Remote Sensing of Riparian Ecosystems

Positive contribution of shoot apex to the growth and flooding tolerance of Hemarthria altissima upon complete submergence

Flooding events tend to destroy the original flood-intolerant vegetation in riparian zones, but the flood-tolerant species can confront the stress, and contribute to the riparian ecosystem. Grass species, Hemarthria altissima, are usually dominant in the riparian zones. This species is considered as good forage which is usually grazed by livestock or mowed by local people. Therefore, the apical tissues of the plants are often removed, and the plants have to grow without stem apexes, during their life cycle. In this study, we aimed to examine the differences in growth performance of intact versus apex-cut individuals of H. altissima upon complete submergence. Two groups of H. altissima plants (with and without shoot apexes) were treated with dark non-submergence and dark complete submergence conditions for 200 days. During the experiment, we measured plant growth, biomass changes in plant organs, and the consumption of non-structural carbohydrates (NSC) by different tissues. During submergence, shoot elongation stopped, and around six lateral buds were developed averagely by each plant without apexes. This growth performance finally caused 60% decline of NSC in underground parts. The relatively intensive consumption of carbohydrates in submerged apex-removed plants induced the 21% stem length decreased under water, which indicated the decreasing submergence tolerance of plants with shoot apex removed. Therefore, we suggest that when using H. altissima for restoring degraded riparian ecosystems, the shoot apexes should be protected from grazing by livestock or harvesting by local people in order to maintain the submergence tolerance of H. altissima.

A Fine-Grained Semantic Alignment Method Specific to Aggregate Multi-Scale Information for Cross-Modal Remote Sensing Image Retrieval

Due to the swift growth in the scale of remote sensing imagery, scholars have progressively directed their attention towards achieving efficient and adaptable cross-modal retrieval for remote sensing images. They have also steadily tackled the distinctive challenge posed by the multi-scale attributes of these images. However, existing studies primarily concentrate on the characterization of these features, neglecting the comprehensive investigation of the complex relationship between multi-scale targets and the semantic alignment of these targets with text. To address this issue, this study introduces a fine-grained semantic alignment method that adequately aggregates multi-scale information (referred to as FAAMI). The proposed approach comprises multiple stages. Initially, we employ a computing-friendly cross-layer feature connection method to construct a multi-scale feature representation of an image. Subsequently, we devise an efficient feature consistency enhancement module to rectify the incongruous semantic discrimination observed in cross-layer features. Finally, a shallow cross-attention network is employed to capture the fine-grained semantic relationship between multiple-scale image regions and the corresponding words in the text. Extensive experiments were conducted using two datasets: RSICD and RSITMD. The results demonstrate that the performance of FAAMI surpasses that of recently proposed advanced models in the same domain, with significant improvements observed in R@K and other evaluation metrics. Specifically, the mR values achieved by FAAMI are 23.18% and 35.99% for the two datasets, respectively.