The effective and efficient monitoring of revegetation outcomes is a key component of ecosystem restoration. Monitoring often involves labour intensive manual methods which can be difficult to deploy when sites are inaccessible or involve large areas of revegetation. This study aimed to identify plant species and quantify α-diversity index on a sub-meter scale at Manlailiang Mine Site in Northwestern China using unmanned aerial vehicles (UAVs) as a means to semi-automate large-scale vegetation monitoring. UAVs equipped with multispectral sensors were combined with three industry-standard supervised classification algorithms (support vector machine (SVM), maximum likelihood, and artificial neural network) to classify plant species. Spectral vegetation indices (NDVI, DVI, VDVI, SAVI, MSAVI, EXG - EXR) were used to assess vegetation diversity obtained from on-ground survey plot data (Margalef, Pielou, Simpson, Shannon indices). Our results showed that SVM outperformed other algorithms in species identification accuracy (overall accuracy 84%). Significant relationships were observed between vegetation indices and diversity indices, with DVI performing significantly better than many more commonly used indices such as NDVI. The findings highlight the potential of combining UAV multispectral data, spectral vegetation indices and ground surveys for effective and efficient fine-scale monitoring of vegetation diversity in the ecological restoration of mining areas. This has significant practical benefits for improving adaptive management of restoration through improved monitoring tools.

Invertebrates are important for restoration processes as they are key drivers of many landscape-scale ecosystem functions, including pollination, nutrient cycling and soil formation. However, invertebrates are often overlooked in restoration monitoring because they are highly diverse, poorly described, and time-consuming to survey, and require increasingly scarce taxonomic expertise to enable identification. DNA metabarcoding is a relatively new tool for rapid survey that is able to address some of these concerns, and provide information about the taxa with which invertebrates are interacting via food webs and habitat. Here we evaluate how invertebrate communities may be used to determine ecosystem trajectories during restoration. We collected ground-dwelling and airborne invertebrates across chronosequences of mine-site restoration in three ecologically disparate locations in Western Australia and identified invertebrate and plant communities using DNA metabarcoding. Ground-dwelling invertebrates showed the clearest restoration signals, with communities becoming more similar to reference communities over time. These patterns were weaker in airborne invertebrates, which have higher dispersal abilities and therefore less local fidelity to environmental conditions. Although we detected directional changes in community composition indicative of invertebrate recovery, patterns observed were inconsistent between study locations. The inclusion of plant assays allowed identification of plant species, as well as potential food sources and habitat. We demonstrate that DNA metabarcoding of invertebrate communities can be used to evaluate restoration trajectories. Testing and incorporating new monitoring techniques such as DNA metabarcoding is critical to improving restoration outcomes.

Invertebrate communities provide many critical ecosystem functions (e.g. pollination, decomposition, herbivory and soil formation), and have been identified as indicators of ecological restoration. Unfortunately, invertebrates are often overlooked in restoration monitoring because they are time-consuming to survey, often require rare taxonomic expertise, and there are many undescribed species. DNA metabarcoding is a tool to rapidly survey invertebrates and can also provide information about plants with which those invertebrates are interacting. Here we evaluate how invertebrate communities may be used to determine ecosystem trajectories during restoration. We collected ground-dwelling and airborne invertebrates across chronosequences of mine-site restoration in three ecologically different locations in Western Australia, and identified invertebrate and plant communities using DNA metabarcoding. Ground-dwelling invertebrates showed the clearest restoration signals, with communities becoming more similar to reference communities over time. These patterns were weaker in airborne invertebrates, which have higher dispersal abilities and therefore less local fidelity to environmental conditions. Invertebrate community recovery was most evident in ecosystems with relatively stable climax communities, while the trajectory in the Pilbara, with its harsh climate and unpredictable monsoonal flooding, was unclear. Plant assay results indicate invertebrates are foraging locally, providing data about interactions between invertebrates and their environment. Thus, we show how DNA metabarcoding of invertebrate communities can be used to evaluate likely trajectories for restoration. Testing and incorporating new monitoring techniques such as DNA metabarcoding is critical to improving restoration outcomes, and is now particularly salient given the ambitious global restoration targets associated with the UN decade on Ecosystem Restoration.

Ant Forest, a mobile app by the monolithic Alibaba Group, is greening individuals' daily activities and transforming human capacity to reverse global environmental degradation. Over 500 million e-trees being cultivated every day in China using Ant Forest. Over 122 million trees planted over more than 112,000 ha of degraded land areas. This is a showcase of how innovation via internet technology combined with digital finance is contributing to solving environmental issues, also the potential to match an individual's daily footprint to their digital footprint and converting this to an ecological footprint.