This study intended to develop a road network planning for timber harvesting in tropical forests in Peru using georeferenced and field data and Geographic Information System (GIS). The Digital Elevation Model (DEM) Alos Palsar 12.5 m was used. The DEM was processed to generate the hydrography and terrain slope maps. A weighted raster was created using overlapping weights of the slope raster and the hydrography map. We created a least cost path raster by using the weighted raster origin and destination points. We used a Triangular Irregular Network (TIN) to validate the Least Cost Path. In addition, histograms of the trajectory of each path with altitude and slope values ​​ were generated. We observed that the forest road planning using GIS provided better definition (economically and environmentally) of road networks in our forest site than those traditionally defined using conventional mapping techniques.

Several studies have assessed forest disturbance in tropical forests using Landsat imagery. However, the spatial resolution (30 m) of Landsat images has often been considered too coarse to accurately detect the extent and impacts of selective logging. The Sentinel-2 satellite launched in 2015 has been providing images at spatial resolutions of 10 to 20 m and those images have shown an improved potential for detecting forest disturbances in tropical regions. We compared Landsat-8 and Sentinel-2 imagery for detecting selective logging in a rain forest site in the Brazilian Amazon. The aerosol-free modified soil adjusted vegetation index (MSAVI_af) was retrieved from the satellite images acquired in August 2020 immediately following logging. A robust reference dataset of very-high-resolution imagery (0.5 m) acquired using a complementary metal oxide semiconductor sensor (visible bands) onboard of an unmanned aerial vehicle was used to image the area of interest and a map derived from it was used to assess the classification accuracies made using satellite-derived data. The overall accuracy of the classified Sentinel-2 and Landsat-8 images varied between 54% and 83%, depending on the applied classification parameters for distinguishing undisturbed from disturbed forest canopy. Images acquired using the UAV allowed us to detect subtle impacts of canopy openings by selective logging activities. Images acquired using the UAV allowed the detection of small canopy openings, but not Sentinel-2 or Landsat-8. Sentinel-2 provided more details of canopy disturbances than Landsat image. Our classification approach is fully implementable on the Google Earth Engine platform and is a promising technique to monitor selective logging impacts in tropical forests.

The effective management of disturbed forests requires adequate knowledge of forest dynamics. In this study, we assessed the changes in a managed forest using 18 permanent 1 ha plots located in a ‘terra firme’ tropical rainforest in the Eastern Amazon (Paragominas, Brazil). All individuals with a diameter at breast height (DBH) ≥ 20 cm were evaluated in two separate assessments conducted in 2006 and 2016. The results show that, ten years after logging, the managed forest exhibits an imbalance between recruitment (1.54% per year) and mortality (2.23% per year) rates, indicating that it is still in the process of recovering its structure. Nevertheless, biomass tended to increase after logging (28.49 tons per hectare). The characteristics of these changes suggest that the forest is undergoing a silvigenetic process driven by the effects of logging. Furthermore, our observations indicate that the forest remains active and has sufficient potential for new timber production at the end of the cutting cycle, considering the same species and tree sizes.

We compared sampling techniques in a managed native forest in Paragominas, Pará, Brazil. Our goal in this study was to evaluate the feasibility of using Double Sampling (DS) and Sampling with Partial Replacement (SPR), when compared to Continuous Forest Inventory (CFI), to estimate the wood stock for trees with DBH ≥ 20 cm in a managed forest. In our results, DS had the best volume prediction, generating a sampling error of 5.20% (11.48 m³ ha^-1) on the second occasion 3.86% (8.78 m³ ha^-1). The average volume increment, estimated for the forest in the monitored period (2014-2016) was 6.88 m³ ha^-1, with a relative sampling error of 63.09%. Therefore, as an alternative and of low cost, we suggest using DS in successive forest inventories in monitoring areas of forest resources in the Brazilian Amazon.