Forest Density Estimation

Geostatistically estimation and mapping of forest stock in a natural unmanaged forest in the Caspian region of Iran

Estimation and mapping of forest resources are preconditions for management, planning and research. In this study, we applied kriging interpolation of geostatistics for estimation and mapping of forest stock at-tributes in a natural, uneven-aged, unmanaged forest in the Caspian region of northern Iran. The site of the study has an area of 516 ha and an elevation that ranges from 1100 to 1450 m ...

Comparing NDVI and RVI for forest density estimation and their relationships with rainfall (Case study: Malekshahi, Ilam Province)

Estimation of Density using Plotless Density Estimator Criteria in Arasbaran Forest

    Sampling methods have a theoretical basis and should be operational in different forests; therefore selecting an appropriate sampling method is effective for accurate estimation of forest characteristics. The purpose of this study was to estimate the stand density (number per hectare) in Arasbaran forest using a variety of the plotless density estimators of the nearest neighbors sampling me...

Spatial variability and estimation of tree attributes in a plantation forest in the Caspian region of Iran using geostatistical analysis

This research was conducted to investigate spatial variability and estimate tree attributes in a plantation forest in the Caspian region of Iran using geostatistical analysis. Sampling was performed based on a 50m?125m systematic grid in a maple stand (Acer velutinum Boiss) 18 years of age using circular samples of 200m2 area. Totally, 96 sample plots were measured in 63 hectares and 14.25 he...

All together now: Simultaneous Detection and Continuous Pose Estimation using a Hough Forest with Probabilistic Locally Enhanced Voting

Simultaneous object detection and pose estimation is a challenging task in computer vision. In this paper, we tackle the problem using Hough Forests. Unlike most methods in the literature, we focus on the problem of continuous pose estimation. Moreover, we aim for a probabilistic output. We first introduce a new pose purity criterion for splitting a node during the forest training. Second, we p...