Spartina alterniflora (S. alterniflora) is one of the worst plant invaders in the coastal wetlands of China. Accurate and repeatable mapping of S. alterniflora invasion is essential to develop cost-effective management strategies for conserving native biodiversity. Traditional remote-sensing-based mapping methods require a lot of fieldwork for sample collection. Moreover, our ability to detect this invasive species is still limited because of poor spectral separability between S. alterniflora and its co-dominant native plants. Therefore, we proposed a novel scheme that uses an ensemble one-class classifier (EOCC) in combination with phenological Normalized Difference Vegetation Index (NDVI) time-series analysis (TSA) to detect S. alterniflora. We evaluated the performance of the EOCC algorithm in two scenarios, i.e., single-scene analysis (SSA) and NDVI-TSA in the core zones of Yancheng National Natural Reserve (YNNR). Meanwhile, a fully supervised classifier support vector machine (SVM) was tested in the two scenarios for comparison. With these scenarios, the crucial phenological stages and the advantage of phenological NDVI-TSA in S. alterniflora recognition were also investigated. Results indicated the EOCC using only positive training data performed similarly well with the SVM trained on complete training data in the YNNR. Moreover, the EOCC algorithm presented a more robust transferability with notably higher classification accuracy than the SVM when being transferred to a second site, without a second training. Furthermore, when combined with the phenological NDVI-TSA, the EOCC algorithm presented more balanced sensitivity–specificity result, showing slightly better transferability than it performed in the best phenological stage (i.e., senescence stage of November). The achieved results (overall accuracy (OA), Kappa, and true skill statistic (TSS) were 92.92%, 0.843, and 0.834 for the YNNR, and OA, Kappa, and TSS were 90.94%, 0.815, and 0.825 for transferability to the non-training site) suggest that our detection scheme has a high potential for the mapping of S. alterniflora across different areas, and the EOCC algorithm can be a viable alternative to traditional supervised classification method for invasive plant detection.

Information pertaining to forest timber volume is crucial for sustainable forest management. Remotely-sensed data have been incorporated into operational forest inventories to serve the need for ever more diverse and detailed forest statistics and to produce spatially explicit data products. In this study, data derived from airborne laser scanning and image-based point clouds were compared using three volume estimation methods to aid wall-to-wall mapping of forest timber volume. Estimates of forest height and tree density metrics derived from remotely-sensed data are used as explanatory variables, and forest timber volumes based on sample field plots are used as response variables. When compared to data derived from image-based point clouds, airborne laser scanning produced slightly more accurate estimates of timber volume, with a root mean square error (RMSE) of 26.3% using multiple linear regression. In comparison, RMSEs for volume estimates derived from image-based point clouds were 28.3% and 29.0%, respectively, using Semi-Global Matching and enhanced Automatic Terrain Extraction methods. Multiple linear regression was the best-performing parameter estimation method when compared to k-Nearest Neighbour and Support Vector Machine. In many countries, aerial imagery is acquired and updated on regular cycles of 1–5 years when compared to more costly, once-off airborne laser scanning surveys. This study demonstrates point clouds generated from such aerial imagery can be used to enhance the estimation of forest parameters at a stand and forest compartment level-scale using small area estimation methods while at the same time achieving sampling error reduction and improving accuracy at the forest enterprise-level scale.

The project PROTECT^FOREST deals with improvements in early bark beetle (e.g., Ips typographus and Pityogenes chalcographus) detection to allow for fast and effective response to initial infestation. The removal of trees in the early infestation stage can prohibit bark beetle population gradation and successive timber price decrease. A semiconductor gas sensor array was tested in the lab and attached to a drone under artificial and real-life field conditions. The sensor array was able to differentiate between α-pinene amounts and between different temperatures under lab conditions. In the field, the sensor responded to a strong artificial α-pinene source. The real-life field trial above a spruce forest showed preliminary results, as technical and environmental conditions compromised a proof of principle. Further research will evaluate the detection rate of infested trees for the new proposed sensor concept.

Digital stereo aerial photographs are periodically updated in many countries and offer a viable option for the regular update of information on forest variables. We compared the potential of image-based point clouds derived from three different sets of aerial photographs with airborne laser scanning (ALS) to assess plot-level forest attributes in a mountain environment. The three data types used were (A) high overlapping pan-sharpened (80/60%); (B) high overlapping panchromatic band (80/60%); and (C) standard overlapping pan-sharpened stereo aerial photographs (60/30%). We used height and density metrics at the plot level derived from image-based and ALS point clouds as the explanatory variables and Lorey’s mean height, timber volume, and mean basal area as the response variables. We obtained a RMSE = 8.83%, 29.24% and 35.12% for Lorey’s mean height, volume, and basal area using ALS data, respectively. Similarly, we obtained a RMSE = 9.96%, 31.13%, and 35.99% and RMSE = 11.28%, 31.01%, and 35.66% for Lorey’s mean height, volume and basal area using image-based point clouds derived from pan-sharpened stereo aerial photographs with 80/60% and 60/30% overlapping, respectively. For image-based point clouds derived from a panchromatic band of stereo aerial photographs (80%/60%), we obtained an RMSE = 10.04%, 31.19% and 35.86% for Lorey’s mean height, volume, and basal area, respectively. The overall findings indicated that the performance of image-based point clouds in all cases were as good as ALS. This highlights that in the presence of a highly accurate digital terrain model (DTM) from ALS, image-based point clouds offer a viable option for operational forest management in all countries where stereo aerial photographs are updated on a routine basis.

The project PROTECT^FOREST deals with the improvement of early bark beetle detection to allow a fast and effective response to initial infestation. The removal of trees in the early infestation stage can prohibit bark beetle population gradation and successive timber price decrease. A semi-conductor gas sensor array was tested in the lab and attached to a drone under artificial and real-life field conditions. The sensor array was able to differentiate between α-pinene amounts and between different temperatures under lab conditions. In the field, the sensor responded to a strong artificial α-pinene source. The real-life field trial showed preliminary results, as technical and environmental conditions did compromise proof of principle. Further research will evaluate the detection rate of infested trees with the new proposed sensor concept.