Greenhouse Mapping and Crop Type Classification for Small-Scale FarmsUsing Airborne Laser Scanning
Journal
Canadian Journal of remote SenSing
Journal Volume
51
Journal Issue
1
Start Page
2551528
ISSN
0703-8992
Date Issued
2025-12-31
Author(s)
Abstract
The increasing frequency of extreme climatic events has resulted in significant crop losses,prompting many farmers to adopt greenhouses as a climate adaptation strategy. Greenhouses,constructed with transparent materials to allow sunlight penetration, are widely used forhigh-quality vegetable and fruit cultivation. These structures have both upper and lowerlayers; airborne laser scanning can penetrate the upper plastic layer and detect crop in thelower layer. This study analyzed 222 small-scale greenhouses in Taiwan, covering a total areaof 1,443 ha. ALS data were used to derive four indices: normalized digital surface model, firstecho intensity, laser penetration index, and surface roughness. These indices were used toclassify greenhouse areas using a support vector machine, achieving an overall accuracy of87.32% and an F1-score of 0.93. A cloth simulation filter was then applied to separate pointdata into upper and lower layers, enabling the removal of upper-layer points. Greenhousearea crops were further classified into bare ground, tall crops, low-lying crops, and mixedcrops, with 199 greenhouse areas correctly identified and an overall accuracy of 92.56%. TheF1-scores for each crop class ranged from 0.87 to 0.97. This method accurately reflectedactual cultivation conditions within the GAs.
Subjects
greenhouse map
greenhousecrop identification
airborne lidar
false-color alS image
SDGs
Publisher
Taylor & Francis
Type
journal article