Flávio Jorge Ponzoni

Climate change impacts are stressing many economic sectors worldwide, including agriculture, increasingly hindering efforts to meet human needs. Dioxide carbon is one of the main greenhouse gases and it affects directly the crop production. The objective of this study was to evaluate if established remote sensing indices could detect the effects of elevated atmospheric CO2 on the leaves of coffee (Coffea arabica L.) plantation growing under field conditions. Plots of coffee plants were exposed to ambient air (~390 µmol CO2 mol-1) and elevated CO2 (~550 µmol CO2 mol-1) at the free air CO2 enrichment (FACE) experiment. The statistical design was the completely randomized blocks with six replicates per treatment (ambient CO2 and elevated CO2), with 10m-diameter plots. Coffee leaves were spectrally characterized by reflectance spectra on their adaxial surfaces and seven vegetation indices were calculated from reflectance data: chlorophyll normalized difference index (Chl NDI), normalized difference nitrogen index (NDNI), normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), pigment specific simple ration indices for chlorophyll a (PSSRa) and chlorophyll b (PSSRb), and structural independent pigment index (SIPI). NDNI was a sensitive indicator of the atmospheric CO2 effects on coffee leaves. NDVI, PSSRa and PSSRb were sensitive to estimate the effect of elevated CO2 only under drought conditions. These indices identify the effect of CO2 when a long period with high precipitation deficit stressing the leaves occurred. Chl NDI, PRI and SIPI were not sensitive to atmospheric CO2. Os impactos das alterações climáticas estão a colocar pressão sobre muitos sectores económicos em todo o mundo, incluindo a agricultura, dificultando cada vez mais os esforços para satisfazer as necessidades humanas. O dióxido de carbono é um dos principais gases de efeito estufa e afeta diretamente a produção agrícola. O objetivo deste estudo foi avaliar se os índices de sensoriamento remoto estabelecidos poderiam detectar os efeitos do elevado dióxido de carbono (CO2) atmosférico nas folhas da plantação de café (Coffea arabica L.) cultivada em condições de campo. Parcelas de cafeeiros foram expostas ao ar ambiente (~390 µmol CO2 mol-1) e CO2 elevado (~550 µmol CO2 mol-1) no experimento de enriquecimento de CO2 ao ar livre (FACE). O delineamento estatístico foi o de blocos inteiramente casualizados com seis repetições por tratamento (CO2 ambiente e CO2 elevado), com parcelas de 10m de diâmetro. Folhas de café foram caracterizadas espectralmente por espectros de refletância em suas superfícies adaxiais e sete índices de vegetação foram calculados a partir de dados de refletância: índice de diferença normalizada de clorofila (Chl NDI), índice de nitrogênio por diferença normalizada (NDVI), índice de vegetação por diferença normalizada (NDVI), índice de refletância fotoquímica (PRI), índices de razão simples específicos de pigmento para clorofila a (PSSRa) e clorofila b (PSSRb) e índice de pigmento independente estrutural (SIPI). O NDNI foi um indicador sensível dos efeitos do CO2 atmosférico nas folhas do café. NDVI, PSSRa e PSSRb foram sensíveis para estimar o efeito do CO2 elevado apenas em condições de seca. Esses índices identificam o efeito do CO2 quando ocorreu um longo período com alto déficit de precipitação estressando as folhas. Chl NDI, PRI e SIPI não foram sensíveis ao CO2 atmosférico. Climate change impacts are stressing many economic sectors worldwide, including agriculture, increasingly hindering efforts to meet human needs. Dioxide carbon is one of the main greenhouse gases and it affects directly the crop production. The objective of this study was to evaluate if established remote sensing indices could detect the effects of elevated atmospheric CO2 on the leaves of coffee (Coffea arabica L.) plantation growing under field conditions. Plots of coffee plants were exposed to ambient air (~390 µmol CO2 mol-1) and elevated CO2 (~550 µmol CO2 mol-1) at the free air CO2 enrichment (FACE) experiment. The statistical design was the completely randomized blocks with six replicates per treatment (ambient CO2 and elevated CO2), with 10m-diameter plots. Coffee leaves were spectrally characterized by reflectance spectra on their adaxial surfaces and seven vegetation indices were calculated from reflectance data: chlorophyll normalized difference index (Chl NDI), normalized difference nitrogen index (NDNI), normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), pigment specific simple ration indices for chlorophyll a (PSSRa) and chlorophyll b (PSSRb), and structural independent pigment index (SIPI). NDNI was a sensitive indicator of the atmospheric CO2 effects on coffee leaves. NDVI, PSSRa and PSSRb were sensitive to estimate the effect of elevated CO2 only under drought conditions. These indices identify the effect of CO2 when a long period with high precipitation deficit stressing the leaves occurred. Chl NDI, PRI and SIPI were not sensitive to atmospheric CO2.

Plant community succession is generally approached with phytosociological methods, but field surveys are time-consuming, expensive, and limited to several of sites. Remote sensing offers an efficient and economical way to analyze vegetation on large extensions and in inaccessible areas. Most studies addressing remote sensing and tree community succession refer to forest physiognomies. We investigated whether structural changes that occur in non-forest physiognomies are identified by multispectral sensor images (OLI-Landsat). Thirteen 0.1-ha plots were set up in Caatinga fragments aging 10-15, 20-25, 30-35, 40-45 and >50 years to calculate the total density of individuals (TD), mean canopy height (H), total basal area (G) and total aboveground biomass (AGB). We performed correlation analyses between these structural descriptors and eight remote sensing variables (reflectance data and spectral indices) obtained from Landsat images at the end of the rainy season and during the dry season. Blue and short-wave infrared reflectances were negatively correlated with mean height, basal area and biomass, regardless of the analyzed scene (coefficients between -0.58 and -0.79). The litter layer (a non-photosynthetic vegetation component) and the soil exposure are important factors influencing the spectral data.

A correção atmosférica é um procedimento necessário em estudos de sensoriamento remoto com foco nas propriedades físicas, químicas e biológicas de alvos. Em abordagens multitemporais e/ou multisensor, a correção atmosférica é fundamental para a obtenção de medidas de reflectância de superfície representativas das características espectrais dos alvos. Embora de grande importância, este procedimento e seus conceitos ainda são pouco difundidos e estudados no âmbito da Geografia brasileira. O objetivo do estudo foi apresentar uma revisão dos principais métodos e parâmetros envolvidos no processo de correção atmosférica de imagens de sensoriamento remoto. Este procedimento visa minimizar os efeitos decorrentes da interação dos constituintes atmosféricos com a radiação eletromagnética, como a absorção provocada pelos gases e o espalhamento produzido por partículas de aerossóis. Sua eficiência depende de um conjunto de fatores, como o método de correção adotado e as características do sistema sensor. Diversos métodos têm sido propostos, sendo classificados em: métodos físicos, fundamentados em modelos de transferência radiativa; métodos empíricos, que estimam os efeitos atmosféricos a partir de alvos específicos da imagem; e métodos híbridos, que combinam informações físicas da atmosfera e estatísticas da cena. Embora os métodos empíricos sejam de fácil aplicação, eles são menos precisos do que os métodos físicos. Estes, por sua vez, necessitam a priori do conhecimento de alguns parâmetros atmosféricos que, dependendo da resolução espectral do sensor, podem ser obtidos da própria imagem. Os métodos híbridos constituem uma alternativa por demandarem um menor conhecimento de parâmetros atmosféricos. Um exemplo dos efeitos atmosféricos sobre o cálculo de índices de vegetação é apresentado. Atmospheric Correction in Remote Sensing: A Review A B S T R A C TAtmospheric correction is a necessary procedure in remote sensing studies focusing on the physical, chemical and biological properties of targets. In multi-temporal and/or multi-sensor approaches, the atmospheric correction is fundamental to obtain surface reflectance measurements representative of the spectral characteristics of targets. Although of great importance, this procedure and its concepts are still little diffused and studied in the scope of the Geography in Brazil. The objective of the study was to present an overview of the main methods and parameters involved in the process of atmospheric correction of remote sensing images. This procedure aims to minimize the effects of the interaction of atmospheric constituents with electromagnetic radiation, such as the absorption caused by gases and the scattering produced by aerosol particles. Its efficiency depends on a set of factors such as the selected correction method and the technical specifications of the sensor system. Several methods have been proposed. They are classified in physical methods, based on radiative transfer models; empirical methods, which estimate atmospheric effects from selected targets in the scene; and hybrid methods, which combine physical information from the atmosphere and scene statistics. Although empirical methods are easy to apply, they are less precise than physical methods. Physical approaches require a priori knowledge of some atmospheric parameters. However, depending on the spectral resolution of the sensor, these parameters can be estimated from the image itself. The hybrid methods are an alternative because they require less knowledge of atmospheric parameters. An example of the atmospheric effects on the determination of vegetation indices is presented.Key-words: image processing, atmospheric effects, spectral behavior, radiometric errors.

We investigated the sensitivity of 16 hyperspectral vegetation indices (VIs) to topographic effects in southeastern Brazil. Using Hyperion data acquired over ombrophilous forest and the topographic C-correction, we calculated the relative changes and statistical differences (Wilcoxon test) between corrected and non-corrected data, and the magnitude of the effect size of topographic correction. The analysis considered 12 terrain illumination classes obtained from cross-comparison between aspect and slope images. The results showed that the most anisotropic indices were the Anthocyanin Reflectance Index (ARI), Enhanced Vegetation Index (EVI) and Photochemical Reflectance Index (PRI). During the comparison between C-corrected and non-corrected data, these VIs presented the largest relative changes, the lowest p-values and the largest metric of effect size. The sensitivity was stronger with increasing slope over south- and west-facing shaded surfaces. The least sensitive VIs were the Visible Index Green (VIG), Visible Atmospherically Resistant Index (VARI) and Structure Insensitive Pigment Index (SIPI).

Several topographic correction methods have been developed to be applied to orbital imagery. The main objective of these initiatives has been focused on improving land cover mapping. Despite of some discussions that include spectral approaches, no attention has been given to the impact of such correction on the spectral characterization of different targets. That impact can influence relationships commonly explored between radiometric data from orbital imagery and biophysical and/or geophysical parameters. TM/Landsat 5 images (orbit 218, row 76) from a mountainous region located between São Paulo, Minas Gerais and Rio de Janeiro states (Brazil) were converted to surface reflectance values. Two topographic correction methods considering different sampling strategies were applied. The local illumination was taking into account during the result analysis in order to evaluate the selective effect of the topographic correction on the spectral characterization of bare soils and vegetated surfaces. The results indicated that the sampling strategy influences both the visual aspect of the topographic corrected images and the spectral characterization of targets. Spectral distortions caused by the topographic correction procedures were higher at darker illumination conditions. The topographic correction is a procedure that deserves attention when exploring spectral characterization of different targets. Sometimes it contributes positively in qualitative approaches such as mapping or digital classification, but it can imply in severe influence on quantitative approaches that are dependent on the targets spectral characterization.

Brazil and China have a long-term joint space based sensor program called China-Brazil Earth Resources Satellite (CBERS). The most recent satellite of this program (CBERS-4) was successfully launched on 7 December 2014. This work describes a complete procedure, along with the associated uncertainties, used to calculate the in-flight absolute calibration coefficients for the sensors Multispectral Camera (MUX) and Wide-Field Imager (WFI) on-board CBERS-4. Two absolute radiometric calibration techniques were applied: (i) reflectance-based approach and (ii) cross-calibration method. A specific site at Algodones Dunes region located in the southwestern portion of the United States of America was used as a reference surface. Radiometric ground and atmospheric measurements were carried out on 9 March 2015, when CBERS-4 passed over the region. In addition, a cross-calibration between both MUX and WFI on-board CBERS-4 and the Operational Land Imager (OLI) on-board Landsat-8 was performed using the Libya-4 Pseudo Invariant Calibration Site. The gain coefficients are now available: 1.68, 1.62, 1.59 and 1.42 for MUX and 0.379, 0.498, 0.360 and 0.351 for WFI spectral bands blue, green, red and NIR, respectively, in units of (W/(m²·sr·μm))/DN. These coefficients were determined with uncertainties lower than 3.5%. As a validation of these radiometric coefficients, cross-calibration was also undertaken. An evaluation of radiometric consistency was performed between the two instruments (MUX and WFI) on-board CBERS-4 and with the well calibrated Landsat-7 ETM+. Results show that the reflectance values match in all the analogous spectral bands within the specified calibration uncertainties.

Foram realizadas estimativas de Índice de Área Foliar (IAF) e de Índice de Área de Vegetação (IAV) em 36 parcelas posicionadas em plantios de Eucalyptus sp. localizados nos municípios de Aracruz e São Mateus (ES), de propriedade da empresa FIBRIA S/A, mediante a aplicação de três diferentes técnicas: método destrutivo, processamento de fotografias hemisféricas tomadas no interior dos dosséis e utilização do equipamento LAI-2000 (LICOR). As coletas de dados em campo foram realizadas em duas épocas do ano, procurando explorar diferenças fenológicas atuantes principalmente sobre os valores de IAF e de IAV. As estimativas de IAF efetuadas a partir do método destrutivo, em termos absolutos, foram sempre superiores àquelas do IAV efetuadas a partir dos métodos indiretos. No período chuvoso, as relações entre as estimativas de IAF e de IAV, para ambos os métodos indiretos, foram mais fortes e consistentes do que aquelas verificadas no período mais seco do ano. As relações entre as estimativas de IAV efetuadas pelos dois métodos indiretos (fotografias e LAI-2000) foram consistentes e com grau de aderência mais elevado do que aquelas verificadas entre o IAF e IAV.

Relationships between biophysical parameters and radiometric data have been tested and evaluated by several professionals using empirical and/or physical approaches. Remote sensing data collected from airborne or orbital platforms are, of course, influenced by different factors, such as illumination/observation geometry (data collection geometry), atmospheric effects, etc., rather than by target spectral properties. Besides that, the target topographic positioning actually defines the amount of incident energy, as well as the amount of energy that is reflected toward the sensor. The sum of both data collection geometry and topographic positioning defines the so-called “local illumination”. The objective of this paper was to evaluate the influence of local illumination on empirical relationships between a biophysical variable (plant area index, PAI) and two vegetation indices calculated from Resourcesat/Linear Imaging Self-Scanner sensor (LISS-3) orbital data. Local illumination was expressed by the cosine factor (Fcos) and calculated from topographic and solar position data at three different dates. The study area was based on a typical Brazilian southeastern forest fragment located in the Augusto Ruschi municipal preservation park dispersed on roughhouse topography. PAI was estimated by hemispherical photographs taken under the forest canopy from sample points arbitrarily dispersed on the forest fragment. Results confirmed a stronger relationship between vegetation indices and local illumination conditions.

The National Institute for Space Research (INPE) decided to freely distribute the orbital images available on its own catalog since the beginning of the last decade. One of the most important consequences of that decision has been the wide usage of the remote sensing technology among different Brazilian user profile, including students, private companies and scientific academies. Besides that, the China-Brazil Earth Resources Satellite (CBERS) program has opened several applications and research possibilities to the Brazilian community, which have not been restricted to mapping or qualitative information extraction from the images, but including quantitative approaches that need an accurate link between the images digital numbers and the effective in-flight radiance measured by the CBERS sensors. This link is established by the so called "absolute calibration" that can be performed before launch and during the flight by internal system or by procedures conducted on the Earth surface (external to the satellite). This last possibility has been carried out on reference surfaces such as those endorsed by the Committee on Earth Observation Satellites (CEOS) that basically are located in desert regions of the planet. The Brazilian territory does not have desert surfaces or any one CEOS endorsed surface, but in-flight calibration campaigns have been performed using agricultural crops surfaces located in the northeast region of the country whose results have been comparable with those achieved internationally. The objective of this chapter is to describe the main CBERS sensors absolute calibration procedures that have been adopted in Brazil.

This work presents the contribution of orbital sensors for obtaining information on vegetation studies. Initially it is described the basic concepts of getting information from remote sensors, information about natural resources satellites and their sensors and applications in the study of vegetation. Following it is presented a description of the works using data from orbital sensors for mapping and monitoring vegetation and works for assessment of vegetation biophysical parameters. In addition data from some sensors and products derived from such data like the vegetation indices that have been used for several studies of vegetation cover are presented. In this manner, it is possible to demonstrate the contribution of orbital sensors data especially for mapping and monitoring vegetation cover. The availability of data from new sensors with better spectral, spatial, and temporal characteristics will enable to monitor these resources with more detail to assure quality and sustainability in our planet.