Estimation of dendrometric characteristics for Submontane Semidecidual Seasonal Forest using OLI and SRTM data
Name: ANNY FRANCIELLY ATAIDE GONÇALVES
Publication date: 20/02/2018
Advisor:
Name |
Role |
|---|---|
| GILSON FERNANDES DA SILVA | Advisor * |
Examining board:
| Name |
Role |
|---|---|
| ADRIANO RIBEIRO DE MENDONÇA | Internal Examiner * |
| ANDRÉ QUINTÃO DA ALMEIDA | Co advisor * |
| GILSON FERNANDES DA SILVA | Advisor * |
Summary: Brazil's forestry policy predict that all states of the federation should update the forest inventory. Linked to this, it is necessary to use techniques, such as remote sensing, that make it possible to obtain accurate information and reduce costs in the development of this activity. The objective of this study was to evaluate the use of Landsat 8 OLI sensor data and SRTM data in equations for estimation of variables basal area and volume of wood for a fragment of Submontane Semidecidual Seasonal Forest belonging to the Private Reserve of the Natural Heritage (RPPN) Cafundó, located in the municipality of Cachoeiro do Itapemirim, ES. The forest inventory was realized out in 25 plots of 1,000 m² (20 m x 50 m) and the estimates of basal area and volume of wood with bark were obtained by means of allometric equations. Subsequently, these estimates were related to the variables derived from the remote sensing, through the regression analysis. In the regression analysis, the dependent variables were the basal area and volume of bark wood, and the independent variables were the OLI sensor spectral bands, the ratio between bands, vegetation indices and relief characteristics extracted from the SRTM, tested for different spectral windows. The technique of selection of explanatory variables used was the exhaustive search and the statistical evaluation of the regression made use of the , RMSE (%), residue dispersion and Leave-one-out ( and RMSEcv) cross-validation. For the studied variables, it was observed that the 3 x 3 pixel spectral window was the most related to the data of basal area and volume of wood, and relief variables extracted from the SRTM presented good performance when combined with the spectral variables of the sensor OLI. For the basal area, the equation that best fit the data presented of 0,6554, of 0,6244, RMSE (%) of 14,53% and RMSEcv (%) of 18,15%. In relation to volume, the equation presented of 0,6039, of 0,5380, RMSE (%) of 23,03% and RMSEcv (%) of 30,30%. The estimation of the basal area and volume of wood for the Submontane Semidecidual Seasonal Forest fragment using spectral data presented satisfactory results, emphasizing the importance of topography in the prediction of these variables in the studied area.
Keywords: Forest Inventory, Remote Sensing, Basal Area, Volume of wood.
