Multiple Patterns of Forest Disturbance and Logging Shape Forest Landscapes in Paragominas, Brazil

Effectiveness and Economic Viability of Forest Certification: A Systematic Review

In the face of accelerating forest degradation and deforestation, forest certification emerged in the early 1990s as a voluntary and market-based mechanism to promote environmentally appropriate, socially beneficial, and economically viable management of the world’s forests. A key goal of forest certification is to reduce forest degradation and deforestation while enhancing forest enterprises’ economic viability. However, whether forest certification contributes to meeting such goals is unclear. We conducted a systematic literature review on such impacts, reviewing empirical studies published between 1993 and 2021 regarding the impact of forest certification on forest degradation, deforestation, and economic viability. Drawing on 98 empirical studies, we analyzed these impacts and provide an overview of the studies’ findings in terms of geographical distribution, indicators considered, and the certification schemes assessed. We found that the impact of forest certification on deforestation has been specifically understudied (n = 11) compared to forest degradation (n = 42) and economic viability (n = 45). On deforestation, studies have focused on Africa (45%) and South America (36%); on forest degradation, studies have focused on Europe (40%) and Asia (20%); on economic viability, studies have focused on Asia (33%), Europe (33%) and South America (20%). We found positive-neutral (54%; 46%) impacts on deforestation, positive-neutral-mixed (70%; 21%; 9%) impacts on forest degradation and positive-negative-mixed (50%; 33%; 17%) impacts on economic viability. We did not find clear evidence that impact is linked to a specific region or certification scheme. However, scarce evidence on the impacts of the Programme for the Endorsement of Forest Certification (PEFC), the application of various methods, and site-specific indicators in the individual studies challenge such comparison and hamper the generalization of findings. This systematic review provides an overview of the state-of-the-art research on the effectiveness and economic viability of forest certification, evaluates and discusses the current evidence base, and concludes with future research lines.

A Multivariate Geomorphometric Approach to Prioritize Erosion-Prone Watersheds

Soil erosion is considered one of the main degradation processes in ecosystems located in developing countries. In northern Mexico, one of the most important hydrological regions is the Conchos River Basin (CRB) due to its utilization as a runoff source. However, the CRB is subjected to significant erosion processes due to natural and anthropogenic causes. Thus, classifying the CRB’s watersheds based on their erosion susceptibility is of great importance. This study classified and then prioritized the 31 watersheds composing the CRB. For that, multivariate techniques such as principal component analysis (PCA), group analysis (GA), and the ranking methodology known as compound parameter (Cp) were used. After a correlation analysis, the values of 26 from 33 geomorphometric parameters estimated from each watershed served for the evaluation. The PCA defined linear-type parameters as the main source of variability among the watersheds. The GA and the Cp were effective for grouping the watersheds in five groups, and provided the information for the spatial analysis. The GA methodology best classified the watersheds based on the variance of their parameters. The group with the highest prioritization and erosion susceptibility included watersheds RH24Lf, RH24Lb, RH24Nc, and RH24Jb. These watersheds are potential candidates for the implementation of soil conservation practices.

Evaluation of Sentinel-1 and 2 Time Series for Land Cover Classification of Forest–Agriculture Mosaics in Temperate and Tropical Landscapes

Monitoring forest–agriculture mosaics is crucial for understanding landscape heterogeneity and managing biodiversity. Mapping these mosaics from remotely sensed imagery remains challenging, since ecological gradients from forested to agricultural areas make characterizing vegetation more difficult. The recent synthetic aperture radar (SAR) Sentinel-1 (S-1) and optical Sentinel-2 (S-2) time series provide a great opportunity to monitor forest–agriculture mosaics due to their high spatial and temporal resolutions. However, while a few studies have used the temporal resolution of S-2 time series alone to map land cover and land use in cropland and/or forested areas, S-1 time series have not yet been investigated alone for this purpose. The combined use of S-1 & S-2 time series has been assessed for only one or a few land cover classes. In this study, we assessed the potential of S-1 data alone, S-2 data alone, and their combined use for mapping forest–agriculture mosaics over two study areas: a temperate mountainous landscape in the Cantabrian Range (Spain) and a tropical forested landscape in Paragominas (Brazil). Satellite images were classified using an incremental procedure based on an importance rank of the input features. The classifications obtained with S-2 data alone (mean kappa index = 0.59–0.83) were more accurate than those obtained with S-1 data alone (mean kappa index = 0.28–0.72). Accuracy increased when combining S-1 and 2 data (mean kappa index = 0.55–0.85). The method enables defining the number and type of features that discriminate land cover classes in an optimal manner according to the type of landscape considered. The best configuration for the Spanish and Brazilian study areas included 5 and 10 features, respectively, for S-2 data alone and 10 and 20 features, respectively, for S-1 data alone. Short-wave infrared and VV and VH polarizations were key features of S-2 and S-1 data, respectively. In addition, the method enables defining key periods that discriminate land cover classes according to the type of images used. For example, in the Cantabrian Range, winter and summer were key for S-2 time series, while spring and winter were key for S-1 time series.

Comparing Sentinel-2 MSI and Landsat 8 OLI Imagery for Monitoring Selective Logging in the Brazilian Amazon

Imagery from medium resolution satellites, such as Landsat, have long been used to map forest disturbances in the tropics. However, the Landsat spatial resolution (30 m) has often been considered too coarse for reliably mapping small-scale selective logging. Imagery from the recently launched Sentinel-2 sensor, with a resampled 10 m spatial resolution, may improve the detection of forest disturbances. This study compared the performance of Landsat 8 and Sentinel-2 data for the detection of selective logging in an area located in the Brazilian Amazon. Logging impacts in seven areas, which had governmental authorization for harvesting timber, were mapped by calculating the difference of a self-referenced normalized burn ratio (ΔrNBR) index over corresponding time periods (2016–2017) for imagery of both satellite sensors. A robust reference dataset was built using both high- and very-high-resolution imagery. It was used to define optimum ΔrNBR thresholds for forest disturbance maps, via a bootstrapping procedure, and for estimating accuracies and areas. A further assessment of our approach was also performed in three unlogged areas. Additionally, field data regarding logging infrastructure were collected in the seven study sites where logging occurred. Both satellites showed the same performance in terms of accuracy, with area-adjusted overall accuracies of 96.7% and 95.7% for Sentinel-2 and Landsat 8, respectively. However, Landsat 8 mapped 36.9% more area of selective logging compared to Sentinel-2 data. Logging infrastructure was better detected from Sentinel-2 (43.2%) than Landsat 8 (35.5%) data, confirming its potential for mapping small-scale logging. We assessed the impacted area by selective logging with a regular 300 m × 300 m grid over the pixel-based results, leading to 1143 ha and 1197 ha of disturbed forest on Sentinel-2 and Landsat 8 data, respectively. No substantial differences in terms of accuracy were found by adding three unlogged areas to the original seven study sites.

Spatial Analysis of Temperate Forest Structure: A Geostatistical Approach to Natural Forest Potential

Forest ecosystems represent an important means of ecosystem services; they are key as carbon sinks, water collectors, soil stabilizers, suppliers of great biological diversity, among other benefits. In addition, regionalization based on forest conditions provides a valuable approach to understanding and analyzing spatial patterns, which is useful as a tool for the implementation of forest ecosystem protection and conservation programs. In this research, the structure of a temperate forest in the western Sierra Madre region of Mexico was analyzed and characterized. The study unit was the watershed and the analysis used a geospatial approach combined with multivariate techniques such as: Principal Component Analysis, Cluster Analysis (CA), Discriminant Analysis (DA) and Multivariate Analysis of Variance. We evaluated the relationships among spectral satellite data, thematic maps and structural forest variables. A total of 345 watersheds were grouped based on these variables. The grouping of watersheds under low, medium and high production conditions was carried out with CA, defining 3 groups. The validation of the grouping was performed through DA, estimating errors with the restitution method, as well as with the cross-validation method. Significant differences were found among the groups. The grouping of watersheds provides observable evidence of the variability of the forest condition throughout the area. This study allows identifying forest areas with different levels of productivity and can help to detect levels of vulnerability and ecological fragility in natural forests in temperate zones.