Hyperspectral field measurements to determine dry matter yield and nutritive value of legume-grass swards

Summary: Productivity, botanical composition and forage quality of legume-grass swards are important factors for successful arable farming in both organic and conventional farming systems. As these attributes can vary considerably within a field, a non-destructive method of detection while doing other tasks would facilitate a more targeted management of crops, forage and nutrients in the soil-plant-animal system. This study was undertaken to explore the potential of field spectral measurements for a non destructive prediction of dry matter (DM) yield, legume proportion in the sward, metabolizable energy (ME), ash content, crude protein (CP) and acid detergent fiber (ADF) of legume-grass mixtures. Two experiments were conducted in a greenhouse under controlled conditions which allowed collecting spectral measurements which were free from interferences such as wind, passing clouds and changing angles of solar irradiation. In a second step this initial investigation was evaluated in the field by a two year experiment with the same legume-grass swards. Several techniques for analysis of the hyperspectral data set were examined in this study: four vegetation indices (VIs): simple ratio (SR), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and red edge position (REP), two-waveband reflectance ratios, modified partial least squares (MPLS) regression and stepwise multiple linear regression (SMLR). The results showed the potential of field spectroscopy and proved its usefulness for the prediction of DM yield, ash content and CP across a wide range of legume proportion and growth stage. In all investigations prediction accuracy of DM yield, ash content and CP could be improved by legume-specific calibrations which included mixtures and pure swards of perennial ryegrass and of the respective legume species. The comparison between the greenhouse and the field experiments showed that the interaction between spectral reflectance and weather conditions as well as incidence angle of light interfered with an accurate determination of DM yield. Further research is hence needed to improve the validity of spectral measurements in the field. Furthermore, the developed models should be tested on varying sites and vegetation periods to enhance the robustness and portability of the models to other environmental conditions.

Identification of Putative Wintering Areas and Ecological Determinants of Population Dynamics of Common House-Martin (Delichon urbicum) and Common Swift (Apus apus) Breeding in Northern Italy

To identify the causes of population decline in migratory birds, researchers must determine the relative influence of environmental changes on population dynamics while the birds are on breeding grounds, wintering grounds, and en route between the two. This is problematic when the wintering areas of specific populations are unknown. Here, we first identified the putative wintering areas of Common House-Martin (Delichon urbicum) and Common Swift (Apus apus) populations breeding in northern Italy as those areas, within the wintering ranges of these species, where the winter Normalized Difference Vegetation Index (NDVI), which may affect winter survival, best predicted annual variation in population indices observed in the breeding grounds in 1992–2009. In these analyses, we controlled for the potentially confounding effects of rainfall in the breeding grounds during the previous year, which may affect reproductive success; the North Atlantic Oscillation Index (NAO), which may account for climatic conditions faced by birds during migration; and the linear and squared term of year, which account for nonlinear population trends. The areas thus identified ranged from Guinea to Nigeria for the Common House-Martin, and were located in southern Ghana for the Common Swift. We then regressed annual population indices on mean NDVI values in the putative wintering areas and on the other variables, and used Bayesian model averaging (BMA) and hierarchical partitioning (HP) of variance to assess their relative contribution to population dynamics. We re-ran all the analyses using NDVI values at different spatial scales, and consistently found that our population of Common House-Martin was primarily affected by spring rainfall (43%–47.7% explained variance) and NDVI (24%–26.9%), while the Common Swift population was primarily affected by the NDVI (22.7%–34.8%). Although these results must be further validated, currently they are the only hypotheses about the wintering grounds of the Italian populations of these species, as no Common House-Martin and Common Swift ringed in Italy have been recovered in their wintering ranges.

Using the variogram to explore imagery of two different spatial resolutions

The resolution of remotely sensed data is becoming increasingly fine, and there are now many sources of data with a pixel size of 1 m x 1 m. This produces huge amounts of data that have to be stored, processed and transmitted. For environmental applications this resolution possibly provides far more data than are needed: data overload. This poses the question: how much is too much? We have explored two resolutions of data-20 in pixel SPOT data and I in pixel Computerized Airborne Multispectral Imaging System (CAMIS) data from Fort A. P. Hill (Virginia, USA), using the variogram of geostatistics. For both we used the normalized difference vegetation index (NDVI). Three scales of spatial variation were identified in both the SPOT and 1 in data: there was some overlap at the intermediate spatial scales of about 150 in and of 500 m-600 in. We subsampled the I in data and scales of variation of about 30 in and of 300 in were identified consistently until the separation between pixel centroids was 15 in (or 1 in 225pixels). At this stage, spatial scales of about 100m and 600m were described, which suggested that only now was there a real difference in the amount of spatial information available from an environmental perspective. These latter were similar spatial scales to those identified from the SPOT image. We have also analysed I in CAMIS data from Fort Story (Virginia, USA) for comparison and the outcome is similar.:From these analyses it seems that a pixel size of 20m is adequate for many environmental applications, and that if more detail is required the higher resolution data could be sub-sampled to a 10m separation between pixel centroids without any serious loss of information. This reduces significantly the amount of data that needs to be stored, transmitted and analysed and has important implications for data compression.

The 2010-2011 drought in the Horn of Africa in ECMWF reanalysis and seasonal forecast products

This study evaluates the use of European Centre for Medium-Range Weather Forecasts (ECMWF) products in monitoring and forecasting drought conditions during the recent 2010–2011 drought in the Horn of Africa (HoA). The region was affected by a precipitation deficit in both the October–December 2010 and March–May 2011 rainy seasons. These anomalies were captured by the ERA-Interim reanalysis (ERAI), despite its limitations in representing the March–May interannual variability. Soil moisture anomalies of ERAI also identified the onset of the drought condition early in October 2010 with a persistent drought still present in September 2011. This signal was also evident in normalized difference vegetation index (NDVI) remote sensing data. The precipitation deficit in October–December 2010 was associated with a strong La Niña event. The ECMWF seasonal forecasts for the October–December 2010 season predicted the La Niña event from June 2010 onwards. The forecasts also predicted a below-average October–December rainfall, from July 2010 onwards. The subsequent March–May rainfall anomaly was only captured by the new ECWMF seasonal forecast system in the forecasts starting in March 2011. Our analysis shows that a recent (since 1999) drying in the region during the March–May season is captured by the new ECMWF seasonal forecast system and is consistent with recently published results. The HoA region and its population are highly vulnerable to future droughts, thus global monitoring and forecasting of drought, such as that presented here, will become increasingly important in the future. Copyright © 2012 Royal Meteorological Society

Probabilistic downscaling of remote sensing data with applications for multi-scale biogeochemical flux modeling

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes.

Detecção de Heterodera glycines em plantio de soja mediante espectrorradiometria no visível e infravermelho próximo

O ataque do nematóide de cisto da soja, Heterodera glycines, limita o potencial de expansão e maior produtividade de áreas plantadas com soja (Glycine Max). O conhecimento da distribuição espacial desse patógeno na lavoura é fundamental, para elaboração de estratégias de manejo. A área em estudo estava localizada em lavoura de soja, variedade BRS133, localizada no Município de Florínea, SP, com solos naturalmente infestados por H. glycines. Foram obtidas medidas de espectrorradiometria de campo, 112 dias após o plantio, nas regiões do visível e do infravermelho próximo do espectro eletromagnético, a fim de se conhecer o padrão da resposta espectral de plantas atacadas pelo fitonematóide. Paralelamente, foram retiradas amostras de solo e encaminhadas ao Laboratório de Nematologia, Departamento de Fitossanidade da Universidade Estadual Paulista Júlio de Mesquita Filho, Campus de Jaboticabal, onde foram processadas para determinação da densidade populacional do nematóide. As medidas do espectrorradiômetro foram transformadas em índice vegetativo, com diferença normalizada (NDVI), que foi relacionado com a densidade populacional do nematóide, peso da matéria fresca e número de vagens por planta. Observou-se que diferentes densidades de população estão diretamente relacionados com a resposta espectral das plantas expressa, através dos valores do NDVI.

Análise de uso e cobertura do solo no município de Parelhas/RN

The human interference in the semiarid region of Seridó Potiguar has promoted the increase of degraded areas. The economic dynamic that was established in the Seridó territory, especially after the fall of the trinomial cattle-cotton-mining in the 70s and 80s of the 20th century as pillars of the regional economy, resulted in an accelerated process of erosion of natural resources. The municipalities of the Seridó region have been spatially reordered by this new economic dynamic, marked by the growth of existing enterprises, and the development of new agricultural practices. One of the municipalities in the region that restructured its territorial space with the emergence of new agro-industrial activities was the town of Parelhas. With the demise of the trinomial cattle-cotton-mining in the 1980s, other productive activities were intensified from the 1990s, amongst them, pottery, responsible for the vegetal extraction for use as energy source. This recent economic and spatial restructuring in the region, reflected in the Parelhense municipal territory, required new productive ingredients responsible for the modification of past production relations that were based on cattle, cotton and mining. By that a process of exploring the environment was unleashed, especially the native vegetation, in an uncontrolled manner. In this context, the objective of this study was to survey and detect deforestation in the areas of Caatinga vegetation, used indiscriminately as energy supply for new agricultural practices, using remote sensing techniques based on the quantification of the Normalized Difference Vegetation Index / NDVI, Soil-Adjusted Vegetation Index / SAVI, surface temperature and rainfall data in the years 1990 and 2010. The results indicated that SAVI values above 0.2 in 1990 and 2010 represent the areas with the highest density of vegetation that occur exclusively along the major drainages in the town and areas of higher elevations. The areas between the ranges of values from 0.5 to 0.15 SAVI are areas with poor vegetation. On the other hand the highest values of temperature are distributed in the western and southeastern parts of the township, usually in places where the soil is exposed or there is sparse vegetation. The areas of bare soil decreased in extension in 2010 at 11, 6% when related to 1990, this was caused by a higher rainfall intensity in the first half of 2010, but no regeneration of vegetation occurred in some places in the western and southeastern areas of the municipality today, due to the extraction of firewood to fuel the furnaces of industries in town

Modelagem geoestatística por geoprocessamento em uma área da Baixada Santista

Pós-graduação em Geociências e Meio Ambiente - IGCE

Modelagem da biomassa acima do solo (BAS), por meio de imagens de alta resolução espacial e índices de vegetação

Pós-graduação em Agronomia - FEIS

Aplicação de efluente de tratamento de esgoto, via aspersão, no solo e em Brachiaria

Pós-graduação em Agronomia (Ciência do Solo) - FCAV

Efeito da fertilização e da disponibilidade hídrica na produtividade florestal e na eficiência do uso da luz de povoamentos clonais de eucalyptus spp. em Bocaiúva,MG

Pós-graduação em Ciência Florestal - FCA

Imagens aéreas multiespectrais na identificação de zonas de manejo em áreas de algodão para aplicação localizada de insumos

Crop yield is influenced by several factors with variability in time and space that are associated with the variations in the plant vigor. This variability allows the identification of management zones and site-specific applications to manage different regions of the field. The purpose of this study was the use of multispectral image for management zones identification and implications of site-specific application in commercial cotton areas. Multispectral airborne images from three years were used to classify a field into three vegetation classes via the Normalized Difference Vegetation Index (NDVI). The NDVI classes were used to verify the potential differences between plant physical measurements and identify management zones. The cotton plant measurements sampled in 8 repetitions of 10 plants at each NDVI class were Stand Count, Plant Height, Total Nodes and Total Bolls. Statistical analysis was performed with treatments arranged in split plot design with Tukey’s Test at 5% of probability. The images were classified into five NDVI classes to evaluate the relationship between cotton plant measurement results and sampling location across the field. The results have demonstrated the possibility of using multispectral image for management zones identification in cotton areas. The image classification into three NDVI classes showed three different zones in the field with similar characteristics for the studied years. Statistical differences were shown for plant height, total nodes and total bolls between low and high NDVI classes for all years. High NDVI classes contained plants with greater height, total nodes and total bolls compared to low NDVI classes. There was no difference in Stand Count between low and high NDVI classes for the three studied years. The final plant stand was the same between all NDVI classes for 2001 and 2003 as it was expected due to the conventional seeding application with the same rate of seeds for the entire field.

Arctic aboveground tundra biomass dynamics between 1982 and 2010

Numerous studies have evaluated the dynamics of Arctic tundra vegetation throughout the past few decades, using remotely sensed proxies of vegetation, such as the normalized difference vegetation index (NDVI). While extremely useful, these coarse-scale satellite-derived measurements give us minimal information with regard to how these changes are being expressed on the ground, in terms of tundra structure and function. In this analysis, we used a strong regression model between NDVI and aboveground tundra phytomass, developed from extensive field-harvested measurements of vegetation biomass, to estimate the biomass dynamics of the circumpolar Arctic tundra over the period of continuous satellite records (1982-2010). We found that the southernmost tundra subzones (C-E) dominate the increases in biomass, ranging from 20 to 26%, although there was a high degree of heterogeneity across regions, floristic provinces, and vegetation types. The estimated increase in carbon of the aboveground live vegetation of 0.40 Pg C over the past three decades is substantial, although quite small relative to anthropogenic C emissions. However, a 19.8% average increase in aboveground biomass has major implications for nearly all aspects of tundra ecosystems including hydrology, active layer depths, permafrost regimes, wildlife and human use of Arctic landscapes. While spatially extensive on-the-ground measurements of tundra biomass were conducted in the development of this analysis, validation is still impossible without more repeated, long-term monitoring of Arctic tundra biomass in the field.

Análise espacial da leishmaniose visceral canina no município de Panorama, São Paulo, Brasil

A leishmaniose visceral é uma zoonose de grande importância para a saúde pública, com ampla distribuição geográfica e epidemiologia complexa. Apesar de diversas estratégias de controle, a doença continua se expandindo, tendo o cão como principal reservatório. Levando em consideração que análises espaciais são úteis para compreender melhor a dinâmica da doença, avaliar fatores de risco e complementar os programas de prevenção e controle, o presente estudo teve como objetivo caracterizar a distribuição da leishmaniose visceral canina e relacionar sua dinâmica com características ou feições espaciais no município de Panorama (SP). A partir de dados secundários coletados em um inquérito sorológico entre agosto de 2012 e janeiro de 2013, 986 cães foram classificados como positivos e negativos de acordo com o protocolo oficial do Ministério da Saúde. Posteriormente uma análise espacial foi conduzida, compreendendo desde a visualização dos dados até a elaboração de um mapa de risco relativo, passando por análises de cluster global (função K) e local (varredura espacial). Para avaliar uma possível relação entre o cluster detectado com a vegetação na área de estudo, calculou-se o Índice de Vegetação por Diferença Normalizada (NDVI). A prevalência da doença encontrada na população de cães estudada foi de 20,3% (200/986). A visualização espacial demonstrou que tanto animais positivos quanto negativos estavam distribuídos por toda a área de estudo. O mapa de intensidade dos animais positivos apontou duas localidades de possíveis clusters, quando comparado ao mapa de intensidade dos animais negativos. As análises de cluster confirmaram a presença de um aglomerado e um cluster foi detectado na região central do município, com um risco relativo de 2,63 (p=0,01). A variação espacial do risco relativo na área de estudo foi mapeada e também identificou a mesma região como área significativa de alto risco (p<0,05). Não foram observadas diferenças no padrão de vegetação comparando as áreas interna e externa ao cluster. Sendo assim, novos estudos devem ser realizados com o intuito de compreender outros fatores de risco que possam ter levado à ocorrência do cluster descrito. A prevalência, a localização do cluster espacial e o mapa de risco relativo fornecem subsídios para direcionamento de esforços do Setor de Vigilância Epidemiológica de Panorama para áreas de alto risco, o que pode poupar recursos e aperfeiçoar o controle da leishmaniose visceral no município.

Automatic building detection using the Dempster-Shafer algorithm

An approach and strategy for automatic detection of buildings from aerial images using combined image analysis and interpretation techniques is described in this paper. It is undertaken in several steps. A dense DSM is obtained by stereo image matching and then the results of multi-band classification, the DSM, and Normalized Difference Vegetation Index (NDVI) are used to reveal preliminary building interest areas. From these areas, a shape modeling algorithm has been used to precisely delineate their boundaries. The Dempster-Shafer data fusion technique is then applied to detect buildings from the combination of three data sources by a statistically-based classification. A number of test areas, which include buildings of different sizes, shape, and roof color have been investigated. The tests are encouraging and demonstrate that all processes in this system are important for effective building detection.