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Impact of precision management on tropical forests Timber forest management is an activity that promotes the maintenance of forest cover in primary forest areas in Brazil. Currently, management techniques are being improved by the development of new geoprocessing technologies such as the Global Positioning Satellite System (GNSS), Airborne Lidar Scanning (ALS) and Remotely Piloted Aircraft (ARPs). The general objective of this work is to present the principles of precision forest management applied to the Amazon region and evaluate the reduction of sampling intensity in the forest inventory from vegetation stratification using Lidar variables. For this, was used a demonstration area of precision forest management located in the experimental field of Embrapa, in Rio Branco, Acre. The study was located in a continuous area of 800 hectares, in open ombrphylous forest with presence of palms, bamboo and dense forest portions. Two data collections were carried out in this area: the census inventory of trees with DBH ≥ 40 cm, which the sectional area of each tree was calculated; and the Lidar flight made at an altitude of 600 meters, with footprint of 30 cm and density of 14 pts.m. From the Lidar data were processed the digital surface models (DSM), terrain (DTM) and height (CHM), CHmean (average canopy height), roughness (standard deviation of the canopy), CHopenness (canopy openness of 15 meters), LAI (leaf area index) and LAIunder (LAI between 5 to 12 meters of height). In chapter 01, the data were analyzed in order to demonstrate the application of Lidar to improve the technical activities of the forest management in the Amazon, being: (i) macroplanning of the forest management area (AMF); (ii) micro-planning of the land and hydrography of the annual production unit (UPA); (iii) forest census and sampling; (iv) exploration infrastructure planning; and (v) monitoring. The analysis performed on the data of chapter 02 were: (i) estimation of the structural attributes derived from the field inventory and of the return cloud Lidar for three cell sizes; (ii) Pearson correlation between the field and canopy attributes derived from the Lidar; (iii) cluster analysis for classify strata by the Ward method; (iv) validation (ANOVA) and characterization of stratas (PCA); and (v) reduction of sample estimation intensity by a computer simulation. As result, the generated maps show the accuracy and quality of the Lidar data for mapping of annual production units within the area of forest management, microzoning of the topography, drainage network modeling, identification of rugged relief areas, APP boundary and planning the exploration infrastructure respecting the environmental conditions and timber potential of the study area. The average height of the forest canopy (CHmean), obtained with the Lidar, presented the highest correlation (r = 0.74) with the basal area and was used to stratify the vegetation in four forest patterns, with significant difference between them for the field variable. The stratification with CHmean distinguished the forest well for its structural variation gradient and vegetation density profile. The simulation of the stratified inventory versus the completely randomized inventory evidenced the ability of the Lidar Altmd variable to reduce the number of plots by up to 41% when using 4 strata for the same relative uncertainty of 10%. The use of a single canopy metric derived from Lidar was able to stratify an area of primary rainforest, in addition to generating additional information such as understory leaf area index and the terrain model that can assist in decision making for management, conservation and monitoring of tropical forests.

Impacto do manejo de precisão em florestas tropicais