Leiguang Wang

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Verified

Leiguang verified their affiliation via an institutional email.

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• Doctor of Engineering
• Professor (Full) at Southwest Forestry University

Forest fine fuels are a crucial component of surface fuels and play a key role in igniting forest fires. However, despite nearly 20 years of long-term prescribed burning management on Zhaobi Mountain in Xinping County, Yunnan Province, China, there remains a lack of specific quantification regarding the effectiveness of fine fuel management in Pinu...

Laser waveform data that contain rich three-dimensional structural object information hold significant value in forest resource monitoring. However, traditional waveform decomposition algorithms are often constrained by complex waveform structures and depend on the initial parameter selections, which affect the accuracy and robustness of the result...

Remote sensing images are indispensable for continuous environmental monitoring and Earth observations. However, cloud occlusion can severely degrade image quality, posing a significant challenge for the accurate extraction of ground information. Existing cloud removal techniques often suffer from incomplete cloud removal, artifacts, and color dist...

Understanding post-fire vegetation recovery dynamics is crucial for damage assessment and recovery planning, yet spatiotemporal patterns in complex plateau environments remain poorly understood. This study addresses this gap by focusing on Yunnan Province, a mountainous plateau region with high fire incidence. We developed an innovative approach co...

Traditional methods for estimating tea yield mainly rely on manual sampling surveys and empirical estimation, which are labor-intensive and time-consuming. Accurately estimating fresh tea production in different seasons has become a challenging task. It is possible to estimate the seasonal yield of tea at the field scale by using the spatial resolu...

The accurate estimation of aboveground biomass (AGB) in rubber plantations is essential for predicting rubber production and assessing carbon storage. Multispectral sensors mounted on unmanned aerial vehicles (UAVs) can obtain high spatiotemporal resolution imagery of rubber plantations, offering significant advantages in capturing fine structural...

As spatial resolution increases in remote sensing imagery, the challenge of semantic segmentation intensifies due to the need to discern intricate changes in terrain. Terrain, a composite of diverse geographic elements arranged in specific spatial patterns, demands a higher level of abstraction in semantic categorization. Achieving accurate semanti...

Existing methods of change detection that rely on individual image modalities, such as concatenation and differencing, risk overlooking the distinct change patterns, and detailed features inherent in different modalities. This oversight can result in missed detections and suboptimal delineation of change boundaries. To address these limitations, th...

Identifying the key climate variables affecting optical saturation values (OSVs) in forest aboveground biomass (AGB) estimation using optical remote sensing is crucial for analyzing OSV changes. This can improve AGB estimation accuracy by addressing the uncertainties associated with optical saturation. In this study, Pinus yunnanensis forests and L...

As part of the ecosystem, the western black-crested gibbon (Nomascus concolor) is important for ecological sustainability. Calls are an important means of communication for gibbons, so accurately recognizing and categorizing gibbon calls is important for their population monitoring and conservation. Since a large amount of sound data will be genera...

The western black-crested gibbon (Nomascus concolor) is a rare and endangered primate that inhabits southern China and northern Vietnam, and has become a key conservation target due to its distinctive call and highly endangered status, making its identification and monitoring particularly urgent. Identifying calls of the western black-crested gibbo...

Timely and accurate information on tree species is of great importance for the sustainable management of natural resources, forest inventory, biodiversity detection, and carbon stock calculation. The advancement of remote sensing technology and artificial intelligence has facilitated the acquisition and analysis of remote sensing data, resulting in...

Veneer knots are the main indicator of plywood quality. Existing veneer knot identification algorithms have a high identification accuracy rate of over 90%. However, the convolutional neural network (CNN) model is complex and requires laborious data labeling. The localization algorithm produces veneer knot bounding boxes, except for the Mask Region...

Most of the existing deep learning-based hyperspectral image (HSI) classification algorithms are based on supervised learning, where a large number of annotated labels with high acquisition cost are required. Self-supervised learning (SSL) methods can learn abundant representations using a large amount of unlabeled data, thereby reducing the reliab...

The optical saturation problem is one of the main factors causing uncertainty in aboveground biomass (AGB) estimations using optical remote sensing data. It is critical for the improvement in AGB estimation accuracy to clarify the relationships between environmental factors and the variations in optical saturation values (OSVs). In this study, we o...

Timely and accurate information on tree species is crucial for the sustainable management of natural resources, forest inventory, biodiversity detection, and carbon stock calculation. The advancement of remote sensing technology and artificial intelligence has facilitated the acquisition and analysis of remote sensing data, resulting in more precis...

Improving the precision of aboveground biomass (AGB) estimation in lowland tropical forests is crucial to enhancing our understanding of carbon dynamics and formulating climate change mitigation strategies. This study proposes an AGB estimation method for lowland tropical forests in Xishuangbanna, which include various vegetation types, such as Pin...

The Altai Mountains, located in the northwesternmost part of China, have a harsh climate and little human activity, making it an excellent location to study forest ecology undisturbed by human interference. The forest is frequently struck by lightning and experiences long-term natural fire disturbances, leading to the evolution of unique fire adapt...

Crop identification is a fundamental task in remote sensing image interpretation. The rapid development of Unmanned Aerial Vehicle (UAV) has revolutionized the acquisition of super-high-resolution images. Compared with remote sensing ones, the fact that UAV images are easier to be flexibly acquired and contain more information brings opportunities...

We propose a novel network for multiview stereo (MVS) reconstruction in the field of remote sensing, which considers clustering-based semantic consistency into depth estimation optimization, referred to as CSC-MVS. In this approach, high-level semantic information acquired from multiple views is used to construct semantic consistency and assist in...

Semantic segmentation is one of the most important tasks in the field of remote sensing. As the spatial resolution increases, the remote sensing images can capture more detailed information and make hierarchical semantic interpretation possible. However, hierarchical semantic segmentation encounters high heterogeneity not only within the intra-laye...

To improve the accuracy of face recognition when there are wrong-labeled samples, a new deep face recognition model with cosine boundary loss is proposed in this paper. First, the proposed model uses the cosine similarity to determine the boundary that divides training samples into easy samples, semi-hard samples and harder samples, which play diff...

Quantification of three-dimensional green volume (3DGV) plays a crucial role in assessing environmental benefits to urban green space (UGS) at a regional level. However, precisely estimating regional 3DGV based on satellite images remains challenging. In this study, we developed a parametric estimation model to retrieve 3DGV in UGS through combinin...

Deep convolutional neural networks have greatly enhanced the semantic segmentation of remote sensing images. However, most networks are primarily designed to process imagery with red, green, and blue bands. Although it is feasible to directly utilize established networks and pre-trained models for remotely sensed images, they suffer from imprecise...

Prescribed burning is a widely used fuel management employed technique to mitigate the risk of forest fires. The Pinus yunnanensis Franch. forest, which is frequently prone to forest fires in southwestern China, serves as a prime example for investigating the effects of prescribed burning on the flammability of surface dead fuel. This research aims...

Introduction Positioning studies on prescribed burning in Pinus yunnanensis forests have been conducted for several years, focusing on the effects of fire on the composition and structure, growth, regeneration, relative bark thickness, and bark density of understory oak species in Pinus yunnanensis forests. Methods The study was conducted on Zhaob...

Though many new remote sensing technologies have been introduced to analyze forests, regional-scale species-level mapping products are still rare, especially in large mountainous areas. Tree species abundance, low spectral separability among species and huge computing demand are hindrances for obtaining an accurate stand tree species map. This stud...

Deep learning has achieved remarkable performance in semantically segmenting remotely sensed images. However, the high-frequency detail loss caused by continuous convolution and pooling operations and the uncertainty introduced when annotating low-contrast objects with weak boundaries induce blurred object boundaries. Therefore, a dual-stream netwo...

It is a challenge to reduce the uncertainties of the underestimation and overestimation of forest aboveground biomass (AGB) which is common in optical remote sensing imagery. In this study, four models, namely, the linear stepwise regression (LSR), artificial neural network (ANN), quantile regression (QR), and quantile regression neural network (QR...

Three-dimension green volume (3DGV) is a quantitative index that measures the crown space occupied by growing plants. It is often used to evaluate the environmental and climatic benefits of urban green space (UGS). We proposed the Mean of neighboring pixels (MNP) algorithm based on unmanned aerial vehicle (UAV) RGB images to estimate the 3DGV in Yu...

Accurately mapping tree species is crucial for forest management and conservation. Most previous studies relied on features derived from optical imagery, and digital elevation data and the potential of synthetic aperture radar (SAR) imagery and other environmental factors have, generally, been underexplored. Therefore, the aim of this study is to e...

The uncertainty from the under-estimation and over-estimation of forest aboveground biomass (AGB) is an urgent problem in optical remote sensing estimation. In order to more accurately estimate the AGB of Pinus densata forests in Shangri-La City, we mainly discuss three non-parametric models—the artificial neural network (ANN), random forests (RFs)...

Deep learning methods have been widely studied in the semantic segmentation field of the remote sensing image. Training images play an important role in these methods; however, each training image usually contains not only the generalization information of each land category but also the specific inter-class context between different categories. Th...

Exposed surface for buildings (ESB), which refers to exposed surfaces with traces of building construction, often leads to urban dust. Accurate ESB detection is important for planning urban development and improving urban environment. Fine-grained monitoring of ESB typically needs massive high-quality pixel-level labels, which are demanding and exp...

Remote sensing technology provides an economical and efficient means for obtaining the distribution of dominant tree species. However, remote sensing mapping of dominant tree species have remained an ongoing challenge, such as massive data and low accuracy of a single machine learning method, especially over large-scale mountainous areas. In this s...

In remote sensing image classification, it is difficult to distinguish the homogeneity of same land class and the heterogeneity between different land classes. Moreover, high spatial resolution remote sensing images often show the phenomenon of ground object classes fragmentation and salt-and-pepper noise after classification. To improve the above...

Forest fire is an ecosystem regulating factor and affects the stability, renewal, and succession of forest ecosystems. However, uncontrolled forest fires can be harmful to the forest ecosystem and to the public at large. Although Yunnan, China is regarded as a global hotspot for forest fires, a general lack of understanding prevails there regarding...

Xishuangbanna is a major natural rubber and tea production base in China and a national nature reserve with the best-preserved tropical ecosystem. However, the extensive exploitation and use of land resources impact the land use/land cover (LULC) and the processes of regional landscape ecology, further causing a battery of ecological and environmen...

Building detection from very high resolution (VHR) optical remote sensing images, which is an essential but challenging task in remote sensing, has attracted increased attention in recent years. However, despite the many methods that have been developed, an in-depth review of the recent literature on building extraction from VHR optical images is s...

Tea plantations encroachment into forests has occurred in the major tea planting area of Yunnan province, China in the past decades. However, the dynamics of tea plantation encroachment into forests and the effect on forest landscape pattern is not clear. In this study, we proposed a method to evaluate the dynamic effects of tea plantation encroach...

The matching problem for heterologous remote sensing images can be simplified to the matching problem for pseudo homologous remote sensing images via image translation to improve the matching performance. Among such applications, the translation of synthetic aperture radar (SAR) and optical images is the current focus of research. However, the exis...

Accurate information about forest type and distribution is critical for many scientific applications. It is possible to make a forest type map from the satellite data in a cost effective way. However, forest type mapping over a large and mountainous geographic area is still challenging, due to complex forest type compositions, spectral similarity a...

The Erhai Lake Basin is an area with the active economic and social development of agriculture and tourism, facing increasingly prominent environmental problems with rapid urbanization. Assessing spatial–temporal changes in ecological environment quality objectively and quantitatively in a timely fashion is crucial for environmental protection and...

Semantic segmentation is one of the most important tasks in the field of remote sensing image processing. Many methods have been proposed to realize it at the pixel granularity or object granularity. Specifically, the pixel-based methods usually can effectively extract the detailed information and edges, and the object-based methods can keep the in...

The specific impact of ecological environment quality at a regional scale due to the rubber plantations expansion is still unclear in Xishuangbanna, Yunnan province, China. First, we used a pixel and phenology-based multiple normalization approach to map rubber plantations over six time periods during 1995-2018 with the available high-quality Lands...

Semantic segmentation is one of the most important tasks in remote sensing. However, as spatial resolution increases, distinguishing the homogeneity of each land class and the heterogeneity between different land classes are challenging. The Markov random field model (MRF) is a widely used method for semantic segmentation due to its effective spati...

Tea plantations expansion has occurred in the major tea planting area of Yunnan province, China, however, it is a big challenge to extract and map the distribution of tea plantations due to their non-obvious phenological characteristics in the tropical and subtropical regions. Firstly, we demonstrated the pruning phenological phase of tea plantatio...

Interactions in microservice systems are complex due to three dimensions: numerous asynchronous interactions, the diversity of asynchronous communication, and unbounded buffers. Analyzing such complex interactions is challenging. In this paper, we propose an approach for interaction analysis using model checking techniques, which is supported by th...

High-spatial-resolution (HSR) remote sensing images usually contain rich hierarchical semantic information. However, many methods fail to solve the segmentation misclassification problems for HSR images due to just considering one layer of granularity information, such as the pixel granularity layer or the object granularity layer. This paper prese...

In order to classify high spatial resolution images more accurately, in this research, a hierarchical rule-based object-based classification framework was developed based on a high-resolution image with airborne Light Detection and Ranging (LiDAR) data. The eCognition® software is employed to conduct the whole process. In detail, firstly, the FBSP...

The traditional fusion methods are based on the fact that the spectral ranges of the Panchromatic (PAN) and multispectral bands (MS) are almost overlapping. In this paper, we propose a new pan-sharpening method for the fusion of PAN and SWIR (short-wave infrared) bands, whose spectral coverages are not overlapping. This problem is addressed with a...

Forest biomass is an important indicator for the structure and function of forest ecosystems, and an accurate assessment of forest biomass is crucial for understanding ecosystem changes. Remote sensing has been widely used for inversion of biomass. However, in mature or over-mature forest areas, spectral saturation is prone to occur. Based on exist...

Features extracted from a single scale cannot effectively express differences among land objects and recognize object boundaries.Thus, hyperspectral image classification suffers from low classification accuracy and the "pepper-and-salt" phenomenon. In this context, we propose a set of multi-scale spatial features that is based on guided filtering t...

GaoFen-2 (GF-2) is a civilian optical satellite self-developed by China equipped with both multispectral and panchromatic sensors, and is the first satellite in China with a resolution below 1 m. Because the pan-sharpening methods on GF-2 imagery have not been a focus of previous works, we propose a novel pan-sharpening method based on guided image...

Hyperspectral images usually consist of more than one hundred spectral bands, which have potentials to provide rich spatial and spectral information. However, the application of hyperspectral data is still challengeable due to “the curse of dimensionality”. In this context, many techniques, which aim to make full use of both the spatial and spectra...

The GaoFen-2 satellite (GF-2) is a self-developed civil optical remote sensing satellite of China, which is also the first satellite with the resolution of being superior to 1 meter in China. In this paper, we propose a pan-sharpening method based on guided image filtering, apply it to the GF-2 images and compare the performance to state-of-the-art...

China has experienced unprecedented urbanization in the past decades, resulting in dramatic changes in the physical, limnological, and hydrological characteristics of lakes in urban landscapes. However, the spatiotemporal dynamics in distribution and abundance of urban lakes in China remain poorly understood. Here, we characterized the spatiotempor...

This paper develops a novel Markov Random Field (MRF) model for edge-preserving spatial regularization of classification maps. MRF methods based on the uniform smoothness lead to oversmoothed solutions. In contrast, MRF methods which take care of local spectral or gradient discontinuities, lead to unexpected object particles around boundaries. To s...

The Markov random field (MRF) model has attracted great attention in the field of image segmentation. However, most MRF-based methods fail to resolve segmentation misclassification problems for high spatial resolution remote sensing images due to insufficiently using the hierarchical semantic information. In order to solve such a problem, this pape...

High spatial resolution (HR) remote-sensing image usually contains hierarchical semantic information. Many supervised methods have been developed to interpret this information through data training. In this article, without data training, a hybrid object-based Markov random field (HOMRF) model is proposed for multilayer semantic segmentation of rem...

Hyperspectral data sets with high spatial resolution have been widely used in the research of image classification. The methodology based on the mathematical morphology, which aims at extracting the structure of hyperspectral images, has been implemented. In this method, opening and closing morphological operation used in hyperspectral data in orde...

In this paper, we propose a novel remote sensing fusion approach based on guided image filtering. The fused images can well preserve the spectral features of the original multispectral (MS) images, meanwhile, enhance the spatial details information. Four quality assessment indexes are also introduced to evaluate the fusion effect when compared with...

Accurate interpretation of high spatial resolution multispectral (MS) imagery relies on the extraction and fusion of information obtained from both spectral and spatial domains. Feature extraction from one or several fixed windows uses inaccurate description of pixel contexts and produces blurred object boundaries and low classification accuracy. I...