Estimating Stand Height and Tree Density in Pinus taeda plantations using in-situ data, airborne LiDAR and k-Nearest Neighbor Imputation

ABSTRACT Accurate forest inventory is of great economic importance to optimize the entire supply chain management in pulp and paper companies. The aim of this study was to estimate stand dominate and mean heights (HD and HM) and tree density (TD) of Pinus taeda plantations located in South Brazil using in-situ measurements, airborne Light Detection and Ranging (LiDAR) data and the non- k-nearest neighbor (k-NN) imputation. Forest inventory attributes and LiDAR derived metrics were calculated at 53 regular sample plots and we used imputation models to retrieve the forest attributes at plot and landscape-levels. The best LiDAR-derived metrics to predict HD, HM and TD were H99TH, HSD, SKE and HMIN. The Imputation model using the selected metrics was more effective for retrieving height than tree density. The model coefficients of determination (adj.R2) and a root mean squared difference (RMSD) for HD, HM and TD were 0.90, 0.94, 0.38m and 6.99, 5.70, 12.92%, respectively. Our results show that LiDAR and k-NN imputation can be used to predict stand heights with high accuracy in Pinus taeda. However, furthers studies need to be realized to improve the accuracy prediction of TD and to evaluate and compare the cost of acquisition and processing of LiDAR data against the conventional inventory procedures.

Leukocytes Detection, Classification and Counting in Smears of Peripheral Blood / Detección, Clasificación y Conteo de Leucocitos en Frotis de Sangre Periférica

Using the k-NN classifier in combination with the first Minkowski metric, in addition to techniques of digital image processing, we developed a computational system platform-independent, which is able to identify, to classify and to count five normal types of leukocytes: neutrophils, eosinophils, basophils, monocytes and lymphocytes. It is important to emphasize that this work does not attempt to diferentiate between smears of leukocytes coming from healthy and sick people; this is because most diseases produce a change in the differential count of leukocytes rather than in theirs forms. In the other side, the system could be used in emerging areas such as the topographic hematology and the chronobiology.

Mediante un clasificador k-NN en combinación con la primera métrica de Minkowski y técnicas de procesamiento digital de imágenes, se desarrolló un sistema computacional independiente de la plataforma, capaz de identificar, clasificar y contar cinco formas normales de leucocitos: neutrófilos, eosinófilos, basófilos, monocitos y linfocitos. Es importante enfatizar que este trabajo no intenta diferenciar entre muestras de leucocitos provenientes de gente sana y enferma, debido a que la mayoría de las enfermedades se detectan principalmente por un cambio en el conteo diferencial de leucocitos más que por cambios en su forma. Finalmente, el contador de leucocitos puede ser usado en áreas emergentes como la hematología topográfica y la cronobiología.