acontecem as maiores variações; além de ser possível indicar as áreas onde houve indicativos de mudanças significativas de uso do solo, mudanças ocasionadas por algum evento climático ou pelo próprio desenvolvimento da vegetação. Através dos dados extraídos com a análise harmônica e a identificação das diferentes fenologias gerou-se também uma classificação sobre a série temporal, com o objetivo de identificar as áreas que ainda apresentam remanescentes de Araucaria angustifolia de forma predominante. Por fim, concluiu-se que a aplicação de uma metodologia baseada em séries harmônicas possibilita uma maior compreensão das coberturas florestais presentes nesta unidade de conservação gerando informações úteis para a gestão e possível revisão do plano de manejo. Para alguma aplicação futura, espera-se utilizar esta metodologia em uma série temporal com maior resolução espacial. / The Mixed Coniferous-Broadleaf forest, mainly represented by the presence of Araucaria angustifolia, is highly important to the southern region of Brazil, the economic interest in this species led to a heavy exploration during the first half of the 20th century. The purpose of the Araucárias National Park is to preserve remnants of the forests with great presence of Araucaria angustifolia; therefore, the comprehension of the behavior of the phenological dynamic of the forest covers is a way of assisting the management and handling of these areas. Thereby, the goal is to apply a methodology based on harmonic analysis of EVI / MODIS time series to perform characterization and phenological mapping of the different vegetation covers present in Araucarias National Park; for that, it is intended to identify the medial values of EVI during the whole time series for different types of coverage of soil use and occupation, analyzing the relation between the phenological variations with precipitation data and maximum temperature, representing these variations of amplitude, phase and additive term for a complete series and individually for each year. Based on the HANTS algorithm, the harmonic analysis was applied to a time series of ten years, comprised between 2006 and 2015. Starting from this processing, images of the phase, amplitude and additive term were analyzed by means of four previously selected samples, representing the four main vegetation covers present in the park. In order to assist the visual interpretation of data, the images were converted from RGB to HLS. When all data was generated, it was possible to characterize how the variation in the value of vegetation indices happen throughout the year, as well as the time of the year when the biggest variations occur. Besides, it is possible to indicate the areas with significant changes in the use of soil, or changes caused by climatic events or by the vegetation own development. Through the data extracted with the harmonic analysis and the identification of the different phenologies, a classification was also generated on the time series, in order to identify the areas that still present remnants of Araucaria angustifolia predominantly.Ultimately, it is concluded that the application of a methodology based on the harmonic series enables a better comprehension of the forest covers present in this unity of conservation, generating useful information for the management and possible review of the management plan. For future application, the use of this methodology in a time series with greater spatial resolution is expected.

A floresta ombrófila mista, representada principalmente pela presença de Araucaria angustifolia possui elevada importância para a região sul do Brasil e o interesse econômico nessa espécie ocasionou uma forte exploração principalmente durante a primeira metade do século XX. O Parque Nacional das Araucárias possui a finalidade de preservar remanescentes de florestas com a presença de Araucaria angustifolia; sendo assim, a compreensão do comportamento da dinâmica fenológica das coberturas florestais é uma forma de auxiliar na gestão e manejo destas áreas. Dessa forma, foi executado a aplicação de uma metodologia baseada em análises harmônicas de séries temporais EVI/MODIS para realizar a caracterização e mapeamento fenológico das diferentes coberturas vegetais presentes no Parque Nacional das Araucárias, por meio desta metodologia foi possível identificar os valores médios de EVI durante toda a série temporal para as diferentes coberturas de uso e ocupação do solo, analisando a relação entre as variações fenológicas com dados de precipitação e temperatura máxima, representando essas variações de amplitude, fase e termo aditivo para a série completa e individualmente para cada ano. Baseado no algoritmo HANTS, aplicou-se a análise harmônica para uma série temporal de dez anos, compreendidas entre os anos de 2006 a 2015. A partir desse processamento foram analisadas as imagens de fase, amplitude e termo aditivo por meio de quatro conjunto de amostras previamente selecionadas, representando as quatro principais coberturas de vegetação presentes no parque. Com o intuito de auxiliar na interpretação visual dos dados, as imagens foram convertidas de RGB para HLS. Uma vez gerados todos os dados, foi possível caracterizar como ocorre a variação dos valores de índices de vegetação ao longo do ano, bem como o período do ano onde acontecem as maiores variações; além de ser possível indicar as áreas onde houve indicativos de mudanças significativas de uso do solo, mudanças ocasionadas por algum evento climático ou pelo próprio desenvolvimento da vegetação. Através dos dados extraídos com a análise harmônica e a identificação das diferentes fenologias gerou-se também uma classificação sobre a série temporal, com o objetivo de identificar as áreas que ainda apresentam remanescentes de Araucaria angustifolia de forma predominante. Por fim, concluiu-se que a aplicação de uma metodologia baseada em séries harmônicas possibilita uma maior compreensão das coberturas florestais presentes nesta unidade de conservação gerando informações úteis para a gestão e possível revisão do plano de manejo. Para alguma aplicação futura, espera-se utilizar esta metodologia em uma série temporal com maior resolução espacial. / The Mixed Coniferous-Broadleaf forest, mainly represented by the presence of Araucaria angustifolia, is highly important to the southern region of Brazil, the economic interest in this species led to a heavy exploration during the first half of the 20th century. The purpose of the Araucárias National Park is to preserve remnants of the forests with great presence of Araucaria angustifolia; therefore, the comprehension of the behavior of the phenological dynamic of the forest covers is a way of assisting the management and handling of these areas. Thereby, the goal is to apply a methodology based on harmonic analysis of EVI / MODIS time series to perform characterization and phenological mapping of the different vegetation covers present in Araucarias National Park; for that, it is intended to identify the medial values of EVI during the whole time series for different types of coverage of soil use and occupation, analyzing the relation between the phenological variations with precipitation data and maximum temperature, representing these variations of amplitude, phase and additive term for a complete series and individually for each year. Based on the HANTS algorithm, the harmonic analysis was applied to a time series of ten years, comprised between 2006 and 2015. Starting from this processing, images of the phase, amplitude and additive term were analyzed by means of four previously selected samples, representing the four main vegetation covers present in the park. In order to assist the visual interpretation of data, the images were converted from RGB to HLS. When all data was generated, it was possible to characterize how the variation in the value of vegetation indices happen throughout the year, as well as the time of the year when the biggest variations occur. Besides, it is possible to indicate the areas with significant changes in the use of soil, or changes caused by climatic events or by the vegetation own development. Through the data extracted with the harmonic analysis and the identification of the different phenologies, a classification was also generated on the time series, in order to identify the areas that still present remnants of Araucaria angustifolia predominantly.Ultimately, it is concluded that the application of a methodology based on the harmonic series enables a better comprehension of the forest covers present in this unity of conservation, generating useful information for the management and possible review of the management plan. For future application, the use of this methodology in a time series with greater spatial resolution is expected.

Nitrogen (N) is the most critical fertilizer applied nutrient for supporting plant growth. It is a critical part of photosynthesis as a component of chlorophyl, hence it is a key indicator of plant health. In recent years, rapid development of multispectral sensing technology and machine learning (ML) methods make it possible to estimate leaf chemical components such as N for predicting yield spatially and temporally. The objectives of this study were to compare the relationships between canopy reflectance and corn (Zea mays L.) leaf N concentration acquired by two multispectral sensors: red-edge multispectral camera mounted on the Unmanned Aerial Vehicle (UAV) and crop circle ACS-430. Four fertilizer N rates were applied, ranging from deficient to excessivein order to have a broad rangein plant N status. Spectral information was collected at different phenological stages of corn to calculate vegetation indices (VIs) for each stage. Moreover, leaf samples were taken simultaneously to determine N concentration. Different ML methods (Multi-Layer Perceptron (MLP), Support Vector Machines (SVMs), Random Forest regression, Regularized regression models, and Gradient Boosting) were used to estimate leaf N% from VIs and predict yield from VIs. Random Forest Regression was utilized as a feature selection method to choose the best combination of variables for different stages and to interpret the relationships between VIs and corn leaf N concentration and grain yield. The Canopy Chlorophyll Content Index (SCCCI) and Red-edge Ratio Vegetation Index (RERVI) were selected as the most efficient VIs in leaf N estimation and SCCCI, Red-edge chlorophyll index (CIRE), RERVI, Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Vegetation Index (NDVI) were chosen as the most effective VIs in predicting corn grain yield. The results derived from using a red-edge multispectral camera showed that the SCCCI was the most proper index for predicting yield at most of the phenological stages and Gradient Boosting was the best-fitted model to estimate leaf N% with an 80% coefficient of determination. Using a Crop Circle ACS-430 showed that the Support Vector Regression (SVR) model achieved the best performance measures than other models tested in the prediction of leaf N concentration.

Strong tornadoes have impacted the central Appalachian Mountains multiple times in recent years. The topography of this region leads to unique spatial patterns of tornado damage as the tornado vortices pass over ridges in forested areas, and this damage can be detected with vegetation indices derived from remotely sensed imagery. The objectives of this study were to 1) Classify forest damage from the April 19, 2019 EF-3 tornado in Franklin County, VA using remotely-sensed images, 2) Quantify the spatial patterns of forest damage intensity across the path using derived vegetation indices and terrain variables (primarily slope, aspect, elevation, and exposure), and 3) Use regression models to determine if relationships exist among terrain variables along the and forest damage patterns. I generated EVI and NDII vegetation indices from Sentinel-2 imagery and compared the derived damage to the underlying terrain variables. Results revealed that the two vegetation indices were effective for classifying tornado damage, and discrete damage classes aligned well with NWS EF-scale tornado intensity estimations. ANOVA testing suggested that EF-3 equivalent damage was more likely to occur on downslope topography, leeward of the tornado's direction of travel. OLS and geographically weighted regression (GWR) modeling performed poorly, suggesting that an alternative method may be more suitable for modeling, the scale of assessment was inadequate, or that important predictor variables were not captured. Overall, the intensity of the tornado was clearly modified by terrain interactions, and the remote sensing methodology used was effective for reliably identifying and rating damage in forested areas. / Master of Science / Strong tornadoes have impacted the central Appalachian Mountains multiple times in recent years. The topography of this region leads to unique spatial patterns of tornado damage as the tornado vortices pass over ridges in forested areas, and this damage can be detected with vegetation indices derived from remotely sensed imagery. The objectives of this study were to 1) Classify forest damage from the April 19, 2019 EF-3 tornado in Franklin County, VA using remotely-sensed images, 2) Quantify the spatial patterns of forest damage intensity across the path using derived vegetation indices and terrain variables (primarily slope, aspect, elevation, and exposure), and 3) Use regression models to determine if relationships exist among terrain variables along the and forest damage patterns. I generated EVI and NDII vegetation indices from Sentinel-2 imagery and compared the derived damage to the underlying terrain variables. Results revealed that the two vegetation indices were effective for classifying tornado damage, and discrete damage classes aligned well with NWS EF-scale tornado intensity estimations. ANOVA testing suggested that EF-3 equivalent damage was more likely to occur on downslope topography, leeward of the tornado's direction of travel. OLS and geographically weighted regression (GWR) modeling performed poorly, suggesting that an alternative method may be more suitable for modeling, the scale of assessment was inadequate, or that important predictor variables were not captured. Overall, the intensity of the tornado was clearly modified by terrain interactions, and the remote sensing methodology used was effective for reliably identifying and rating damage in forested areas.

Light reflectance from plants can be used as a non-invasive predictor of health and yield for many cropping systems, and has been investigated to a lesser extent with managed turfgrass systems. The frequent agronomic inputs associated with maintaining golf course grasses allow for exceptional stand quality under harsh growing conditions, but often expend resources inefficiently, leading to either stand loss or unnecessary inputs in localized areas. Turfgrass researchers have adopted some basic principles of light reflectance formerly developed for cropping systems, but field radiometric-derived narrow-band algorithms for turfgrass-specific protocols are lacking. Research was conducted to expand the feasibility of using radiometry to detect various turfgrass stressors and improve speed and geographic specificity of turfgrass management. Methods were developed to detect applied turfgrass stress from herbicide five days before visible symptoms developed under normal field growing conditions. Soil volumetric water content was successfully estimated using a water band index of creeping bentgrass canopy reflectance. The spectral reflectance of turfgrass treated with conventional synthetic pigments was characterized and found to erroneously influence plant health interpretation of common vegetation indices because of near infrared interference by such pigments. Finally, reflectance data were used to estimate root zone temperatures and root depth of creeping bentgrass systems using a gradient of wind velocities created with turf fans. Collectively, these studies provide a fundamental understanding of several turfgrass-specific reflectance algorithms and support unique opportunities to detect stresses and more efficiently allocate resources to golf course turf. / Ph. D.

The study was aimed to provide information on local biomass development during crop growth using ground based optical sensors and to incorporate the local crop status to a crop growth simulation model to improve understanding on inherent variability of crop field. The experiment was conducted in a farmer’s field located near Princeton in Caldwell County, Western Kentucky. Data collection on soil, crop and weather variables was carried out in the farm from 2006 December to 2008 October. During this period corn (Zea mays L.) and winter wheat (Triticum sp) were grown in the field. A 450 m long representative transect across the field consisting of 45 locations each separated by 10 m was selected for the study. Soil water content was measured in a biweekly interval during crop growth from these locations. Measurements on crop growth parameters such as plant height, tiller count, biomass and grain yield were able to show spatial variability in crop biomass and grain yield production. Crop reflectance measured at important crop growth stages. Soil water sensing capacitance probe was site specifically calibrated for each soil depth in each location. Various vegetation indices were calculated as proxy variables of crop growth. Inherent soil properties such as soil texture and elevation were found playing a major role in influencing spatial variability in crop yield mainly by affecting soil water storage. Temporal persistence of spatial patterns in soil water storage was not observed. Optimum spatial correlation structure was observed between crop growth parameters and optical sensor measurements collected early in the season and aggregated at 2*2 m2 sampling area. NDVI, soil texture, soil water storage and different crop growth parameters were helpful in explaining the spatial processes that influence grain yield and biomass using state space analysis. DSSAT was fairly sensitive to reflect site specific inputs on soil variability in crop production.