Low budget ranging for forest management: A Microsoft Kinect study

Organisator Laboratory of Geo-information Science and Remote Sensing

di 1 oktober 2013 14:15

Locatie Atlas, building number 104
Droevendaalsesteeg 4
6708 PB Wageningen
Zaal/kamer 2

By Tim Brouwer (the Netherlands)


Recent advancements in forestry science provide foresters with technology to accurately measure and analyze tree metric data within almost any forest stand. Ranging technology and other advancements in the field of remote sensing have increased the efficiency from measuring individual trees significantly in the last decade. However small scale foresters are having trouble keeping up with the efficiency from large forestry companies using the expensive ranging technology such as LiDAR to make the lowest cost production ratio possible.

A smaller less expensive device that could still acquire accurate tree metric data would benefit small scale foresters to compete larger forestry companies. Light Coding, which is used within devices such as the Microsoft Kinect, can be used to collect data which could be useful in forestry assessments and decision making software.

Forestry data was obtained from different forest stands near Wageningen, the Netherlands. Two production forest stands and two mixed heterogeneous forest stands were measured. The data obtained was subjected to numerous calibrations and modifications for optimization. It was proven that Microsoft Kinect data can be used in forestry applications. Tree metric data was able to be extracted in coniferous, deciduous, shrub and mixed forest stands. However the Microsoft Kinect has limitations, mixed and shrub forest stands are difficult to distinguish and higher light ranging resolution is needed for better extraction.

A forestry assessment decision making model was created to test the both Microsoft Kinect and LiDAR data. This model was based upon growth factors and other forest principles. Forestry data obtained in production stands was used in decision making software to produce forest management schemes for deciduous and coniferous species. Nevertheless, tree metric data obtained by the Microsoft Kinect has proven to be useful for the automatization of forestry assessment.

The evaluation of the data proved that data within standard forestry settings could be used for forestry assessments. LiDAR data was compared with Kinect data and for production stands had roughly the same result for both stand as tree characteristics. The manual assessment proved that the Microsoft Kinect data within the decision making software can make decisions as accurately and precise as expert forestry assessments.

Keywords: Microsoft Kinect, precision forestry, ranging applications, forestry modeling, tree metrics, forestry assessment, decision science, remote sensing, structured light coding.