Automatic Delineation of Forest Patches in Highly Fragmented Landscapes Using Coloured Point Clouds

Abstract

Accurate mapping of landscape features is key for natural resources management and planning. For this purpose, the use of high-resolution remote sensing data has become widespread and is increasingly freely available. However, mapping some target features, such as small forest patches, is still a challenge. Standard, easily replicable, and automatic methodologies to delineate such features are still missing. A common alternative to automated methods is manual delineation, but this is often too time and resource intensive. We developed a simple and automatic method from freely available aerial light detection and ranging (LiDAR) and aerial ortho-images that provide accurate land use mapping and overcome some of the aforementioned limitations. The input for the algorithm is a coloured point cloud, where multispectral information from the ortho-images is associated to each LiDAR point. From this, four-class segmentation and mapping were performed based on vegetation indices and the ground-elevation of the points. We tested the method in four areas in the north-western Iberian Peninsula and compared the results with existent cartography. The completeness and correctness of our algorithm ranging between 78% and 99% in most cases, and it allows for the delineation of very small patches that were previously underrepresented in the reference cartography.

Identification of the Forest Cover Growth on Landscape Level from Aerial Laser Scanning Data

“…We present the results of studies with a similar focus in Table 4. The results we achieved were better than those in the study by [7] and comparable to the results according to [37] (overall accuracy of 93%), [38] (96%) and [20] (97%). In our study, the best results (overall accuracy of 98%) were achieved in area 1 (with a predominance of coniferous spruce stands).…”

Accuracy of Determination of Corresponding Points from Available Providers of Spatial Data—A Case Study from Slovakia

“…Laser scanning (LS) is an active method of RS of the Earth, which uses the reflection of electromagnetic radiation to capture the 3D geometry of objects, the surface, and the relief of the Earth's landscape sphere [6]. It is also referred to as LiDAR (Light Detection and Ranging) and is currently the most effective and progressive method of collecting a large amount of 3D data (10 4 -10 6 p/s) [7]. It has found use in various industries (geodesy, remote sensing (RS), archaeology, geology, transport, ecology, meteorology, forestry, and agriculture).…”

“…Airborne LS, due to its highly economical and time-consuming nature, is directly intended for mapping the 3D geometry of the surface and objects of large areas, even in the case of hard-to-access locations from the surface [8]. ALS is one of the active methods of RS, with the help of which it is possible to obtain extensive data (up to 2 × 10 5 p/s) for the creation of a digital relief model (DMR) of the country [7]. The advantage of this method is that it can capture the surface of the terrain even through vegetation, trees, or bushes, and it is widely used [9].…”

Correlation between Geochemical and Multispectral Patterns in an Area Severely Contaminated by Former Hg-As Mining

“…Given these considerations and in the context of soil pollution studies, there is a need to find appropriate non-invasive approaches that reduce the cost and time required for these campaigns. Remote sensing techniques are widely used in natural environment research, such as studies on forest 2 of 14 studies [7][8][9], relative climate change [10][11][12], topography [13][14][15][16][17], and precision agriculture [18,19], among others. Of note, in recent years, some studies have combined techniques of remote sensing and geochemistry, especially in areas affected by heavy metal pollution, either with satellites or high-altitude manned aircrafts [20][21][22][23] or unmanned aerial vehicles (UAVs) [24][25][26][27][28].…”