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Modern forestry systems rely on typologies of forest types (FTs). In Argentina, several proposals have been developed, but they lack unified criteria. The objective was to compare different approaches, specifically focusing on (i) phenoclusters (functional forests based on vegetation phenology variations and climate variables) and (ii) forest canopy cover composition by tree species. We conducted comparative uni-variate analyses using data from national forest inventories, forest models (biodiversity, carbon, structure), and regional climate. We assessed the performance of phenoclusters in differentiating the variability of native forests (proxy: forest structure), biodiversity (proxy: indicator species), and environmental factors (proxies: soil carbon stock, elevation, climate). Additionally, we proposed a simple FT classification methodology based on species composition, considering the basal area of tree species. Finally, we compared the performance of both proposals. Our findings showed that classifications based on forest canopy cover composition are feasible to implement in regions dominated by mono-specific forests. However, phenoclusters allowed for the increased complexity of categories at the landscape level. Conversely, in regions where multi-specific stands prevailed, classifications based on forest canopy cover composition proved ineffective; however, phenoclusters facilitated a reduction in complexity at the landscape level. These results offer a pathway to harmonize national FT classifications by employing criteria and indicators to achieve sustainable forest management and conservation initiatives. [ABSTRACT FROM AUTHOR]

Change detection methods are often incapable of accurately detecting changes within time series that are heavily influenced by seasonal variations. Techniques for de-seasoning time series or methods that apply the spatial context have been used to improve the results of change detection. However, few studies have explored Landsat's shortwave infrared channel (SWIR 2) to discriminate between seasonal changes and land use/land cover changes (LULCC). Here, we explored the effectiveness of Operational Land Imager (OLI) spectral bands and vegetation indices for detecting deforestation in highly seasonal areas of Brazilian savannas. We adopted object-based image analysis (OBIA), applying a multidate segmentation to an OLI time series to generate input data for discrimination of deforestation from seasonal changes using the Random Forest (RF) algorithm. We found adequate separability between deforested objects and seasonal changes using SWIR 2. Using spectral indices computed from SWIR 2, the RF algorithm generated a change map with an overall accuracy of 88.3%. For deforestation, the producer's accuracy was 88.0% and the user's accuracy was 84.6%. The SWIR 2 channel as well as the mid-infrared burn index presented the highest importance among spectral variables computed by the RF average impurity decrease measure. Our results give support to further change detection studies regarding to suitable spectral channels and provided a useful foundation for savanna change detection using an object-based method applied to Landsat time series. [ABSTRACT FROM AUTHOR]

In forested areas that experience strong seasonality and are undergoing rapid land cover conversion (e.g., Brazilian savannas), the accuracy of remote sensing change detection is affected by seasonal changes that are erroneously classified as having changed. To improve the quality and consistency of regionally important forest change maps, we aim to separate process related change (for example, spectral variability due to phenology) from changes related to deforestations or fires. Seasonal models are typically used to account for seasonality, but fitting a model is difficult when there are insufficient data points in the time series. In this research, we utilize remotely sensed data and related spectral trends and the spatial context at the object level to evaluate the performance of geostatistical features to reduce the impact of seasonality from the NDVI (Normalized Difference Vegetation Index) of Landsat time series. The study area is the São Romão municipality, totaling 2440 km2, and is part of the Brazilian savannas biome. We first create image objects via multiresolution segmentation, basing the objects on the characteristics found in the first image (2003) of the 13-year time series. We intersected the objects with the NDVI images in order to extract semivariogram indices, the RVF (Ratio Variance—First lag) and AFM (Area First lag—First Maximum), and spectral information (average and standard deviation of NDVI values) to generate the time series from these features and to derive Spatio-Temporal Metrics (change and trend) to train a Random Forest (RF) algorithm. The NDVI spatial variability, captured by the AFM semivariogram index time series produced the best result, reaching 96.53% of the overall accuracy (OA) to separate no-change from forest change, while the greatest inter-class confusion occurred using the average of the NDVI values time series (OA = 63.72%). The spatial context approach we presented is a novel approach for the detection of forest change events that are subject to seasonality (and possible miss-classification of change) and mitigating the effects of forest phenology without the need for specific de-seasoning models. [ABSTRACT FROM AUTHOR]

Here we model and describe the wood volume of Cerrado Sensu Stricto, a highly heterogeneous vegetation type in the Savanna biome, in the state of Minas Gerais, Brazil, integrating forest inventory data with spatial-environmental variables, multivariate regression, and regression kriging. Our study contributes to a better understanding of the factors that affect the spatial distribution of the wood volume of this vegetation type as well as allowing better representation of the spatial heterogeneity of this biome. Wood volume estimates were obtained through regression models using different environmental variables as independent variables. Using the best fitted model, spatial analysis of the residuals was carried out by selecting a semivariogram model for generating an ordinary kriging map, which in turn was used with the fitted regression model in the regression kriging technique. Seasonality of both temperature and precipitation, along with the density of deforestation, explained the variations of wood volume throughout Minas Gerais. The spatial distribution of predicted wood volume of Cerrado Sensu Stricto in Minas Gerais revealed the high variability of this variable (15.32 to 98.38 m3 ha-1) and the decreasing gradient in the southeast-northwest direction.