Tillage practices have changed dramatically during the past several decades as agricultural specialists have recognized the unfavorable environmental effects of mechanized tillage. Alternatively, conservation tillage management can mitigate adverse environmental impacts of tillage, such as soil and water degradation. Adoption of conservation tillage has continued to increase since its first introduction, which raises questions of when and where it is practiced. Spatial and temporal specifics of tillage practices form important dimensions for development of effective crop management practices and policies.  Because Landsat has been and will continue to image the Earth globally, it provides opportunities for systematic mapping of crop residue cover (CRC) /tillage practices. Thus, the overall objective of this study is to develop methodologies to improve our ability to monitor crop management across different landscapes in a time-efficient and cost-effective manner using Landsat TM and ETM+ imagery, which is addressed in three separate studies. The first study found that previous efforts to estimate CRC along a continuum using Landsat-based tillage indices were unsuccessful because they neglected the key temporal changes in agricultural surfaces caused by tilling, planting, and crop emergence at the start of the growing season. The first study addressed this difficulty by extracting minimum values of multi-temporal NDTI (Normalized Difference Tillage Index) spectral profiles, designated here as the minNDTI method. The minNDTI improves crop residue estimation along a continuum (R2 = 0.87) as well as tillage classification accuracy (overall accuracy > 90%).   A second study evaluated effectiveness of the minNDTI approach for assessing CRC at multiple locations over several years, and compared minNDTI to hyperspectral tillage index (CAI), and the ASTER tillage index (SINDRI). The minNDTI is effective across four different locations (R2 of 0.56 ~ 0.93). The third study, built upon the second study, addressed the Landsat ETM+ missing data issue, and devised methodologies for producing field-level tillage data at broad scales (multiple counties).  In summary, this research demonstrates that the minNDTI technique is currently the best alternative for monitoring CRC and tillage practices from space, and provides a foundation for monitoring crop residue cover at broad spatial and temporal scales. / Ph. D.

Forests cover a large portion of the global land area and provide critical resources such as timber, food, and medicine in addition to playing a significant role in the global carbon cycle. As such, sustainable forest management practices are required to balance forest economies and climate change mitigation with other non-timber objectives. A key aspect of many sustainable forest management programs is forest monitoring, for which technological and methodological development has led to enhanced forest inventory (EFI) methods, many of which rely on remote sensing data from high-resolution light detection and ranging (LiDAR) and optical imagery. However, to date, current applications of EFI methods have mostly focused on timber attributes with limited research on non-timber attributes or analyses regarding multi-temporal monitoring, method scaling, or method transferability. The objective of this thesis is to expand applications of EFIs in monitoring and analysis through two distinct studies, first evaluating the utility of LiDAR-based EFI methods in multi-temporal silvicultural treatment assessment and secondly in the pre-harvest estimation of merchantable wood and non-merchantable wood left as logging residues. The first study evaluates a process that expands the sampling of fertilization treatment effects on forest stands to the wider treatment area by utilizing paired LiDAR blocks made up of raster cell estimates from a multi-temporal area-based model. Results showed promise for detecting treatment impacts on stand volume, biomass, and height and highlights the potential for the methods to be used as a means to rapidly expand analysis from sample plots to the entire treatment area. The second study focuses on the use of a hybrid area-based and individual tree EFI approach to model merchantable and non-merchantable forest wood volumes while exploring the scalability of these models to harvest blocks and the transferability to additional blocks without prior training. Results from this study indicated that models for both volume attributes are successfully scalable and transferable to harvest blocks. Overall, the research results presented in this thesis demonstrate the potential of enhanced forest inventory methods for the monitoring and assessment of timber attributes, such as wood volume or biomass, as well as alternative attributes, such as stand height, or non-merchantable wood volume, over multiple years. This work further demonstrates the potential for these methods to expand areas of assessment and increase prediction accuracies. / Graduate / 2022-08-17

Landslides are a natural hazard that is expected to increase in the future, due to climate change. In order to keep risk management plans up to date, an efficient inventory method is needed. In previous studies, multi-temporal high-resolution digital elevation models (DEM) produced with LiDAR technology have been used successfully for landslide inventory and monitoring in different parts of the world. The aim of this study has been to discover an inventory method for landslides in Sweden, using two generations of elevation data produced with LiDAR. The analysis was performed in GIS with the creation of a DEM of difference (DoD) and visual comparison as key components. The sites were also verified using Google Earth satellite imagery and aerial photos. The result of the study shows that a functional, efficient method was developed and several potential landslides were found in the three different study areas. The soil characteristics, slope gradient and distance to areas affected by forestry were recorded for all potential landslide sites. Using multi-temporal DEM for landslide inventory is time- and cost efficient, and the results are more accurate compared to traditional inventory techniques. Hopefully the method developed in this study can be used on a larger scale and lead to updated risk management and prevention plans throughout all risk areas for landslides in Sweden. In future studies field work is recommended to verify the potential landslide sites.

Establishing wetland gains and losses, delineating wetland boundaries, and determining their vegetative composition are major challenges that can be improved through remote sensing studies. In this study, we used the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to separate wetlands from uplands in a study of 870 locations on the Virginia Coastal Plain. We used the first five bands from each of two ASTER scenes (6 March 2005 and 16 October 2005), covering the visible to the short-wave infrared region (0.52-2.185υm). We included GIS data layers for soil survey, topography, and presence or absence of water in a logistic regression model that predicted the location of over 78% of the wetlands. While this was slightly less accurate (78% vs. 86%) than current National Wetland Inventory (NWI) aerial photo interpretation procedures of locating wetlands, satellite imagery analysis holds great promise for speeding wetland mapping, lowering costs, and improving update frequency. To estimate wetland vegetation composition classs of the study locations, we generated a Classification and Regression Tree (CART) model and a Multinomial Logistic Regression (logit) model, and compared their accuracy in separating woody wetlands, emergent wetlands and open water. The overall accuracy of the CART model was 73.3%, while the overall accuracy of the logit model was 76.7%. Although the CART producer's accuracy (correct category classification) of the emergent wetlands was higher than the accuracy from the multinomial logit (57.1% vs. 40.7%), we obtained the opposite result for the woody wetland category (68.7% vs. 52.6%). A McNemar test between the two models and NWI maps showed that their accuracies were not statistically different. We conducted a sub-pixel analysis of the ASTER images to establish canopy cover of forested wetlands. The canopy cover ranged from 0 to 225 m2. We used visble-near-infrared ASTER bands, Delta Normalized Difference Vegetation Index, and a Tasselled Cap transformation in an ordinary linear regression (OLS) model. The model achieved an adjusted-R2 of 0.69 and an RMSE of 2.73% when the canopy cover is less than 16%. For higher canopy cover values, the adjusted-R2 was 0.4 and the RMSE was19.79%. Taken together, these findings suggest that satellite remote sensing, in concert with other spatial data, has strong potential for mapping both wetland presence and type. / Ph. D.

[Abstract]: Since early settlement, agriculture has been one of the main industries contributing to the livelihoods of most rural communities in Australia. The wheat grain industry is Australia’s second largest agricultural export commodity, with an average value of $3.5 billion per annum. Climate variability and change, higher input costs, and world commodity markets have put increased pressure on the sustainability of the grain industry. This has lead to an increasing demand for accurate, objective and near real-time crop production information by industry. To generate such production estimates, it is essential to determine crop area planted at the desired spatial and temporal scales. However, such information at regional scale is currently not available in Australia.The aim of this study was to determine broadacre crop area estimates through the use of multi-temporal satellite imagery for major Australian winter crops. Specifically, the objectives were to: (i) assess the ability of a range of approaches to using multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) imagery to estimate total end-of-season winter crop area; (ii) determine the discriminative ability of such remote sensing approaches in estimating planted area for wheat, barley and chickpea within a specific cropping season; (iii) develop and evaluate the methodology for determining the predictability of crop area estimates well before harvest; and (iv) validate the ability of multi-temporal MODIS approaches to determine the pre-harvest and end-of-season winter crop area estimates for different seasons and regions.MODIS enhanced vegetation index (EVI) was used as a surrogate measure for crop canopy health and architecture, for two contiguous shires in the Darling Downs region of Queensland, Australia. Multi-temporal approaches comprising principal component analysis (PCA), harmonic analysis of time series (HANTS), multi-date MODIS EVI during the crop growth period (MEVI), and two curve fitting procedures (CF1, CF2) were derived and applied. These approaches were validated against the traditional single-date approach. Early-season crop area estimates were derived through the development and application of a metric, i.e. accumulation of consecutive 16-day EVI values greater than or equal to 500, at different periods before flowering. Using ground truth data, image classification was conducted by applying supervised (maximum likelihood) and unsupervised (K-means) classification algorithms. The percent correctly classified and kappa coefficient statistics from the error matrix were used to assess pixel-scale accuracy, while shire-scale accuracy was determined using the percent error (PE) statistic. A simple linear regression of actual shire-scale data against predicted data was used to assess accuracy across regions and seasons. Actual shire-scale data was acquired from government statistical reports for the period 2000, 2001, 2003 and 2004 for the Darling Downs, and 2005 and 2006 for the entire Queensland cropping region.Results for 2003 and 2004 showed that multi-temporal HANTS, MEVI, CF1, CF2 and PCA methods achieved high overall accuracies ranging from 85% to 97% to discriminate between crops and non-crops. The accuracies for discriminating between specific crops at pixel scale were less, but still moderate, especially for wheat and barley (lowest at 57%). The HANTS approach had the smallest mean absolute percent error of 27% at shire-scale compared to other multi-temporal approaches. For early-season prediction, the 16-day EVI values greater than or equal to 500 metric showed high accuracy (94% to 98%) at a pixel scale and high R2 (0.96) for predicting total winter crop area planted.The rigour of the HANTS and the 16-day EVI values greater than or equal to 500 approaches was assessed when extrapolating over the entire Queensland cropping region for the 2005 and 2006 season. The combined early-season estimate of July and August produced high accuracy at pixel and regional scales with percent error of 8.6% and 26% below the industry estimates for 2005 and 2006 season, respectively. These satellite-derived crop area estimates were available at least four months before harvest, and deemed that such information will be highly sought after by industry in managing their risk. In discriminating among crops at pixel and regional scale, the HANTS approach showed high accuracy. Specific area estimates for wheat, barley and chickpea were, respectively, 9.9%, -5.2% and 10.9% (for 2005) and -2.8%, -78% and 64% (for 2006). Closer investigation suggested that the higher error in 2006 area estimates for barley and chickpea has emanated from the industry figures collected by the government.Area estimates of total winter crop, wheat, barley and chickpea resulted in R2 values of 0.92, 0.89, 0.82 and 0.52, when contrasted against the actual shire-scale data. A significantly high R2 (0.87) was achieved for total winter crop area estimates in Augusts across all shires for the 2006 season. Furthermore, the HANTS approach showed high accuracy in discriminating cropping area from non-cropping area and highlighted the need for accurate and up-to-date land use maps.This thesis concluded that time-series MODIS EVI imagery can be applied successfully to firstly, determine end-of-season crop area estimates at shire scale. Secondly, capturing canopy green-up through a novel metric (i.e. 16-day EVI values greater than or equal to 500) can be utilised effectively to determine early-season crop area estimates well before harvest. Finally, the extrapolability of these approaches to determine total and specific winter crop area estimates showed good utility across larger areas and seasons. Hence, it is envisaged that this technology is transferable to different regions across Australia. The utility of the remote sensing techniques developed in this study will depend on the risk agri-industry operates at within their decision and operating regimes. Trade-off between risk and value will depend on the accuracy and timing of the disseminated crop production forecast.

O crescimento desordenado das cidades favorece o surgimento de cenários urbanos que não asseguram aos cidadãos necessidades básicas reconhecidas pela Constituição Brasileira. A descoberta e a exploração de petróleo na Bacia de Campos a partir de 1974 fizeram com que o município de Macaé (RJ) sofresse profundas mudanças em sua configuração espacial, acompanhadas de acelerado aumento e concentração populacional. O objeto da presente pesquisa consiste no desenvolvimento de aplicação apoiada por um Sistema de Informação Geográfica, possibilitando mapeamento temático dinâmico, com foco principal no acompanhamento multitemporal do crescimento urbano, localizado na zona litorânea desse município e em suas proximidades, utilizando uma série histórica de imagens, composta por sete épocas, relativas aos anos de 1956, 1966, 1976, 1989, 1999, 2001 e 2004. Desta forma foi realizado processamento digital das imagens aerofotográficas e do sistema Quickbird, com o propósito de validação do potencial do SPRING, com exploração pautada nas etapas de segmentação e classificação supervisionada, sendo obtidos resultados referentes ao crescimento da área urbana do município, dentro de um período de 48 anos, aproximadamente. O SPRING se apresentou como ferramenta importante nesse processo, com potencial utilização em estudos relevantes para a gestão de cidades. Observando-se assim, efetiva possibilidade de aplicação em fases de elaboração de itens do plano diretor e atividades relativas a projetos executivos, tanto em seu planejamento, quanto em sua execução. / The disorderly growth of cities leads to the emergence of urban settings which do not provide citizens with basic needs recognized by the Brazilian Constitution. The discovery and exploitation of oil in the Campos Basin in 1974 made the municipality of Macaé (RJ) suffered profound changes in its spatial configuration, accompanied by accelerated growth and population density. The aim of this research is to develop application supported by a Geographic Information System, enabling dynamic thematic mapping with main focus on multi-temporal accompaniment of urban growth, located in the coastal zone of the municipality and its vicinity by using a series of historical images, composed of seven times, for the years 1956, 1966, 1976, 1989, 1999, 2001 and 2004. Thus, it was done digital processing of aerial photography and Quickbird system images, in order to validate the potential of SPRING, with operation guided through the steps of segmentation and supervised classification, obtaining results for the growth of the urban area council, within a period of 48 years approximately. SPRING is presented as an important tool in this process, with potential use in studies relevant to the management of cities. So, it was observed the possibility of effective implementation in stages of preparation of the master plan items and activities related to executive projects, both in its planning, and in its implementation.

O crescimento desordenado das cidades favorece o surgimento de cenários urbanos que não asseguram aos cidadãos necessidades básicas reconhecidas pela Constituição Brasileira. A descoberta e a exploração de petróleo na Bacia de Campos a partir de 1974 fizeram com que o município de Macaé (RJ) sofresse profundas mudanças em sua configuração espacial, acompanhadas de acelerado aumento e concentração populacional. O objeto da presente pesquisa consiste no desenvolvimento de aplicação apoiada por um Sistema de Informação Geográfica, possibilitando mapeamento temático dinâmico, com foco principal no acompanhamento multitemporal do crescimento urbano, localizado na zona litorânea desse município e em suas proximidades, utilizando uma série histórica de imagens, composta por sete épocas, relativas aos anos de 1956, 1966, 1976, 1989, 1999, 2001 e 2004. Desta forma foi realizado processamento digital das imagens aerofotográficas e do sistema Quickbird, com o propósito de validação do potencial do SPRING, com exploração pautada nas etapas de segmentação e classificação supervisionada, sendo obtidos resultados referentes ao crescimento da área urbana do município, dentro de um período de 48 anos, aproximadamente. O SPRING se apresentou como ferramenta importante nesse processo, com potencial utilização em estudos relevantes para a gestão de cidades. Observando-se assim, efetiva possibilidade de aplicação em fases de elaboração de itens do plano diretor e atividades relativas a projetos executivos, tanto em seu planejamento, quanto em sua execução. / The disorderly growth of cities leads to the emergence of urban settings which do not provide citizens with basic needs recognized by the Brazilian Constitution. The discovery and exploitation of oil in the Campos Basin in 1974 made the municipality of Macaé (RJ) suffered profound changes in its spatial configuration, accompanied by accelerated growth and population density. The aim of this research is to develop application supported by a Geographic Information System, enabling dynamic thematic mapping with main focus on multi-temporal accompaniment of urban growth, located in the coastal zone of the municipality and its vicinity by using a series of historical images, composed of seven times, for the years 1956, 1966, 1976, 1989, 1999, 2001 and 2004. Thus, it was done digital processing of aerial photography and Quickbird system images, in order to validate the potential of SPRING, with operation guided through the steps of segmentation and supervised classification, obtaining results for the growth of the urban area council, within a period of 48 years approximately. SPRING is presented as an important tool in this process, with potential use in studies relevant to the management of cities. So, it was observed the possibility of effective implementation in stages of preparation of the master plan items and activities related to executive projects, both in its planning, and in its implementation.

Snow represents an important resource in mountainous regions. Monitoring its extent and amount is relevant for several applications, such as hydrology, ecology, avalanche monitoring, or hydropower production. However, a correct understanding of the high spatial and temporal variability of snow accumulation, redistribution and ablation processes requires its monitoring in a spatialized and detailed way. Recently, the launch of the Sentinel missions has opened the doors to new approaches that mainly exploit high resolution (HR) data having a spatial detail of few dozens of m. In this thesis, we aimed at exploiting these new sources of information to retrieve important parameters related to the snowmelt processes. In detail, we i) investigated the use of Sentinel-1 Synthetic Aperture Radar (SAR) observations to evaluate snowmelt dynamics in alpine regions, ii) developed a novel approach based on a hierarchical multi-resolution analysis of optical time-series to reconstruct the daily HR snow cover area (SCA), and iii) explored the combination of HR SCA time-series, SAR snowmelt information and other multi-source data to reconstruct a daily HR snow water equivalent (SWE) time-series. In detail, in the first work we analyzed the relationship between the snowmelt phases of a snowpack and the multi-temporal SAR backscattering. We found that the SAR is able to provide useful information about the moistening, ripening and runoff phases. In the second work, we exploited the snow pattern repetition on an inter-annual basis driven by the geomorphological features of a study area to carry out historical analyses. Thus, we took advantage of these repeated patterns to fuse low resolution and HR satellite optical data and set up a gap filling to derive daily HR snow cover area (SCA) time-series. These two research works are the pillars for the last contribution, which aims at combining all these information sources together with both in-situ data and a simple yet robust degree day model that provides an estimate of the potential melting to derive daily HR SWE time-series. These final results have an unprecedented spatial detail, that allows to sample the phenomena linked to the complex snow accumulation, redistribution and ablation processes with the required spatial and temporal resolution. The methodology and the results of each experimental work are illustrated and discussed in detail in the chapters of this thesis, with a look on further research and potential applications.

No description available.

A espacialização de atributos dos solos é necessária com vistas ao planejamento e monitoramento do solo. As imagens do satélite Landsat 5 Thematic Mapper (TM) são utilizadas em estudos relacionados aos recursos naturais por fornecerem informações da superfície das terras em áreas amplas e de difícil acesso. Nesse trabalho objetivou-se gerar uma imagem multitemporal de solo exposto através de imagens de satélite e, com ela, mapear atributos da superfície do solo. A área de estudo é a região de Piracicaba, SP, onde foram selecionadas treze imagens do Landsat TM. Amostras da camada mais superficial dos solos foram coletadas em 740 pontos, e nelas analisados vários atributos do solo. Por meio da reflectância espectral dos objetos das imagens de satélite foram obtidas informações de solo exposto e eliminados outros alvos. As imagens foram adquiridas em série histórica e sobrepostas, gerando uma composta final com solo exposto. Os atributos do solo que obtiveram boa correlação com as bandas dessa imagem foram quantificados por meio da técnica de regressão multivariada e espacializados. Mapas pré-existentes de geologia e pedologia auxiliaram no entendimento da variabilidade espacial da textura e cor dos solos na paisagem. A taxa de variação do solo exposto em uma imagem individual variou de 7 a 20 %, enquanto a unificada atingiu 53 % da área total. Valores de reflectância entre as bandas TM3 e TM4 contrapostos representando a linha do solo e curva espectral média de espectros de amostras de solos obtidas em laboratório apresentaram semelhança com as de satélite. Entre os atributos estudados, a argila obteve a melhor correlação com R2 de 0,75, erro baixo e RPD acima de 2. Outros atributos relacionados com a argila também obtiveram boa correlação, como matéria orgânica (MO) e capacidade de troca de cátions (CTC) com R2 de 0,4 e 0,34 respectivamente. / The knowledge of spatial distribution of soil attributes is necessary for soil planning and monitoring. Landsat 5 Thematic Mapper (TM) images are used in studies related to natural resources for providing the land surface information in large areas and in areas of difficult access. This work aimed to create a multi-temporal image of bare soil through satellite scenes and map soil attributes from the surface. The study area is located in Piracicaba region, SP, where thirteen Landsat TM scenes were selected. Samples of the soil superficial layer were collected at 740 points, and several soil properties were analyzed. Spectral reflectance of different objects from satellite images was obtained and only exposed soil information was selected. Images were acquired in historical series and overlapped, generating a final composed image with bare soil. Soil attributes that presented good correlation with the bands were quantified by multivariate regression and mapped. Pre-existing maps of geology and soil helped in understanding soil texture spatial variability and color in the landscape. The soil variation rate in an individual exposed image ranged from 7 to 20%, while the unified reached 53% of the total area. Obtained values of reflectance between TM3 and TM4 bands representing the soil line and average spectral curve of laboratory soil samples were similar to the satellite ones. Among the soil attributes studied, clay presented the best correlation with R2 value of 0.75, low error and RPD value above 2.0. Other attributes related to clay also presented good correlation, such as organic matter (OM) and cation exchange capacity (CEC) with R2 values of 0.4 and 0.34 respectively.