This letter presents a classifier-constrained deep adversarial domain adaptation (CDADA) method for cross-domain semisupervised classification in remote sensing (RS) images. A deep convolutional neural network (DCNN) is used to build feature representations to describe the semantic content of scenes before the adaptation process. Then, adversarial domain adaptation is used to align the feature distribution of the source and the target. Specifically, two different land-cover classifiers are used as a discriminator to consider land-cover decision boundaries between classes and increase their distance to separate them from the original land-cover class boundaries. The generator then creates robust transferable features far from the original land-cover class boundaries under the classifier constraint. The experimental results of six scenarios built from three benchmark RS scene data sets (AID, Merced, and RSI-CB data sets) are reported and discussed.

Research Highlights: This study carried out a feasibility analysis on the tree height extraction of a planted coniferous forest with high canopy density by combining terrestrial laser scanner (TLS) and unmanned aerial vehicle (UAV) image–based point cloud data at small and midsize tree farms. Background and Objectives: Tree height is an important factor for forest resource surveys. This information plays an important role in forest structure evaluation and forest stock estimation. The objectives of this study were to solve the problem of underestimating tree height and to guarantee the precision of tree height extraction in medium and high-density planted coniferous forests. Materials and Methods: This study developed a novel individual tree localization (ITL)-based tree height extraction method to obtain preliminary results in a planted coniferous forest plots with 107 trees (Metasequoia). Then, the final accurate results were achieved based on the canopy height model (CHM) and CHM seed points (CSP). Results: The registration accuracy of the TLS and UAV image-based point cloud data reached 6 cm. The authors optimized the precision of tree height extraction using the ITL-based method by improving CHM resolution from 0.2 m to 0.1 m. Due to the overlapping of forest canopies, the CSP method failed to delineate all individual tree crowns in medium to high-density forest stands with the matching rates of about 75%. However, the accuracy of CSP-based tree height extraction showed obvious advantages compared with the ITL-based method. Conclusion: The proposed method provided a solid foundation for dynamically monitoring forest resources in a high-accuracy and low-cost way, especially in planted tree farms.

Spectral data can potentially offer a rapid assessment of nutrients in leaves and reveal information about the geologic history of the soil. This study evaluated the capability of the partial least squares regression (PLSR) for estimating foliar macro- and micronutrients (Ca, Mg, K, P, Mn, and Zn) using spectral data (400 to 2,450 nm). First, filter-based wavelength selection was conducted to reduce the independent variables. PLSR performance was then assessed across 4 geologic materials (coarse glacial till, glaciofluvial, melt-out till, and outwash) and 4 dominant tree genera ( Acer , Betula , Fagus , and Quercus ) in the northeastern United States. The spectral ranges 400 to 500 nm and 1,800 to 2,450 nm were found to be the most important spectral regions for estimating foliar nutrient concentrations. The developed PLSR model predicted 6 foliar nutrients with moderate to high accuracy (adjusted R ² from 0.60 to 0.75). Foliar macronutrient concentrations were estimated with higher accuracy (mean adj. R ² = 0.69) than micronutrient concentrations (mean adj. R ² = 0.635). The prediction for the individual tree genera group and the individual geologic materials group outperformed the combined group; for instance, the adj. R ² for estimating Ca and P was 39% higher for American beech ( Fagus grandifolia ) than all tree genera combined. Spectral measurements combined with wavelength selection and PLSR models can potentially be used to quantify foliar macro- and micronutrients at regional scales, and taking into account geologic materials and tree genera will improve this prediction.

Salt marshes rely on sediment to maintain elevation and resist sea level rise (SLR). While riverine sediment is well studied, marine‐sourced sediment remains understudied, particularly in regions with limited river input. We examined the influence of marine‐sourced sediment on salt marsh stability across 103 sites in the Northeastern US, using a new satellite‐derived suspended sediment concentration (SSC) product. Marsh vulnerability was assessed using the Unvegetated‐to‐Vegetated Ratio (UVVR) and relative marsh elevation (Z*). We identified two thresholds: SSC >20 mg/L and RSLR <5.2 mm/year. Marshes meeting either threshold tended to remain intact, while those with both low SSC and high RSLR were more vulnerable to degradation. A north–south gradient was observed, with most unstable marshes in the southern region, where SSC declined by 0.9% per decade on average, compared to 0.072% in the north. These findings highlight the need to include coastal sediment dynamics in restoration and adaptation planning.