Search

Despite the importance of the Amazonian rainforests in the global carbon cycle, their phenological responses measured by large field-of-view satellite sensors are still not completely understood. In this scenario, close-to-nadir observations at high spatial and temporal resolutions made by satellite constellations may contribute to improve this knowledge. Here, we investigated the sensitivity of five vegetation indices (VIs) to canopy shadows over 15 protected forests of the Amazon, and the possible existence of coupled effects of solar illumination and vegetation phenology on the VI determination. The VIs are the Enhanced Vegetation Index (EVI), Green-Red Normalized Difference (GRND), Modified Photochemical Reflectance Index (MPRI), Normalized Difference Vegetation Index (NDVI), and RedEdge Normalized Difference (REND). They were calculated from 432 images obtained in 2022 by the Planet’s eight-band SuperDove instrument. Few daily images acquired on the same day with distinct Solar Zenith Angle (SZA) were used to disentangle the effects of solar illumination from those of vegetation phenology. The results showed the presence of coupled effects of solar illumination and vegetation phenology on the EVI determination regardless of the site location, especially over dense forests. Such effects were not observed significantly in the GRND, MPRI, NDVI, and REND data. When the vegetation phenology was kept fixed in the analysis, solar illumination generated pseudo-greening effects from the EVI, even for small differences in SZA between daily observations. As the most sensitive VI to illumination conditions, the EVI increased from the beginning to the end of the dry season tracking solar angles and shade fractions. This dry-season trend was not observed for GRND, MPRI, NDVI, and REND, which presented low correlations with SZA and shade fractions. These four VIs were correlated with each other over most sites, which explained the agreement observed between their seasonal profiles. From the analysis of 15 sites distributed throughout the Amazon, our findings did not confirm patterns of large-scale greening at the end of the dry season. Local changes in greening (vegetation productivity) and browning were captured by the VIs over a few sites but in different periods of the dry season (June to September). At the high spatial scale of SuperDove observations, our results highlight the necessity of correcting solar and, in some cases, terrain illumination effects on the EVI before retrieving phenological metrics over the Amazon.

Integrating Light Detection And Ranging (LiDAR) and Hyperspectral Imaging (HSI) enhances the assessment of tropical forest degradation and regeneration, which is crucial for conservation and climate mitigation strategies. This study optimized procedures using combined airborne LiDAR, HSI data, and machine learning algorithms across 12 sites in the Brazilian Amazon, covering various environmental and anthropogenic conditions. Four forest classes (undisturbed, degraded, and two stages of second-growth) were identified using Landsat time series (1984–2017) and auxiliary data. Metrics from 600 samples were analyzed with three classifiers: Random Forest, Stochastic Gradient Boosting, and Support Vector Machine. The combination of LiDAR and HSI data improved classification accuracy by up to 12% compared with single data sources. The most decisive metrics were LiDAR-based upper canopy cover and HSI-based absorption bands in the near-infrared and shortwave infrared. LiDAR produced significantly fewer errors for discriminating second-growth from old-growth forests, while HSI had better performance to discriminate degraded from undisturbed forests. HSI-only models performed similarly to LiDAR-only models (mean F1 of about 75% for both data sources). The results highlight the potential of integrating LiDAR and HSI data to improve our understanding of forest dynamics in the context of nature-based solutions to mitigate climate change impacts.

Precision agriculture integrates multiple sensors and data types to support farmers with informed decision-making tools throughout crop cycles. This study evaluated Aboveground Biomass (AGB) estimates of Rye using attributes derived from PlanetScope (PS) optical, Sentinel-1 Synthetic Aperture Radar (SAR), and hybrid (optical plus SAR) datasets. Optical attributes encompassed surface reflectance from PS’s blue, green, red, and near-infrared (NIR) bands, alongside the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Sentinel-1 SAR attributes included the C-band Synthetic Aperture Radar Ground Range Detected, VV and HH polarizations, and both Ratio and Polarization (Pol) indices. Ground reference AGB data for Rye (Secale cereal L.) were collected from 50 samples and four dates at a farm located in southern Brazil, aligning with image acquisition dates. Multiple linear regression models were trained and validated. AGB was estimated based on individual (optical PS or Sentinel-1 SAR) and combined datasets (optical plus SAR). This process was repeated 100 times, and variable importance was extracted. Results revealed improved Rye AGB estimates with integrated optical and SAR data. Optical vegetation indices displayed higher correlation coefficients (r) for AGB estimation (r = +0.67 for both EVI and NDVI) compared to SAR attributes like VV, Ratio, and polarization (r ranging from −0.52 to −0.58). However, the hybrid regression model enhanced AGB estimation (R2 = 0.62, p < 0.01), reducing RMSE to 579 kg·ha−1. Using only optical or SAR data yielded R2 values of 0.51 and 0.42, respectively (p < 0.01). In the hybrid model, the most important predictors were VV, NIR, blue, and EVI. Spatial distribution analysis of predicted Rye AGB unveiled agricultural zones associated with varying biomass throughout the cover crop development. Our findings underscored the complementarity of optical with SAR data to enhance AGB estimates of cover crops, offering valuable insights for agricultural zoning to support soil and cash crop management.

Full-waveform LiDAR (FWF) offers a promising advantage over other technologies to represent the vertical canopy structure of secondary successions in the Amazon region, as the waveform encapsulates the properties of all elements intercepting the emitted beam. In this study, we investigated modifications in the vertical structure of the Amazonian secondary successions across the vegetation gradient from early to advanced stages of vegetation regrowth. The analysis was performed over two distinct climatic regions (Drier and Wetter), designated using the Maximum Cumulative Water Deficit (MCWD). The study area was covered by 309 sample plots distributed along 25 LiDAR transects. The plots were grouped into three successional stages (early—SS1; intermediate—SS2; advanced—SS3). Mature Forest (MF) was used as a reference of comparison. A total of 14 FWF LiDAR metrics from four categories of analysis (Height, Peaks, Understory and Gaussian Decomposition) were extracted using the Waveform LiDAR for Forestry eXtraction (WoLFeX) software (v1.1.1). In addition to examining the variation in these metrics across different successional stages, we calculated their Relative Recovery (RR) with vegetation regrowth, and evaluated their ability to discriminate successional stages using Random Forest (RF). The results showed significant differences in FWF metrics across the successional stages, and within and between sample plots and regions. The Drier region generally exhibited more pronounced differences between successional stages and lower FWF metric values compared to the Wetter region, mainly in the category of height, peaks, and Gaussian decomposition. Furthermore, the Drier region displayed a lower relative recovery of metrics in the early years of succession, compared to the areas of MF, eventually reaching rates akin to those of the Wetter region as succession progressed. Canopy height metrics such as Waveform distance (WD), and Gaussian Decomposition metrics such as Bottom of canopy (BC), Bottom of canopy distance (BCD) and Canopy distance (CD), related to the height of the lower forest stratum, were the most important attributes in discriminating successional stages in both analyzed regions. However, the Drier region exhibited superior discrimination between successional stages, achieving a weighted F1-score of 0.80 compared to 0.73 in the Wetter region. When comparing the metrics from SS in different stages to MF, our findings underscore that secondary forests achieve substantial relative recovery of FWF metrics within the initial 10 years after land abandonment. Regions with potentially slower relative recovery (e.g., Drier regions) may require longer-term planning to ensure success in providing full potential ecosystem services in the Amazon.

Pasture degradation poses significant economic, social, and environmental impacts in the Brazilian savanna ecosystem. Despite these impacts, effectively detecting varying intensities of agronomic and biological degradation through remote sensing remains challenging. This study explores the potential of the eight-band PlanetScope SuperDove satellite constellation to discriminate between five classes of pasture degradation: non-degraded pasture (NDP); pastures with low- (LID) and moderate-intensity degradation (MID); severe agronomic degradation (SAD); and severe biological degradation (SBD). Using a set of 259 cloud-free images acquired in 2022 across five sites located in central Brazil, the study aims to: (i) identify the most suitable period for discriminating between various degradation classes; (ii) evaluate the Random Forest (RF) classification performance of different SuperDove attributes; and (iii) compare metrics of accuracy derived from two predicted scenarios of pasture degradation: a more challenging one involving five classes (NDP, LID, MID, SAD, and SBD), and another considering only non-degraded and severely degraded pastures (NDP, SAD, and SBD). The study assessed individual and combined sets of SuperDove attributes, including band reflectance, vegetation indices, endmember fractions from spectral mixture analysis (SMA), and image texture variables from Gray-level Co-occurrence Matrix (GLCM). The results highlighted the effectiveness of the transition from the rainy to the dry season and the period towards the beginning of a new seasonal rainy cycle in October for discriminating pasture degradation. In comparison to the dry season, more favorable discrimination scenarios were observed during the rainy season. In the dry season, increased amounts of non-photosynthetic vegetation (NPV) complicate the differentiation between NDP and SBD, which is characterized by high soil exposure. Pastures exhibiting severe biological degradation showed greater sensitivity to water stress, manifesting earlier reflectance changes in the visible and near-infrared bands of SuperDove compared to other classes. Reflectance-based classification yielded higher overall accuracy (OA) than the approaches using endmember fractions, vegetation indices, or texture metrics. Classifications using combined attributes achieved an OA of 0.69 and 0.88 for the five-class and three-class scenarios, respectively. In the five-class scenario, the highest F1-scores were observed for NDP (0.61) and classes of agronomic (0.71) and biological (0.88) degradation, indicating the challenges in separating low and moderate stages of pasture degradation. An initial comparison of RF classification results for the five categories of degraded pastures, utilizing reflectance data from MultiSpectral Instrument (MSI)/Sentinel-2 (400–2500 nm) and SuperDove (400–900 nm), demonstrated an enhanced OA (0.79 versus 0.66) with Sentinel-2 data. This enhancement is likely to be attributed to the inclusion of shortwave infrared (SWIR) spectral bands in the data analysis. Our findings highlight the potential of satellite constellation data, acquired at high spatial resolution, for remote identification of pasture degradation.

Gaps are openings within tropical forest canopies created by natural or anthropogenic disturbances. Important aspects of gap dynamics that are not well understood include how gaps close over time and their potential for contagiousness, indicating whether the presence of gaps may or may not induce the creation of new gaps. This is especially important when we consider disturbances from selective logging activities in rainforests, which take away large trees of high commercial value and leave behind a forest full of gaps. The goal of this study was to quantify and understand how gaps open and close over time within tropical rainforests using a time series of airborne LiDAR data, attributing observed processes to gap types and origins. For this purpose, the Jamari National Forest located in the Brazilian Amazon was chosen as the study area because of the unique availability of multi-temporal small-footprint airborne LiDAR data covering the time period of 2011–2017 with five data acquisitions, alongside the geolocation of trees that were felled by selective logging activities. We found an increased likelihood of natural new gaps opening closer to pre-existing gaps associated with felled tree locations (−1) and about 90% of the original gap area was closed at six years post-disturbance. Therefore, the relative contribution of lateral and vertical growth for gap closure was similar when consolidated over time. We highlight that aboveground biomass or carbon density of logged forests can be overestimated if considering only top of the canopy height metrics due to fast lateral ingrowth of neighboring trees, especially in the first two years of regeneration where 26% of gaps were closed solely by lateral ingrowth, which would not translate to 26% of regeneration of forest biomass. Trees inside gaps grew 2.2 times faster (1.5 m yr−1) than trees at the surrounding non-gap canopy (0.7 m yr−1). Our study brings new insights into the processes of both the opening and closure of forest gaps within tropical forests and the importance of considering gap types and origins in this analysis. Moreover, it demonstrates the capability of airborne LiDAR multi-temporal data in effectively characterizing the impacts of forest degradation and subsequent recovery.

We investigated the potential of hyperspectral remote sensing to estimate aboveground biomass (AGB) over the Brazilian savannas (Cerrado), the second-largest source of carbon emissions in Brazil. For this purpose, a Hyperion/Earth Observing-1 (EO-1) image was collected in the dry season at the Ecological Station of Águas Emendadas (ESAE). In order to estimate the AGB, we evaluated the performance of five machine learning models (Classification and Regression Trees – CART; Cubist – CB, Partial Least Squares Regression – PLS; Random Forest – RF; and Support Vector Machine – SVM) and four sets of metrics (reflectance, narrowband vegetation indices – VIs; absorption band parameters; and the combination of these attributes). The lowest root mean square error (RMSE) was obtained for RF using VIs (29%) and a combination of metrics (28%). For VIs, RF differed from CUB, PLS and SVM at 5% significance level. From cross-validation results, the RMSE was 26.36% for grasslands, 35.04% for open savannas, and 24.85% for dense savannas. The RF model with VIs had the most stable predictive performance across the models, as indicated by small variations in RMSE from CART to SVM. The five most important ranked VIs in the RF model were the Normalized Difference Vegetation Index (NDVI), Pigment Specific Simple Ratio (PSSR), Enhanced Vegetation Index (EVI), Red Edge Normalized Difference Vegetation Index (RENDVI) and Structure Insensitive Pigment Index (SIPI). Most of their relationships with AGB were non-linear. The resultant AGB estimates showed consistent results with a vegetation cover map of the ESAE. Areas of the ESAE with AGB lower than 10 Mg.ha−1 were coincident with the occurrence of grassland physiognomies (savanna grasslands and shrub savannas), while areas with AGB higher than 25 Mg.ha−1 matched the occurrence of dense savanna physiognomies (woodland savanna and dense woodland savanna). Grassland areas showed larger values of coefficient of variation (CV) than areas of dense savannas. These first-hand results set a baseline of models and metrics for AGB modeling of savannas during the future transition from current sampling-type hyperspectral missions (< 10 km of swath) to large-coverage hyperspectral satellites (> 100 km of swath).

Blowdown events are a major natural disturbance in the central Amazon Forest, but their impact and subsequent vegetation recovery have been poorly understood. This study aimed to track post-disturbance regeneration after blowdown events in the Amazon Forest. We analyzed 45 blowdown sites identified after September 2020 at Amazonas, Mato Grosso, and Colombia jurisdictions using Landsat-8 and PlanetScope NICFI satellite imagery. Non-photosynthetic vegetation (NPV), green vegetation (GV), and shade fractions were calculated for each image and sensor using spectral mixture analysis in Google Earth Engine. The results showed that PlanetScope NICFI data provided more regular and higher-spatial-resolution observations of blowdown areas than Landsat-8, allowing for more accurate characterization of post-disturbance vegetation recovery. Specifically, NICFI data indicated that just four months after the blowdown event, nearly half of ΔNPV, which represents the difference between the NPV after blowdown and the NPV before blowdown, had disappeared. ΔNPV and GV values recovered to pre-blowdown levels after approximately 15 months of regeneration. Our findings highlight that the precise timing of blowdown detection has huge implications on quantification of the magnitude of damage. Landsat data may miss important changes in signal due to the difficulty of obtaining regular monthly observations. These findings provide valuable insights into vegetation recovery dynamics following blowdown events.

We investigated the dynamics of savanna clearing and land degradation in the MATOPIBA region, the newest agricultural frontier over the Cerrado in Brazil. For geospatial analysis of the “big data,” we used algorithms available on the Google Earth Engine (GEE) platform along with different satellite products. For the detection of vegetation clearing in the 1985–2018 period, we applied the LandTrendr algorithm to the normalized difference vegetation index (NDVI) obtained from Landsat satellite instruments. In the evaluation of land degradation (2001–2015), we used the Trends.Earth algorithm to analyze NDVI data calculated from the Moderate Resolution Imaging Spectroradiometer (MODIS). By combining different datasets into the analysis, we discussed the relationships among savanna clearings, land degradation, topographic attributes, and fire frequency. The results showed that the largest proportions of savanna clearing in the 1985–1990 period were detected in the south of MATOPIBA (mesoregions Ocidental do Tocantins and Extremo Oeste Baiano). The savanna clearing progressed toward the mesoregions Oriental do Tocantins, Sudoeste Piauiense, and Sul Maranhense between 1991 and 2005. Variations over time in cleared areas ranged from 0.086 Mha to 2.146 Mha per year, showing well-defined peaks in 1986, 1991, and 2012. The clearing events followed the soybean expansion, especially over flat terrains at high elevations. In addition to the spillover effects of the Amazon Soy Moratorium on the savannas, other political and economic factors promoted changes in cleared areas of MATOPIBA. Compared to the decade preceding the signature of the Amazon Soy Moratorium in 2006, the cleared area of MATOPIBA increased 41% in the 10-year post-moratorium period. In the other analysis, our Trends.Earth results showed that the largest proportions of land degradation areas, as proxied by the MODIS NDVI, were the Sudoeste Piauiense and Centro Maranhense. These mesoregions presented distinct patterns of precipitation and soil composition. The number of fire events increased with vegetation clearing. However, the amount of cleared areas correlated moderately with that of burned areas due to the multi-use of fire in the region. Finally, we observed that the amount of land-degraded areas increased with fire frequency over agricultural areas, but remained relatively stable over native vegetated areas. Results highlight the importance of satellite sensors and cloud-computing platforms to inspect long-term trajectories of land occupation and degradation over broad savanna areas.

We designed a unique hyperspectral experiment from the Earth Observing One (EO-1) orbit change to evaluate solar illumination effects over tropical forests in Brazil. Ten nadir-viewing Hyperion images collected over a fixed site and period of the year (July to August) were selected for analysis. We evaluated variations in reflectance and in 16 narrowband vegetation indices (VIs) with increasing solar zenith angle (SZA) from the pre-drift (2004–2008) to the EO-1 drift period (2011–2016). To detect changes in reflectance and shadows, we applied spectral mixture analysis (SMA) and principal component analysis (PCA) and calculated the similarity spectral angle (θ) between the vegetation spectra measured with variable SZA. The magnitude of the illumination effects was also evaluated from change-point analysis and nonparametric Mann-Whitney U tests applied over the time series. Finally, we complemented our experiment using the PROSAIL model to simulate the VIs variation with increasing SZA resultant from satellite drift. The results showed significant changes in Hyperion reflectance and VIs, especially when the EO-1 crossed the study area at earlier times and larger SZA in 2015 (9:05 a.m.; SZA = 59°) and 2016 (8:30 a.m.; SZA = 67°). Compared to the pre-drift period (10:30 a.m.; SZA = 45°), the SZA differences of 14° (2015) and 22° (2016) increased the shade fractions and decreased the vegetation brightness. PCA separated the pre-drift and drift reflectance datasets, showing shifts in scores due to changes in brightness. θ increased with SZA, indicating changes in the shape of the vegetation spectra with drift. For most VIs, the change-point analysis indicated 2015 (SZA = 59°) as the predominant year of detected changes. Compared to the EO-1 original orbit, the Plant Senescence Reflectance Index (PSRI), Anthocyanin Reflectance Index (ARI) and Structure Insensitive Pigment Index (SIPI) presented the largest positive changes during drift, while the Photochemical Reflectance Index (PRI), Visible Atmospherically Resistant Index (VARI) and Enhanced Vegetation Index (EVI) had the largest negative changes. The effect size of the illumination geometry on these VIs was large, as indicated by increasing values of the Cohen’s r metric toward 2016. The anisotropy of the Hyperion VIs was generally consistent with that from PROSAIL in the simulated pre-drift and drift periods. Focusing on structural indices, it affected the relationships between VIs and simulated leaf area index (LAI) at large SZA.

Tropical seasonal biomes (TSBs), such as the savannas (Cerrado) and semi-arid woodlands (Caatinga) of Brazil, are vulnerable ecosystems to human-induced disturbances. Remote sensing can detect disturbances such as deforestation and fires, but the analysis of change detection in TSBs is affected by seasonal modifications in vegetation indices due to phenology. To reduce the effects of vegetation phenology on changes caused by deforestation and fires, we developed a novel object-based change detection method. The approach combines both the spatial and spectral domains of the normalized difference vegetation index (NDVI), using a pair of Operational Land Imager (OLI)/Landsat-8 images acquired in 2015 and 2016. We used semivariogram indices (SIs) as spatial features and descriptive statistics as spectral features (SFs). We tested the performance of the method using three machine-learning algorithms: support vector machine (SVM), artificial neural network (ANN) and random forest (RF). The results showed that the combination of spatial and spectral information improved change detection by correctly classifying areas with seasonal changes in NDVI caused by vegetation phenology and areas with NDVI changes caused by human-induced disturbances. The use of semivariogram indices reduced the effects of vegetation phenology on change detection. The performance of the classifiers was generally comparable, but the SVM presented the highest overall classification accuracy (92.27%) when using the hybrid set of NDVI-derived spectral-spatial features. From the vegetated areas, 18.71% of changes were caused by human-induced disturbances between 2015 and 2016. The method is particularly useful for TSBs where vegetation exhibits strong seasonality and regularly spaced time series of satellite images are difficult to obtain due to persistent cloud cover.

Several methods have been proposed to delineate management zones in agricultural fields, which can guide interventions of the farmers to increase crop yield. In this study, we propose a new approach using remote sensing data to delineate management zones at three farm sites located in southern Brazil. The approach is based on the hypothesis that the measured aboveground biomass (AGB) of the cover crops is correlated with the measured cash-crop yield and can be estimated from surface reflectance and/or vegetation indices (VIs). Therefore, we used seven different statistical models to estimate AGB of three cover crops (forage turnip, white oats, and rye) in the season prior to cash-crop planting. Surface reflectance and VIs were used as predictors to test the performance of the models. They were obtained from high spatial and temporal resolution data of the PlanetScope (PS) constellation of satellites. From the time series of 30 images acquired in 2017, we used the PS data that matched the dates of the field campaigns to build the models. The results showed that the satellite AGB estimates of the cover crops at the date of maximum VI response at the beginning of the flowering stage were useful to delineate the management zones. The cover-crop AGB models that presented the highest coefficient of determination (R2) and the lowest root mean square (RMSE) in the validation and test datasets were Support Vector Machine (SVM), Cubist (CUB) and Stochastic Gradient Boosting (SGB). For most models and cover crops, the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI) were the two most important AGB predictors. At the date of maximum VI at the beginning of the flowering stage, the correlation coefficients (r) between the cover-crop AGB and the cash-crop yield (soybean and maize) ranged from +0.70 for forage turnip to +0.78 for rye. The fuzzy unsupervised classification of the cover-crop AGB estimates delineated two management zones, which were spatially consistent with those obtained from cash-crop yield. The comparison between both maps produced overall accuracies that ranged from 61.20% to 68.25% with zone 2 having higher cover-crop AGB and cash-crop yield than zone 1 over the three sites. We conclude that satellite AGB estimates of cover crops can be used as a proxy for generating management zone maps in agricultural fields. These maps can be further refined in the field with any other type of method and data, whenever necessary.

Remote sensing of disturbance in the savannas from Brazil is challenging, especially due to confounding effects of the vegetation phenology and natural soil exposure on the detection of clearing and fire events. In this study, we investigated the detection of disturbance over this global hotspot of biodiversity using seven vegetation indices (VIs) calculated from the Landsat time series (2017–2019) and the Continuous Change Detection and Classification (CCDC) algorithm. The selected VIs represented distinct biophysical characteristics of the savannas. We evaluated the effects of disturbance on these VIs and assessed the accuracy of CCDC-detection in 2019, considering individual VIs, ensemble VIs, and the type of disturbance (savanna clearing and fire). Finally, we analyzed the possible existence of seasonal patterns of disturbance in a study area located at the new agricultural frontier of the Cerrado biome. The results showed that the overall accuracy of CCDC detection of total disturbance ranged from 51.2% for the Green-Red Normalized Difference (GRND) to 65.9% for the Normalized Burn Ratio (NBR2). It increased to 71.2% for ensemble VIs, whose multivariate approach reduced the omission errors in the analysis when compared to the use of single VIs. For detecting events of savanna clearing and fire, the most important VIs used near-infrared and shortwave infrared reflectance bands on their formulations (NBR2, NBR, and Moisture Stress Index—MSI). The CCDC accuracy was generally higher for detecting clearing than for mapping burned areas. In contrast, the recorded date of disturbance occurrence was less precise for detecting clearing than for recording events caused by fire, especially due to the existence of some gradual processes of vegetation degradation until complete clearing. Our findings showed also the existence of a seasonal pattern of disturbance occurrence. Savanna clearing predominated in the transition from the rainy to the dry season (April to July) to open new areas for agriculture. It preceded most events of fire disturbance between August and October that occurred near the consolidated areas of agriculture and extended into the native vegetation areas. Results reinforce the importance of data-driven approaches for generating early warning alerts of disturbance in the Cerrado to be further checked in the field.

ABSTRACT The analysis of changes in species composition and vegetation structure in chronosequences improves knowledge on the regeneration patterns following land abandonment in the Amazon. Here, the objective was to perform floristic-structural analysis in mature forests (with/without timber exploitation) and secondary successions (initial, intermediate and advanced vegetation regrowth) in the Tapajós region. The regrowth age and plot locations were determined using Landsat-5/Thematic Mapper images (1984-2012). For floristic analysis, we determined the sample sufficiency and the Shannon-Weaver (H'), Pielou evenness (J), Value of Importance (VI) and Fisher's alpha (α) indices. We applied the Non-metric Multidimensional Scaling (NMDS) for similarity ordination. For structural analysis, the diameter at the breast height (DBH), total tree height (Ht), basal area (BA) and the aboveground biomass (AGB) were obtained. We inspected the differences in floristic-structural attributes using Tukey and Kolmogorov-Smirnov tests. The results showed an increase in the H', J and α indices from initial regrowth to mature forests of the order of 47%, 33% and 91%, respectively. The advanced regrowth had more species in common with the intermediate stage than with the mature forest. Statistically significant differences between initial and intermediate stages (p

Remote sensing techniques have been widely used in forestry studies as they allow evaluation and monitoring of large forested areas. The Parque Estadual do Turvo (PET) (17 491 ha) is the largest remaining tract of well-preserved subtropical deciduous forest in Southern Brazil, it constitutes the northern-most portion of the Misiones forest of Argentina (10 000 km²). The area is of great environmental importance and is suitable for conducting remote sensing studies using high or even coarse-to-moderate spatial resolution data and related vegetation indices. Both, reflectance values and vegetation indices are affected by external factors that modify the spectral response of surface elements. Among the factors that can induce errors in image interpretation are topographic effects that add spectral variability to satellite products. In addition, previous studies in subtropical forests have shown that the geometry of data acquisition also affects significantly the estimates of vegetation parameters derived from images acquired at off-nadir viewing or by sensors with large field-of-view (FOV)...

In times of global environmental change, the sustainability of human–environment systems is only possible through a better understanding of ecosystem processes. An assessment of anthropogenic environmental impacts depends upon monitoring natural ecosystems. These systems are intrinsically complex and dynamic, and are characterized by ecological gradients. Remote sensing data repeatedly collected in a systematic manner are suitable for describing such gradual changes over time and landscape gradients, e.g., through information on the vegetation’s phenology. Specifically, imaging spectroscopy is capable of describing ecosystem processes, such as primary productivity or leaf water content of vegetation. Future spaceborne imaging spectroscopy missions like the Environmental Mapping and Analysis Program (EnMAP) will repeatedly acquire high-quality data of the Earth’s surface, and will thus be extremely useful for describing natural ecosystems and the services they provide. In this conceptual paper, we present some of the preparatory research of the EnMAP Scientific Advisory Group (EnSAG) on natural ecosystems and ecosystem transitions. Through two case studies we illustrate the usage of spectral indices derived from multi-date imaging spectroscopy data at EnMAP scale, for mapping vegetation gradients. We thus demonstrate the benefit of future EnMAP data for monitoring ecological gradients and natural ecosystems.

Irrigation-induced salinization is an important land degradation process that affects crop yield in the Brazilian semi-arid region, and gypsum has been used as a corrective measure for saline soils. Fluvent soil samples (180) were treated with increasing levels of salinization of NaCl, MgCl2 and CaCl2. The salinity was gauged using electrical conductivity (EC). Gypsum was added to one split of these samples before they were treated by the saline solutions. Laboratory reflectance spectra were measured at nadir under a controlled environment using a FieldSpec spectrometer, a 250-W halogen lamp and a Spectralon panel. Variations in spectral reflectance and brightness were evaluated using principal component analysis, as well as the continuum-removed absorption depths of major features at 1450, 1950, 1750 and 2200 nm for both the gypsum-treated (TG) and non-treated (NTG) air-dried soil samples as a function of EC. Pearson’s correlation coefficients of reflectance and the band depth with EC were also obtained to establish the relationships with salinity. Results showed that NTG samples presented a decrease in reflectance and brightness with increasing CaCl2 and MgCl2 salinization. The reverse was observed for NaCl. Gypsum increased the spectral reflectance of the soil. The best negative correlations between reflectance and EC were observed in the 1500–2400 nm range for CaCl2 and MgCl2, probably because these wavelengths are most affected by water absorption, as Ca and Mg are much more hygroscopic than Na. These decreased after chemical treatment with gypsum. The most prominent features were observed at 1450, 1950 and 1750 nm in salinized-soil spectra. The 2200-nm clay mineral absorption band depth was inversely correlated with salt concentration. From these features, only the 1750 and 2200 nm ones are within atmospheric absorption windows and can be more easily measured using hyperspectral sensors.

We used Moderate Resolution Imaging Spectroradiometer (MODIS) data, processed by the multi−angle implementation of atmospheric correction (MAIAC) algorithm, to investigate the sensitivity of seven vegetation indices (VIs) to bidirectional reflectance distribution function (BRDF) effects in the dry season (June−September) of the Brazilian Amazon. The analysis was first performed over three sites, located from north to south of the Amazon, and then extended into the entire region. We inspected for differences in viewing−illumination parameters and pixel quality retrievals during MODIS data acquisition over the region. By comparing and correlating corrected and non−corrected data for bidirectional effects, we evaluated monthly changes in reflectance and VIs (2000−2014). Finally, we computed the effect size of the BRDF correction using non−parametric Mann−Whitney tests and Cohen’s r metrics. The results showed that the most anisotropic VIs were the enhanced vegetation index (EVI), photochemical reflectance index (PRI), and shortwave infrared normalized difference (SWND). These VIs presented the largest relative changes and the lowest correlation coefficients, between corrected and non−corrected data, because of the large effect size of the BRDF. The least anisotropic VI was the normalized difference water index (NDWI). The anisotropy of these VIs was stronger in the northern Amazon. It increased from the beginning to the end of the dry season, following changes in the relative azimuth angle (RAA) toward the BRDF hotspot in September. The modifications in the relative proportions of backscattering observations used in composite products caused a reflectance increase in all MODIS bands at the end of the dry season, especially in the near infrared (NIR). The reflectance decreased after BRDF correction. Because of the atmospheric effects, the view zenith angle (VZA) of the pixels selected in composite products decreased toward the south of the Amazon. In the southern Amazon, the seasonal amplitude in the solar zenith angle (SZA) reached values close to 18°. For the most anisotropic index, the BRDF correction removed, on average, 30% of the EVI signal in June, and 60% of the EVI signal in September, reducing dry season variations over time. The results reinforce the need for bidirectional correction of MODIS data before the seasonal and inter−annual analyses of the most anisotropic VIs.

Our limited understanding of the climate controls on tropical forest seasonality is one of the biggest sources of uncertainty in modeling climate change impacts on terrestrial ecosystems. Combining leaf production, litterfall and climate observations from satellite and ground data in the Amazon forest, we show that seasonal variation in leaf production is largely triggered by climate signals, specifically, insolation increase (70.4% of the total area) and precipitation increase (29.6%). Increase of insolation drives leaf growth in the absence of water limitation. For these non-water-limited forests, the simultaneous leaf flush occurs in a sufficient proportion of the trees to be observed from space. While tropical cycles are generally defined in terms of dry or wet season, we show that for a large part of Amazonia the increase in insolation triggers the visible progress of leaf growth, just like during spring in temperate forests. The dependence of leaf growth initiation on climate seasonality may result in a higher sensitivity of these ecosystems to changes in climate than previously thought.

Remote sensing techniques have been widely used in forestry studies because they allow evaluation and monitoring of large areas. The Parque Estadual do Turvo (PET) (17.491 ha) is the largest fragment of preserved subtropical deciduous forest of South Brazil, representing an extension of the Misiones forest in Argentina (10.000 km²). This area has great environmental importance and is adequate for performing remote sensing studies using high or even coarse-to-moderate spatial resolution data as well as related vegetation indices. Both, the reflectance and vegetation indices, are affected by external factors that change the spectral response of the surface components. Among the factors that can introduce errors in the interpretation of the images, topographic effects add spectral variability in satellite products. In addition, previous studies in subtropical forests showed that the geometry of data acquisition affects significantly the estimates of vegetation parameters derived from images acquired at off-nadir viewing or by large field-of-view (FOV) sensors. This study aimed to evaluate the magnitude of the bidirectional reflectance variations and of the derived vegetation indices as a function of local topography using high spatial resolution data acquired by the RapidEye constellation of satellites.

O ecossistema formado pela Floresta Estacional Decidual (FED) predomina na região do Alto Uruguai no Estado do Rio Grande do Sul, Brasil. O presente trabalho teve como objetivo analisar de forma descritiva a composição florística e fitossociológica dos componentes arbóreos de um fragmento de FED presente no Parque Estadual do Turvo (PET), localizado no Município de Derrubadas, RS (floresta do Yucumã). Utilizou-se o método de área fixa, sendo instaladas 18 unidades amostrais de 1.000 m2 de forma aleatória, orientadas de acordo com a estrada principal no parque. Todos os indivíduos arbóreos com diâmetro a 1,30 m do solo (DAP) superior a 10 cm foram medidos e identificados. Verificou-se a presença de 842 indivíduos, distribuídos em 32 famílias botânicas, 67 gêneros e 83 espécies (12% não foram identificados). As famílias que apresentaram maior riqueza de espécies foram Fabaceae, Euphorbiaceae, Meliaceae, Myrtaceae e Sapindaceae. O Índice de Shannon estimado foi de 3,72.

Reflectance, emissivity and elevation data of the sensor ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer)/Terra were used to characterize soil composition variations according to the toposequence position. Normalized data of SWIR (shortwave infrared) reflectance and TIR (thermal infrared) emissivity, coupled to a soil-fraction image from a spectral mixture model, were evaluated to separate bare soils from nonphotosynthetic vegetation. Regression relationships of some soil properties with reflectance and emissivity data were then applied on the exposed soil pixels. The resulting estimated values were plotted on the ASTER-derived digital elevation model. Results showed that the SWIR bands 5 and 6 and the TIR bands 10 and 14 measured the clay mineral absorption band and the quartz emissivity feature, respectively. These bands improved also the discrimination between nonphotosynthetic vegetation and soils. Despite the differences in pixel size and field sampling size, some soil properties were correlated with reflectance (R² of 0.65 for Al2O3 in band 6; 0.61 for Fe2O3 in band 3) and emissivity (R² of 0.65 for total sand fraction in the 10/14 band ratio). The combined use of reflectance, emissivity and elevation data revealed variations in soil composition with topography in specific parts of the landscape. From higher to lower slope positions, a general decrease in Al2O3 and increase in total sand fraction was observed, due to the prevalence of Rhodic Acrustox at the top and its gradual transition to Typic Acrustox at the bottom.Dados de reflectância, emissividade e elevação do sensor ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer)/Terra foram usados para caracterizar variações na composição dos solos com a posição em topossequências. Dados normalizados de reflectância do SWIR (shortwave infrared) e de emissividade do TIR (thermal infrared), integrados com a imagem fração-solo de um modelo de mistura espectral, foram avaliados para separar solos expostos de vegetação não fotossinteticamente ativa. Relações de regressão de alguns atributos dos solos com dados de reflectância e emissividade foram aplicadas sobre os pixels de solos expostos. Os valores estimados resultantes foram projetados sobre um modelo digital de elevação gerado a partir dos dados ASTER. Os resultados mostraram que as bandas 5 e 6 do SWIR e 10 e 14 do TIR mediram a feição de absorção das argilas e a de emissividade do quartzo, respectivamente. Essas bandas melhoraram também a discriminação entre vegetação não fotossinteticamente ativa e solos. Apesar das diferenças no tamanho dos pixels e dos locais de amostragem de campo, alguns atributos dos solos foram correlacionados com a reflectância (R² de 0,65 para Al2O3 na banda 6 e de 0,61 para Fe2O3 na banda 3) e a emissividade (R² de 0,65 para fração total de areia com a razão das bandas 10/14). O uso combinado dos dados de reflectância, emissividade e elevação revelou variações na composição dos solos com a topografia em porções específicas da cena. Das maiores para as menores elevações, um decréscimo no conteúdo de Al2O3 e aumento na fração total de areia foram geralmente observados. Isso ocorreu em razão do predomínio de Latossolo Vermelho-Escuro no topo e de sua gradual transição para Latossolo Vermelho-Amarelo na base.

Images acquired at the same day by the ETM+/Landsat-7 (30 m of spatial resolution) and MODIS/Terra (250 m) sensors were used to estimate areas of three major crops (soybean, sugarcane, and corn) with different landscape patterns in Southeastern Brazil. Majority filtering of ETM + classification results was applied to describe the behavior of 15 selected landscape metrics at distinct simulated spatial resolutions (90, 150, 210 and 270 m). By using regression models, the performance of MODIS and derived metrics to predict adequately the crop area, considering ETM+ data as reference, were analyzed. Results showed that the MODIS instrument overestimated the areas of soybean (15%) and sugarcane (1%), and underestimated the area of corn (12%). Multiple regression results indicated that coarse spatial resolution sensors can be used to predict adequately the area viewed by the 30 m spatial resolution instruments only for crops with low fragmentation pattern such as soybean. These sensors cannot be used to predict the area of corn due to aggregation pixel effects of the less fragmented crops (soybean and sugarcane) over the most fragmented one (corn), as demonstrated by the spatial resolution simulation using majority filtering of the ETM+ image. Landscape metrics improved MODIS area estimates only for sugarcane, as indicated by higher values of R² for multiple than for simple regression. Only a small set of metrics was select to compose the multiple regression models because most of them were not preserved across different spatial resolutions (30 m and 250 m).Imagens coletadas no mesmo dia pelos sensores ETM+/Landsat-7 (30 m de resolução espacial) e MODIS/Terra (250 m) foram utilizadas para estimar a área de três importantes culturas agrícolas (soja, cana-de-açúcar e milho) com diferentes padrões de paisagem no Sudeste Brasileiro. Filtragem de Maioria dos resultados da classificação da imagem ETM+ foi aplicada para descrever o comportamento de 15 métricas em diferentes simulações de resolução espacial (90, 150, 210 e 270 m). Utilizando modelos de regressão, o desempenho do MODIS e de suas métricas para predizer a área das culturas, considerando os dados ETM+ como referência, foi analisado. Os resultados mostraram que o sensor MODIS superestimou as áreas de soja (15%) e cana-de-açúcar (1%) e subestimou a área de milho (12%). A regressão múltipla indicou que sensores de resolução espacial grosseira podem ser usados para predizer adequadamente a área vista por instrumentos com 30 m de resolução espacial apenas para culturas com baixo padrão de fragmentação como soja. Estes sensores não podem predizer adequadamente a área de milho devido aos efeitos de agregação de pixels das culturas menos fragmentadas (soja e cana-de-açúcar) sobre a mais fragmentada (milho), conforme demonstrado pela simulação da resolução espacial por filtragem de maioria da imagem ETM+. As métricas da paisagem melhoraram as estimativas de área com o MODIS apenas para a cana-de-açúcar, conforme indicado por maiores valores de R² observados para regressão múltipla do que para regressão simples. Apenas um número pequeno de métricas foi selecionado para compor os modelos de regressão visto que a maior parte delas não foi preservada entre resoluções espaciais diferentes (30 e 250 m).

Water composition undergoes complex spatial and temporal variations throughout the central Amazon floodplain. This study analyzed the spectral mixtures of the optically active substances (OASs) in water with spaceborne hyperspectral images. The test site was located upstream the confluence of Amazon (white water) and Tapajós (clear-water) rivers, where two Hyperion images were acquired from the Earth Observing One (EO-1) satellite. The first image was acquired on September 16, 2001, during the falling water period of the Amazon River. The second image was acquired on June 23, 2005, at the end of the high water period. The images were pre-processed to remove stripes of anomalous pixels, convert radiance-calibrated data to surface reflectance, mask land, clouds and macrophytes targets, and spectral subset the data within the range of 457-885nm. A sequential procedure with the techniques Minimum Noise Fraction (MNF), Pixel Purity Index (PPI) and n-dimensional visualization of the MNF feature space was employed to select end-members from both images. A single set of end-members was gathered to represent the following spectrally unique OASs: clear-water; dissolved organic matter; suspended sediments; and phytoplankton. The Linear Spectral Unmixing algorithm was applied to each Hyperion image in order to map the spatial distribution of these constituents, in terms of sub-pixel fractional abundances. Results showed three patterns of changes in the water quality from high to falling flood periods: decrease of suspended inorganic matter concentration in the Amazon River; increase of suspended inorganic matter and phytoplankton concentrations in varzea lakes; and increase of phytoplankton concentration in the Tapajós River.

Composições de 16 dias de índices de vegetação do sensor MODerate resolution Imaging Spectroradiometer (MODIS), com resolução espacial de 1km, a bordo dos satélites TERRA e AQUA, foram usadas para caracterizar a dinâmica sazonal em 2004 de cinco fitofisionomias de Cerrado e analisar a sua separabilidade espectral. Os índices Normalized Difference Vegetation Index (NDVI) e Enhanced Vegetation Index (EVI), calculados a partir dos dados dos sensores de ambas as plataformas e de uma base comum de pixels, foram comparados entre si. Os resultados indicaram que: (a) dentre as fitofisionomias estudadas, a Floresta Estacional decídua apresentou uma dinâmica sazonal muito marcante em função da perda de folhas da estação chuvosa para a seca (substancial redução nos índices) e do rápido verdejamento com o início da precipitação no final de outubro (rápido incremento de NDVI e EVI); (b) o NDVI mostrou maior variabilidade entre as classes de vegetação do que o EVI apenas na estação seca; (c) a discriminação entre as fitofisionomias melhorou da estação chuvosa para a seca; (d) o NDVI foi mais eficiente do que o EVI para discriminar as classes de vegetação na estação seca, ocorrendo o contrário na estação chuvosa; e (e) na maioria das datas selecionadas para estudo, não houve diferenças estatisticamente significativas entre os índices de vegetação gerados de ambas as plataformas, apesar das variações na qualidade dos pixels selecionados para as composições de 16 dias e na geometria de iluminação e de visada.MODerate-resolution Imaging Spectroradiometer (MODIS) 16-day vegetation index composites with 1 km of spatial resolution from TERRA and AQUA satellites were used to characterize the seasonal dynamics of five Brazilian savanna physiognomies and to analyze their spectral separability in 2004. The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), using data from both platforms and from a common set of pixels, were compared to each other. The results showed that: (a) among the physiognomies under study, Dry Forest (Floresta Estacional decídua) presented a marked seasonal dynamics as a result of the leaf fall from the rainy to the dry season (strong decrease in the indices) and of the fast green up of vegetation with precipitation at the end of October (strong and rapid increase in NDVI and EVI values); (b) NDVI showed greater variability between the vegetation classes than EVI only in the dry season; (c) the discrimination between the physiognomies improved from the rainy to the dry season; (d) the NDVI was more efficient than EVI to separate the vegetation classes in the dry season, but the contrary was observed in the rainy season; and (e) for the majority of the dates under analysis, in spite of the variations in the quality of the pixels selected to compose the vegetation index MODIS product and in the Sun-view geometry, no statistically significant differences between the indices generated from both platforms were observed.

This study assessed sunglint effects in airborne high spatial and high spectral resolution images acquired by the SpecTIR sensor under different view-illumination geometries over the Brazilian Ibitinga reservoir (Case II waters). These effects were corrected using the Goodman et al. (2008) and the Kutser et al. (2009) methods, and a variant that used the continuum removal technique to calculate the oxygen absorption band depth. The performance of each method to removing sunglint effects was evaluated by a quantitative analysis of pre- and post-sunglint correction reflectance values (residual reflectance images). Furthermore, the analysis was supported by inspection of the reflectance differences along transects placed over homogeneous masses of waters or over specific portions of the scenes affected and non-affected by sunglint. Results showed that the algorithm of Goodman et al. (2008) produced better results than the other two methods, as it approached to zero the amplitude of the reflectance values between homogenous water masses free and contaminated by sunglint. The Kutser et al. (2009) method had also good performance, except for the most contaminated sunglint portions of the scenes. When the continuum removal technique was incorporated to the Kutser et al. (2009) method, results varied with the scene and were more sensitive to atmospheric correction artifacts and instrumental signal-to-noise ratio.

Water composition undergoes complex spatial and temporal variations throughout the central Amazon floodplain. This study analyzed the spectral mixtures of the optically active substances (OASs) in water with spaceborne hyperspectral images. The test site was located upstream the confluence of Amazon (white water) and Tapajós (clear-water) rivers, where two Hyperion images were acquired from the Earth Observing One (EO-1) satellite. The first image was acquired on September 16, 2001, during the falling water period of the Amazon River. The second image was acquired on June 23, 2005, at the end of the high water period. The images were pre-processed to remove stripes of anomalous pixels, convert radiance-calibrated data to surface reflectance, mask land, clouds and macrophytes targets, and spectral subset the data within the range of 457-885nm. A sequential procedure with the techniques Minimum Noise Fraction (MNF), Pixel Purity Index (PPI) and n-dimensional visualization of the MNF feature space was employed to select end-members from both images. A single set of end-members was gathered to represent the following spectrally unique OASs: clear-water; dissolved organic matter; suspended sediments; and phytoplankton. The Linear Spectral Unmixing algorithm was applied to each Hyperion image in order to map the spatial distribution of these constituents, in terms of sub-pixel fractional abundances. Results showed three patterns of changes in the water quality from high to falling flood periods: decrease of suspended inorganic matter concentration in the Amazon River; increase of suspended inorganic matter and phytoplankton concentrations in varzea lakes; and increase of phytoplankton concentration in the Tapajós River.

High spectral resolution data collected by the Hyperion/EO-1 instrument were converted into surface reflectance images and evaluated for the discrimination of five Brazilian sugarcane varieties. The relationships between some spectral indices and the causes of spectral variability from the sugarcane canopies were discussed. For discrimination purposes, multiple discriminant analysis was applied over the following set of variables submitted to a stepwise selection procedure: band reflectance values, ratios of reflectance, and several spectral indices potentially sensitive to changes in chlorophyll, leaf water and lignin-cellulose. Results showed that the cultivar SP80-1842 presented the lowest reflectance values, which facilitated discrimination from the remaining varieties. Discrimination between the other four varieties (RB72-454, SP80-1816, SP81-3250, and SP87-365) with similar reflectance required discriminant analysis, which produced an overall classification accuracy of 87.5% for a hold-out set of pixels. Misclassification of some sugarcane plantations was associated with varieties that presented the highest standard deviation values in their spectral response. For a given cultivar, such variability was not produced by single factor such as soil type, sugarcane cutting or in farm management practices. The comparison of ground truth data with the discriminant-derived classification image confirmed the potential of the Hyperion data and of the proposed approach to differentiate sugarcane varieties in southeastern Brazil.

Dados hiperespectrais coletados no Brasil pelo sensor AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) foram utilizados para a caracterização espectral de uma típica cena agropastoril e para testar o uso da técnica Spectral Feature Fitting (SFF) na identificação de minerais argilosos na imagem. Utilizou-se um modelo linear de mistura espectral, usando como membros de referência a vegetação verde e seca, a água, e os solos Nitossolo Vermelho, Latossolo Vermelho e Neossolo Quartzarênico órtico. Na identificação dos minerais, foram selecionados espectros de referência da biblioteca espectral do JPL/NASA. Os espectros dos pixels e das referências foram normalizados pelo método do contínuo espectral, entre 2.100 e 2.330 nm, e depois comparados quanto à similaridade com o uso da técnica SFF. A caulinita predomina na cena, cuja identificação remota é dependente do tipo de solo e das proporções dos componentes da cena no interior do pixels. Os melhores resultados foram obtidos em solos de reflectância intermediária a alta e em pixels com valor de abundância da fração solo superior a 70%. Isto ocorreu devido, respectivamente, à menor quantidade de substâncias opacas nestes solos e à redução nos pixels dos efeitos espectrais da lignina-celulose. Estes fatores tendem a mascarar as bandas de absorção das argilas.

Soil salinization due to irrigation affects agricultural productivity in the semi-arid region of Brazil. In this study, the performance of four computational models to estimate electrical conductivity (EC) (soil salinization) was evaluated using laboratory reflectance spectroscopy. To investigate the influence of bandwidth and band positioning on the EC estimates, we simulated the spectral resolution of two hyperspectral sensors (airborne ProSpecTIR-VS and orbital Hyperspectral Infrared Imager (HyspIRI)) and three multispectral instruments (RapidEye/REIS, High Resolution Geometric (HRG)/SPOT-5, and Operational Land Imager (OLI)/Landsat-8)). Principal component analysis (PCA) and the first-order derivative analysis were applied to the data to generate metrics associated with soil brightness and spectral features, respectively. The three sets of data (reflectance, PCA, and derivative) were tested as input variable for Extreme Learning Machine (ELM), Ordinary Least Square regression (OLS), Partial Least Squares Regression (PLSR), and Multilayer Perceptron (MLP). Finally, the laboratory models were inverted to a ProSpecTIR-VS image (400–2500 nm) acquired with 1-m spatial resolution in the northeast of Brazil. The objective was to estimate EC over exposed soils detected using the Normalized Difference Vegetation Index (NDVI). The results showed that the predictive ability of the linear models and ELM was better than that of the MLP, as indicated by higher values of the coefficient of determination (R2) and ratio of the performance to deviation (RPD), and lower values of the root mean square error (RMSE). Metrics associated with soil brightness (reflectance and PCA scores) were more efficient in detecting changes in the EC produced by soil salinization than metrics related to spectral features (derivative). When applied to the image, the PLSR model with reflectance had an RMSE of 1.22 dS·m−1 and an RPD of 2.21, and was more suitable for detecting salinization (10–20 dS·m−1) in exposed soils (NDVI < 0.30) than the other models. For all computational models, lower values of RMSE and higher values of RPD were observed for the narrowband-simulated sensors compared to the broadband-simulated instruments. The soil EC estimates improved from the RapidEye to the HRG and OLI spectral resolutions, showing the importance of shortwave intervals (SWIR-1 and SWIR-2) in detecting soil salinization when the reflectance of selected bands is used in data modelling.

O sensoriamento remoto termal tem sido utilizado em diversos estudos urbanos pela possibilidade de espacialização de dados de temperatura e de emissividade e de sua associação com o uso do espaço urbano. O objetivo deste trabalho foi desenvolver uma metodologia de conversão de Números Digitais (ND´s) de imagens do infravermelho termal (TIR) do sensor HSS (Hyperspectral Scanner System) em grandeza física (radiância). Além disto, foi feita a correção atmosférica da imagem em radiância usando o modelo de correção MODTRAN, visando estimar a temperatura da superfície e a emissividade espectral relativa com três métodos: Canal de Referência (REF), Normalização da Emissividade (NOR) e Emissividade ALPHA. Os resultados destes métodos foram comparados entre si, mostrando que a emissividade espectral dos principais materiais presentes na área de estudo foi consistente. Já para as temperaturas termodinâmicas destes mesmos materiais, determinadas com os métodos NOR e REF, verificou-se que houve diferença nos valores calculados de 1,2°C a mais para o REF, para todos os materiais estudados.

Dados de alta resolução espectral coletados pelo sensor Hyperion/EO-1 foram convertidos para imagens de reflectância de superfície e testados quanto à discriminação de cinco variedades brasileiras de cana-de-açúcar. As relações entre alguns índices espectrais e os fatores responsáveis pela variabilidade espectral registrada nos dosséis foram discutidos. Para fins de discriminação, análise discriminante múltipla foi aplicada com as seguintes variáveis submetidas a um processo de seleção passo-a-passo: valores de reflectância das bandas, razões de reflectância das bandas e vários índices espectrais sensíveis às variações no conteúdo de clorofila, água das folhas e lignina-celulose. Os resultados obtidos demonstraram que a variedade SP80-1842 apresentou uma reflectância menor do que as demais, o que facilitou sua discriminação na imagem. A discriminação das outras quatro variedades (RB72-454, SP80-1816, SP81-3250 e SP87-365), que possuem valores de reflectância similares, foi feita com análise discriminante. Esta técnica produziu uma exatidão de classificação de 87,5% para um conjunto independente de pixels. Erros de classificação de alguns talhões ocorreram principalmente para variedades que apresentaram valores maiores de desvio-padrão em sua resposta espectral. Para um dado cultivar, esta variabilidade espectral não pôde ser explicada pela influência de um único fator como tipo de solo, corte da cana ou práticas locais de manejo da cultura. A comparação entre os dados de campo e os dados resultantes da classificação das imagens por análise discriminante demonstrou o potencial dos dados coletados pelo sensor Hyperion e da abordagem metodológica proposta para diferenciar variedades de cana-de-açúcar no sudeste brasileiro.

Este trabalho analisou a relação entre índices de vegetação e precipitação, no Parque Estadual do Turvo (PET), localizado no noroeste do Estado do Rio Grande do Sul (RS). O Normalized Difference Vegetation Index (NDVI) e o Enhanced Vegetation Index (EVI) foram determinados a partir de imagens do Moderate Resolution Imaging Spectroradiometer (MODIS/Terra). Dados de precipitação do Tropical Rainfall Measuring Mission (TRMM) permitiram o cálculo do índice de estiagem. Foram identificados nove meses com precipitações abaixo da média, 92 meses com precipitações normais e 50 meses com valores acima da média. Os ciclos anuais de NDVI e EVI não apresentaram relação conclusiva com a precipitação anormal, mostrando que a floresta apresenta resiliência para suportar pequenos períodos de seca. O NDVI apresentou maior estabilidade no verão e no inverno.