The post-evaluation of ecological redevelopment is a good way of describing its achievements. In this paper, eco-engineering techniques, including hydrodynamic circulation reconstruction, water purification treatment, and aquatic ecosystem restoration, along with plant harvesting management, have been applied to reconstruct landscape water from aquaculture ponds. Both sediments and water quality were sampled and tested for basic physicochemical parameters and heavy metals. The ecological redevelopment of landscape water reconstructed from aquaculture ponds was evaluated using the single and Nemero comprehensive pollution index methods. The results demonstrate that nutrients, including organic matter and organic nitrogen and their ratio to sediments, were confirmed to be in a state of moderate pollution, while the ecological risk of heavy metal pollution was relatively low. Although the concentrations of total nitrogen and total phosphorus were significantly higher than those of other indexes, ammonia nitrogen, total nitrogen and total phosphorus, all presented obvious downward trends over time, and a majority of the water samples exhibited mild-to-moderate pollution levels. In general, this study provides a set of reference values for redeveloping water ecosystems from aquaculture ponds using eco-engineering technologies.

Post-evaluation of ecological redevelopment is a good method for its achievements. The eco-engineering technologies and achievements of landscape water reconstructed from aquaculture ponds in Shanghai Chenshan Botanical Garden have been introduced in this study. The sediments and water quality were also sampled and tested for basic physicochemical parameters and heavy metal concentration. The ecological redevelopment of landscape water reconstructed from aquaculture ponds was evaluated using the Nemero comprehensive pollution index method. The results shown that nutrients including organic matter, organic nitrogen and their ratio of sediment were found to be in a state of moderate pollution, while their ecological risk of heavy metals was low. Although total nitrogen and total phosphorus of water quality was really higher than that of other indexes, the decline trends of ammonia nitrogen, total nitrogen and total phosphorus was obvious presented over time. In general, this is a good example that redevelopment of water ecosystems from aquaculture ponds using eco-engineering technologies.

Bioaugmentation is an important measure for improving the efficiency of phytoremediation. The objective was to identify the role of exogenous plant debris with different processing and microbial agents for soil characteristics, copper bioavailability and phytoextraction. The experimental design consisted of four blocks, which were divided into 48 plots. Each plot was planted with Fraxinus chinensis and Salix matsudana × alba, which was added to plant composts, woody chips and effective microorganisms (EM) agents, using an orthogonal experimental design. The results showed that the order of bioaugmentation materials on the Cu phytoextraction of two woody species was plant composts > wood chips > EM agents. The best performance of F. chinensis was in the T15 treatment (30% plant composts + 7.5% wood chips + 40 mL·m⁻²), with phytoextraction of 33.66 mg·m⁻², as well as 4.32 mg·m⁻² in the T16 treatment (30% plant composts + 15% wood chips) of S. matsudana × alba. Cu was accumulated mainly in the roots of the two woody plants. The phytoextraction of the above-ground parts was promoted by bioaugmentation, due to the improvement in the physical soil characteristics and Cu bioavailability. The phytoextraction performance of F. chinensis was promoted by the improvement in the Cu concentration after treatments, while for S. matsudana × alba, it was the dry biomass. Thus, targeted strengthening measures should be to applied, to improve the efficiency of phytoremediation.

Accurate individual tree inventories are fundamental to urban forest management, yet automated delineation from Terrestrial Laser Scanning (TLS) data remains a challenge. This study presents a two-stage hybrid framework that combines a domain-adapted deep learning model (TreeLA-Net) with a geometric algorithm (SEGR) to solve this issue, aiming to reduce the need for instance-level annotations. TreeLA-Net first generates semantic labels, outperforming the baseline RandLA-Net by 2.5 percentage points in overall accuracy. Subsequently, SEGR leverages these priors to achieve a tree detection rate of 92.0% on our primary study site. To assess the framework’s transferability, an external validation was conducted on a new, independent site, where the model, without retraining, yielded a recall of 81.5%. These findings suggest that the framework is not strictly overfitted and possesses generalization capabilities. The proposed approach is offered as a potential tool to support data-driven urban forest management, particularly for automated tree mapping and inventory. We hope that this study may contribute to ongoing efforts to develop robust methods for characterizing complex urban forest structures.