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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.

Logging, including selective and illegal activities, is widespread, affecting the carbon cycle and the biodiversity of tropical forests. However, automated approaches using very high resolution (VHR) satellite data (≤1 m spatial resolution) to accurately track these small-scale human disturbances over large and remote areas are not readily available. The main constraint for performing this type of analysis is the lack of spatially accurate tree-scale validation data. In this study, we assessed the potential of VHR satellite imagery to detect canopy tree loss related to selective logging in closed-canopy tropical forests. To do this, we compared the tree loss detection capability of WorldView-2 and GeoEye-1 satellites with airborne LiDAR, which acquired pre- and post-logging data at the Jamari National Forest in the Brazilian Amazon. We found that logging drove changes in canopy height ranging from −5.6 to −42.2 m, with a mean reduction of −23.5 m. A simple LiDAR height difference threshold of −10 m was enough to map 97% of the logged trees. Compared to LiDAR, tree losses can be detected using VHR satellite imagery and a random forest (RF) model with an average precision of 64%, while mapping 60% of the total tree loss. Tree losses associated with large gap openings or tall trees were more successfully detected. In general, the most important remote sensing metrics for the RF model were standard deviation statistics, especially those extracted from the reflectance of the visible bands (R, G, B), and the shadow fraction. While most small canopy gaps closed within ~2 years, larger gaps could still be observed over a longer time. Nevertheless, the use of annual imagery is advised to reach acceptable detectability. Our study shows that VHR satellite imagery has the potential for monitoring the logging in tropical forests and detecting hotspots of natural disturbance with a low cost at the regional scale.

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.

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.