Target Classification of Similar Spatial Characteristics in Complex Urban Areas by Using Multispectral LiDAR

Advancements in remote sensing for active fire detection: A review of datasets and methods

This study comprehensively and critically reviews active fire detection advancements in remote sensing from 1975 to the present, focusing on two main perspectives: datasets and corresponding instruments, and detection algorithms. The study highlights the increasing role of machine learning, particularly deep learning techniques, in active fire detection. Looking forward, the review outlines current challenges and future research opportunities in remote sensing for active fire detection. These include exploring data quality management and multi-modal learning, developing spatiotemporally explicit models, investigating self-supervised learning models, improving explainable and interpretable models, integrating physical-process based models with machine learning, and building digital twins to replicate wildfire dynamics and perform what-if scenario analysis. The review aims to serve as a valuable resource for informing natural resource management and enhancing environmental protection efforts through the application of remote sensing technology.

Multispectral Light Detection and Ranging Technology and Applications: A Review

Light Detection and Ranging (LiDAR) is a well-established active technology for the direct acquisition of 3D data. In recent years, the geometric information collected by LiDAR sensors has been widely combined with optical images to provide supplementary spectral information to achieve more precise results in diverse remote sensing applications. The emergence of active Multispectral LiDAR (MSL) systems, which operate on different wavelengths, has recently been revolutionizing the simultaneous acquisition of height and intensity information. So far, MSL technology has been successfully applied for fine-scale mapping in various domains. However, a comprehensive review of this modern technology is currently lacking. Hence, this study presents an exhaustive overview of the current state-of-the-art in MSL systems by reviewing the latest technologies for MSL data acquisition. Moreover, the paper reports an in-depth analysis of the diverse applications of MSL, spanning across fields of "ecology and forestry", "objects and Land Use Land Cover (LULC) classification", "change detection", "bathymetry", "topographic mapping", "archaeology and geology", and "navigation". Our systematic review uncovers the potentials, opportunities, and challenges of the recently emerged MSL systems, which integrate spatial-spectral data and unlock the capability for precise multi-dimensional (nD) mapping using only a single-data source.

Forest stand spectrum reconstruction using spectrum spatial feature gathering and multilayer perceptron

Introduction

Three-dimensional spectral distributions of forest stands can provide spatial information on the physiological and biochemical status of forests, which is vital for forest management. However, three-dimensional spectral studies of forest stands are limited.

Methods

In this study, LiDAR and multispectral data were collected from Masson pine stands in southern Fujian Province, China, and a method was proposed for inverting forest spectra using point clouds as a unit. First, multispectral values were mapped to a point cloud, and the isolated forest algorithm combined with K-means clustering was applied to characterize fusion data. Second, five deep learning algorithms were selected for semantic segmentation, and the overall accuracy (oAcc) and mean intersection ratio (mIoU) were used to evaluate the performance of various algorithms on the fusion data set. Third, the semantic segmentation model was used to reconfigure the class 3D spectral distribution, and the model inversion outcomes were evaluated by the peaks and valleys of the curve of the predicted values and distribution gaps.

Results

The results show that the correlations between spectral attributes and between spatial attributes were both greater than 0.98, while the correlation between spectral and spatial attributes was 0.43. The most applicable method was PointMLP, highest oAcc was 0.84, highest mIoU was 0.75, peak interval of the prediction curve tended to be consistent with the true values, and maximum difference between the predicted value and the true value of the point cloud spectrum was 0.83.

Discussion

Experimental data suggested that combining spatial fusion and semantic segmentation effectively inverts three-dimensional spectral information for forest stands. The model could meet the accuracy requirements of local spectral inversion, and the NIR values of stands in different regions were correlated with the vertical height of the canopy and the distance from the tree apex in the region. These findings improve our understanding of the precise three-dimensional spectral distribution of forests, providing a basis for near-earth remote sensing of forests and the estimation of forest stand health.

Robust algorithm for precise XCO2 retrieval using single observation of IPDA LIDAR

CO₂ column-weighted dry-air mixing ratio (X_CO2) products with high precision and spatial resolution are essential for inverting CO₂ fluxes and promoting our understanding of global climate change. Compared with passive remote sensing methods, IPDA LIDAR, as an active remote sensing technique, offers many advantages in measuring X_CO2. However, a significant random error in IPDA LIDAR measurements causes X_CO2 values calculated directly from LIDAR signals to be unqualified as the final X_CO2 products. Hence, we propose an efficient particle filter-based inversion of CO₂ for single observation (EPICSO) algorithm to precisely retrieve the X_CO2 of every LIDAR observation while preserving the high spatial resolution of LIDAR measurements. The EPICSO algorithm adopts the sliding average results as the first estimate of the local X_CO2; subsequently, it estimates the difference between two adjacent X_CO2 points and calculates the posterior probability of X_CO2 based on particle filter theory. To evaluate the performance of the EPICSO algorithm numerically, we perform an EPICSO to process pseudo-observation data. The simulation results show that the results retrieved by the EPICSO algorithm satisfy the required high precision and that the algorithm is robust to a significant amount of random errors. In addition, we utilize LIDAR observation data from actual experiments in Hebei, China, to validate the performance of the EPICSO algorithm. The results retrieved by the EPICSO algorithm are more consistent with the actual local X_CO2 than those of the conventional method, indicating that the EPICSO algorithm is efficient and practical for retrieving X_CO2 with high precision and spatial resolution.

High-Coverage Reconstruction of XCO2 Using Multisource Satellite Remote Sensing Data in Beijing-Tianjin-Hebei Region

The extreme climate caused by global warming has had a great impact on the earth's ecology. As the main greenhouse gas, atmospheric CO₂ concentration change and its spatial distribution are among the main uncertain factors in climate change assessment. Remote sensing satellites can obtain changes in CO₂ concentration in the global atmosphere. However, some problems (e.g., low time resolution and incomplete coverage) caused by the satellite observation mode and clouds/aerosols still exist. By analyzing sources of atmospheric CO₂ and various factors affecting the spatial distribution of CO₂, this study used multisource satellite-based data and a random forest model to reconstruct the daily CO₂ column concentration (XCO₂) with full spatial coverage in the Beijing-Tianjin-Hebei region. Based on a matched data set from 1 January 2015, to 31 December 2019, the performance of the model is demonstrated by the determination coefficient (R²) = 0.96, root mean square error (RMSE) = 1.09 ppm, and mean absolute error (MAE) = 0.56 ppm. Meanwhile, the tenfold cross-validation (10-CV) results based on samples show R² = 0.91, RMSE = 1.68 ppm, and MAE = 0.88 ppm, and the 10-CV results based on spatial location show R² = 0.91, RMSE = 1.68 ppm, and MAE = 0.88 ppm. Finally, the spatially seamless mapping of daily XCO₂ concentrations from 2015 to 2019 in the Beijing-Tianjin-Hebei region was conducted using the established model. The study of the spatial distribution of XCO₂ concentration in the Beijing-Tianjin-Hebei region shows its spatial differentiation and seasonal variation characteristics. Moreover, daily XCO₂ map has the potential to monitor regional carbon emissions and evaluate emission reduction.