Mapping Net Stocked Plantation Area for Small-Scale Forests in New Zealand Using Integrated RapidEye and LiDAR Sensors

Abstract

Abstract:In New Zealand, approximately 70% of plantation forests are large-scale (over 1000 ha) with accurate resource description. In contrast, the remaining 30% of plantation forests are small-scale (less than 1000 ha). It is forecasted that these small-scale forests will supply nearly 40% of the national wood production in the next decade. However, in-depth description of these forests, especially those under 100 ha, is very limited. This research evaluates the use of remote sensing datasets to map and estimate the net stocked plantation area for small-scale forests. We compared a factorial combination of two classification approaches (Nearest Neighbour (NN), Classification and Regression Tree (CART)) and two remote sensing datasets (RapidEye, RapidEye plus LiDAR) for their ability to accurately classify planted forest area. CART with a combination of RapidEye and LiDAR metrics outperformed the other three combinations producing the highest accuracy for mapping forest plantations (user's accuracy = 90% and producer's accuracy = 88%). This method was further examined by comparing the mapped plantations with manually digitised plantations based on aerial photography. The mapping approach overestimated the plantation area by 3%. It was also found that forest patches exceeding 10 ha achieved higher conformance with the digitised areas. Overall, the mapping approach in this research provided a proof of concept for deriving forest area and mapping boundaries using remote sensing data, and is especially relevant for small-scale forests where limited information is currently available.

Regional Forest Volume Estimation by Expanding LiDAR Samples Using Multi-Sensor Satellite Data

“…LiDAR is capable of directly capturing the three-dimension information on forest structure via actively transmitting laser pulses that interact with forest structure and then receiving the return signals. It has evolved into the preeminent remotely sensed platform to characterize detailed information of forest attributes spatially, since the end of the last century [10][11][12][13][14][15][16][17][18] due to its high precision and flexibility critical for operational forest management, and it has been extensively adopted for artificial or natural forest attributes monitoring on the individual tree-level [19][20][21] and small-scale [22][23][24]. Moreover, forest volume, as one of the structure parameters, was often accurately estimated by LiDAR [25], for example, Clementel et al [26] have carried out statistical models combined with medium-resolution LiDAR to produce timber volume mapping, and Lo et al [19] demonstrated tree growth competition index (LCI) derived from LiDAR scanning while using a rasterized canopy height model (multilevel morphological active-contour algorithm) was a key factor for forest volume estimation.…”

Taking a closer look at invasive alien plant research: A review of the current state, opportunities, and future directions for UAVs

“…Since the earliest remote sensing studies, pixel‐based image analysis has been the mainstay of automated image classification (Duro, Franklin, & Dubé, ), although more recently OBIA has become increasingly popular (Blaschke, ). Several studies have compared the two methods (Castillejo‐González et al, ; Cleve, Kelly, Kearns, & Moritz, ; Duro et al, ; Whiteside, Boggs, & Maier, ; Yu et al, ) and have frequently found OBIA to be more accurate (Xu, Morgenroth, & Manley, ); but this is not true in all cases (Duro et al, ). A UAV‐based IAP study (Mafanya et al, ) compared both methods and found that the accuracy of was similar.…”

Detection of Very Small Tree Plantations and Tree-Level Characterization Using Open-Access Remote-Sensing Databases

“…Laser pulses can penetrate the canopy permitting the 3D imaging of the vertical strata of the vegetation. The study by Xu et al [29], which focuses on data provided by the 5 m-spatial resolution imagery of RapidEye and by aircraft-LiDAR in radiata pine stands, stands out in the field of LiDAR-based detection of very small, forested areas. A different approach was followed by Palenichka et al [30], who developed an algorithm for multi-scale segmentation of forested areas, from a stand level to an individual-tree level.…”