Conference Agenda

Microwave and optical remote sensing data, including Sentinel-1, Sentinel-2, GF-3, Landsat-8, and ICESat-2, were utilized to analyze the salt field types, salt crust types, lake water depth, and sediment thickness in the Qarhan Salt Lake region. The classification and inversion capabilities of different data sources were comprehensively assessed. The application potential of polarimetric SAR data in salt lake monitoring was systematically evaluated, demonstrating its advantages in type identification, change detection, and inversion accuracy enhancement.

For multi-temporal polarimetric SAR (PolSAR) feature extraction, a time correlation entropy feature was proposed based on satellite-borne Sentinel-1 data. A multi-temporal correlation matrix of dominant scattering mechanisms was constructed, and the degree of target variation over the entire observation period was quantified using Shannon entropy. This approach overcomes the limitation of traditional multi-temporal features that only capture changes between two time points, enabling a more comprehensive representation of cumulative changes in surface cover types. Furthermore, by incorporating the principle of maximum entropy, a classification method based on interval estimation of distribution parameters was developed, which enhances spatial consistency and noise robustness. The proposed method was applied to feature extraction and classification in the Qarhan Salt Lake region, achieving superior performance compared to conventional PolSAR features and classification approaches.

To optimize polarization features, we addressed the challenge of high similarity in the polarimetric scattering characteristics of different salt crust types in the Qarhan Salt Lake. These features are predominantly governed by surface scattering and mixed with a small amount of volume scattering, resulting in considerable redundancy among features. To address this, a feature selection method based on polarimetric feature similarity metrics was developed using high-resolution GF-3 polarimetric SAR data. A statistical and textural similarity-based metric, referred to as the Statistical Similarity-based Feature Similarity Measure (SSFSM), was proposed to quantify inter-feature similarity. Based on this metric, seven representative polarimetric features were selected, effectively reducing feature dimensionality while maintaining high classification accuracy (>95%), which is comparable to the performance using the full feature set. This approach not only enhances the efficiency of salt crust classification but also provides a reliable technical foundation for subsequent high-precision identification and automated classification of salt lake surface features.

For fine classification of salt crusts, we proposed a feature optimization and reconstruction method based on neighborhood autoencoder (AE). This method addresses two major challenges: speckle noise interference in high-resolution polarimetric SAR images of homogeneous salt lake surfaces, and the high similarity and redundancy among the polarized scattering features of different salt crust types. The method reconstructs and compresses the original polarimetric features from fully polarized and dual-polarized SAR data, improving feature representation and alleviating redundancy. We performed classification validation experiments based on field survey results. The results show that the method is effective in extracting salt crust distributions and significantly enhances classification accuracy and efficiency.

Optical remote sensing demonstrates outstanding capabilities in water body monitoring. Therefore, research on the water depth inversion of salt lakes and the fine classification of salt fields based on optical remote sensing has been conducted, expanding the means for fine monitoring of salt lake resources. For water depth inversion inversion, a remote sensing method based on the multilayer perceptron (MLP) model was used. Time-series monitoring of salt lake depth changes was achieved through the combined use of multispectral and LiDAR data. Seven spectral bands from Landsat-8 L2 data were used as inputs, with ICESat-2 ATL13 data serving as labels for model training and fitting. This method provides an effective technical solution for salt lake water depth inversion. By integrating multispectral data with LiDAR, the inversion accuracy was improved, achieving a high accuracy (>85%). This approach provides strong support for predicting salt lake water depth and optimizing salt field processes.

In terms of salt field refined classification, Sentinel-2 multispectral data were used to classify and study the sediment thickness in carnallite pool areas. Four types of sediment situation were labeled based on actual collection data. To address the linear operation characteristics in carnallite pools, a line target recognition method incorporating the attention mechanism was proposed. Based on this, a semi-empirical and semi-structural line target classification model was developed. This model integrates linear geometric features and structural information, improving the accuracy of sediment thickness recognition in carnallite pools. Field investigations and comparisons demonstrated the feasibility of this method in distinguishing sedimentation levels, providing effective support for the timing of carnallite collection and resource distribution assessment.

At present, the inversion of salt crust roughness and water content based on PolSAR, estimation of regional salt content in salt fields, water depth changes using optical remote sensing, and monitoring of dynamic changes in sediment thickness are all included in the research plan. These objectives aim to further enhance the capability for high-precision inversion and quantitative monitoring of salt lake resource parameters.

The project aims to produce high-precision, high-resolution forest type classification maps, monitor forest growth dynamics using multi-temporal LiDAR data, invert forest vertical structure parameters from LiDAR data, and develop multi-scale forest quality evaluation methods. Currently, research has been conducted on forest parameter estimation based on TECIS data, optimization of forest type classification algorithms, and extraction of understory sapling parameters, providing critical data support for further analysis of forest ecosystems.

A forest parameter extraction method was developed based on TECIS data. By extracting waveform characteristics from TECIS LiDAR spot data and integrating airborne forest carbon stock data, a machine learning model was constructed to estimate forest carbon stocks at the spot scale. Subsequently, reflectance, texture, and BRDF features derived from multi-angle imagery were combined with spot-scale carbon stock estimates to build a deep learning model for regional forest carbon stock mapping. The mapping results were validated using both airborne and plot-level carbon stock data. Multi-scale accuracy assessments of the TECIS active-passive collaborative carbon stock maps were conducted using ALS-derived carbon data and field measurements, demonstrating high precision across different scales (plot scale: R²=0.45, RMSE=13.24 t/ha; 25m scale: R²=0.58, RMSE=11.74 t/ha; 100m scale: R²=0.70, RMSE=10.12 t/ha; 1km scale: R²=0.85, RMSE=5.18 t/ha).

A forest type mapping methodology integrating Sentinel-1/Sentinel-2 data with Digital Terrain Models (DTM) was developed. The approach utilized Sentinel-1's VV and VH backscattering coefficients, Sentinel-2's original spectral bands, three vegetation indices (NDVI, MSAVI2, and GEMI), along with three DTM-derived topographic variables (elevation in meters above sea level, aspect, and slope) to construct a random forest classification model for forest type discrimination. The resulting forest type map was validated against Switzerland's National Forest Inventory (NFI) plot data. Experimental results demonstrated that the synergistic use of Sentinel-1/Sentinel-2 and DTM effectively mitigates challenges posed by complex terrain and atmospheric conditions in mountainous regions. This model leverages freely available Sentinel-1/Sentinel-2 data and open-source software, featuring reproducible procedures with updatable training datasets. The implemented workflow proves robust, cost-efficient, and highly automated, significantly supporting forestry sector activities requiring precise spatial characterization of forest resources.

A LiDAR-based method for understory sapling parameter extraction was developed. The approach first optimized a spectral clustering-based individual tree segmentation algorithm to accurately delineate larch canopy trees and identify their trunk positions in the study area. After effectively removing overstory tree information, the method employed a mean shift algorithm with adaptive kernel bandwidth, incorporating local stem density, to segment understory spruce saplings. Subsequently, individual sapling height and crown phenotypic parameters were extracted from the segmented point clouds. The detection rate for understory saplings ranged from 94.41% to 152.78%, with matching accuracy improving from 62.59% to 95.65% as canopy closure decreased. Airborne Laser Scanning (ALS)-derived sapling heights showed strong correlation with field measurements (R²=0.71, N=3241, RMSE=0.26 m, p<0.01) and high consistency with Terrestrial Laser Scanning (TLS) data (R²=0.78, N=443, RMSE=0.23 m, p<0.01). ALS-extracted crown widths also demonstrated acceptable accuracy compared to TLS measurements (R²=0.64, N=443, RMSE=0.24 m). The results indicated systematic underestimation of sapling dimensions by ALS due to overstory occlusion effects. This research establishes a foundation for enhancing forest carbon sequestration capacity through regulating gap dynamics and optimizing light resource utilization in managed forests.

翻译

搜索

复制