Supervised Deep-Learning (DL)-based reconstruction algorithms have shown state-of-the-art results for highly-undersampled dynamic Magnetic Resonance Imaging (MRI) reconstruction. However, the requirement of excessive high-quality ground-truth data hinders their applications due to generalization problem. Recently, Implicit Neural Representation (INR) has emerged as a powerful DL-based tool solving inverse problem by characterizing attributes signal continuous function corresponding...

Estimating forest structural attributes of planted forests plays a key role in managing resources, monitoring carbon stocks, and mitigating climate change. High-resolution low-cost remote-sensing data are increasingly available to measure three-dimensional (3D) canopy structure model attributes. In this study, we compared two suites point cloud metrics the accuracies predictive models using unmanned aerial vehicle (UAV) light detection ranging (LiDAR) digital photogrammetry (DAP) data,...

Accurate classification of tree-species is essential for sustainably managing forest resources and effectively monitoring species diversity. In this study, we used simultaneously acquired hyperspectral LiDAR data from LiCHy (Hyperspectral, CCD) airborne system to classify in subtropical forests southeast China. First, each individual tree crown was extracted using the by a point cloud segmentation algorithm (PCS) sunlit portion selected data. Second, different suites metrics were indices...

Canopy cover is a key forest structural parameter that commonly used in inventory, sustainable management and maintaining ecosystem services. Recently, much attention has been paid to the use of unmanned aerial vehicle (UAV)-based light detection ranging (LiDAR) due flexibility, convenience, high point density advantages this method. In study, we UAV-based LiDAR data with individual tree segmentation-based method (ITSM), canopy height model-based (CHMM), statistical model (SMM) metrics...

Tree species composition of forest stand is an important indicator inventory attributes for assessing ecosystem health, understanding successional processes, and digitally displaying biodiversity. In this study, we acquired high spatial resolution multispectral RGB imagery over a subtropical natural in southwest China using fixed-wing UAV system. Digital aerial photogrammetric (DAP) technique was used to generate multi-spectral derived point clouds, upon which individual tree crown (ITC)...

Light detection and ranging (LiDAR) has contributed immensely to forest mapping 3D tree modelling. From the perspective of data acquisition, integration LiDAR from different platforms would enrich information at plot levels. This research develops a general framework integrate ground-based UAV-LiDAR (ULS) better estimate parameters based on quantitative structure modelling (QSM). is accomplished in three sequential steps. First, ground-based/ULS were co-registered local density peaks...

This study introduces a Learning-based Load Frequency Control (LB-LFC) approach to manage the challenges posed by renewable energy’s intermittency in microgrids, which often causes load disturbances, frequency fluctuations, and higher generation costs. The LB-LFC method employs reinforcement learning balance costs stability effectively. In addition, novel sort replay actor critic technique is proposed, leveraging deep deterministic policy gradient algorithm experience enhance control...

Developing an accurate model for estimating the forest structural parameters of planted forests is crucial productivity predictions and can provide a better understanding carbon cycle under climate change. Unmanned aerial vehicle-light detecting ranging (UAV-LiDAR) systems represents promising active remote sensing technology that has potential to be used inventories. In addition, process-based model, physiological principles predicting growth (3-PG), which based on environmental factors,...

Forest structural attributes are key indicators for parameterization of forest growth models, which play roles in understanding the biophysical processes and function ecosystem. In this study, UAS-based multispectral RGB imageries were used to estimate planted subtropical forests. The point clouds generated from using digital aerial photogrammetry (DAP) approach. Different suits spectral metrics (i.e., wide-band indices cloud metrics) derived compared assessed. selected fit partial least...

Ginkgo (Ginkgo biloba L.) is not only considered a ‘living fossil’, but also has important ecological, economic, and medicinal values. However, the impact of climate change on performance distribution this plant an increasing concern. In study, we developed bioclimatic model based data about occurrence ginkgo from 277 locations, validated predictions using wide-ranging field test (12 sites, located at areas 22.49° N to 39.32° N, 81.11° E 123.53° E). We found that degree-days below zero were...

Pigments are the biochemical material basis for energy and exchange between vegetation external environment, therefore quantitative determination of pigment content is crucial. Unmanned Aerial Vehicle (UAV)-borne remote sensing data coupled with radiative transfer models (RTM) provide marked strengths three-dimensional (3D) visualization, as well accurate distributions in forest canopies. In this study, Light Detection Ranging (LiDAR) hyperspectral images acquired by a multi-rotor UAV were...

Accurate information on dominant tree species and their spatial distribution in subtropical natural forests are key ecological monitoring factors for accurately characterizing forest biodiversity, depicting the competition mechanism quantitatively evaluating ecosystem stability. In this study, northwest Yunnan province of China was selected as study area. Firstly, an object-oriented multi-resolution segmentation (MRS) algorithm used to segment individual crowns from UAV RGB imagery satellite...

Forest canopy height is a fundamental parameter of forest structure, and plays pivotal role in understanding biomass allocation, carbon stock, productivity, biodiversity. Spaceborne LiDAR (Light Detection Ranging) systems, such as GEDI (Global Ecosystem Dynamics Investigation), provide large-scale estimation ground elevation, height, other parameters. However, these measurements may have uncertainties influenced by topographic factors. This study focuses on the calibration L2A L1B data using...

The accurate estimation of individual tree level aboveground biomass (AGB) is critical for understanding the carbon cycle, detecting potential biofuels and managing forest ecosystems. In this study, we assessed capability metrics point clouds, extracted from full-waveform Airborne Laser Scanning (ALS) data, composite waveforms, calculated based on a voxel-based approach, estimating AGB individually in combination, over planted coastal region east China. To do so, investigated importance...

Forest structural parameters are key indicators for forest growth assessment, and play a critical role in resources monitoring ecosystem management. Terrestrial laser scanning (TLS) can obtain three-dimensional (3D) structures with ultra-high precision without destruction, whereas some shortcomings such as non-portability cost-consuming limit the quick broad acquisition of structure. Structure from motion (SfM) backpack (BLS) technology have advantages low-cost high-portability while...

Accurate acquisition of forest structural parameters, which is essential for the parameterization growth models and understanding ecosystems, also crucial inventories sustainable management. In this study, simultaneously acquired airborne full-waveform (FWF) LiDAR hyperspectral data were used to predict parameters in subtropical forests southeast China. The pulse amplitude waveform shape FWF calibrated using a physical process-driven voxel-based approach, respectively. Different suites...

Accurately estimating and mapping forest structural parameters are essential for monitoring resources understanding ecological processes. The novel deep learning algorithm has the potential to be a promising approach improve estimation accuracy while combining with advanced remote sensing technology. Airborne light detection ranging (LiDAR) preferable capability characterize 3-D canopy structure estimate parameters. In this study, we developed learning-based (Deep-RBN) that combined fully...