Tree Species (Genera) Identification with GF-1 Time-Series in A Forested Landscape, Northeast China

Xu, Kaijian; Tian, Qingjiu; Zhang, Zhaoying; Yue, Jibo; Chang, Chih-Hsiung

doi:10.3390/rs12101554

Cited by 13 publications

(4 citation statements)

References 99 publications

(101 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a medium-resolution satellite, Gaofen-1 was launched in 2013, and its data have been freely available since 2019. Scholars used Gaofen-1 and Sentinel-1 data to conduct research in various fields [52,53]. However, it was unknown whether the performance of Gaofen-1 and Sentinel-1 was appropriate for coniferous forest identification.…”

Section: The Performance Of the Proposed Methodological Frameworkmentioning

confidence: 99%

Mapping Coniferous Forest Distribution in a Semi-Arid Area Based on Multi-Classifier Fusion and Google Earth Engine Combining Gaofen-1 and Sentinel-1 Data: A Case Study in Northwestern Liaoning, China

Liu,

Zhang,

Guo

et al. 2024

Forests

View full text Add to dashboard Cite

Information about the distribution of coniferous forests holds significance for enhancing forestry efficiency and making informed policy decisions. Accurately identifying and mapping coniferous forests can expedite the achievement of Sustainable Development Goal (SDG) 15, aimed at managing forests sustainably, combating desertification, halting and reversing land degradation, and halting biodiversity loss. However, traditional methods employed to identify and map coniferous forests are costly and labor-intensive, particularly in dealing with large-scale regions. Consequently, a methodological framework is proposed to identify coniferous forests in northwestern Liaoning, China, in which there are semi-arid and barren environment areas. This framework leverages a multi-classifier fusion algorithm that combines deep learning (U2-Net and Resnet-50) and shallow learning (support vector machines and random forests) methods deployed in the Google Earth Engine. Freely available remote sensing images are integrated from multiple sources, including Gaofen-1 and Sentinel-1, to enhance the accuracy and reliability of the results. The overall accuracy of the coniferous forest identification results reached 97.6%, highlighting the effectiveness of the proposed methodology. Further calculations were conducted to determine the area of coniferous forests in each administrative region of northwestern Liaoning. It was found that the total area of coniferous forests in the study area is about 6013.67 km2, accounting for 9.59% of northwestern Liaoning. The proposed framework has the potential to offer timely and accurate information on coniferous forests and holds promise for informed decision making and the sustainable development of ecological environment.

show abstract

Section: The Performance Of the Proposed Methodological Frameworkmentioning

confidence: 99%

Mapping Coniferous Forest Distribution in a Semi-Arid Area Based on Multi-Classifier Fusion and Google Earth Engine Combining Gaofen-1 and Sentinel-1 Data: A Case Study in Northwestern Liaoning, China

Liu,

Zhang,

Guo

et al. 2024

Forests

View full text Add to dashboard Cite

show abstract

“…For example, Liu et al [42] demonstrated that the anthocyanin content index and photochemical reflectance index were the most important HS features for the identification of TS in the spring season when they mapped urban tree species by integrated airborne HS image and LiDAR data. Xu et al [131] reported that the integration of time-series GF-1 NDVI images, vegetation growth status, and feature of canopy information simultaneously will be beneficial to mapping TS at a landscape scale.…”

Section: Feature Types and Selectionmentioning

confidence: 99%

Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective

2021

J Remote Sens

View full text Add to dashboard Cite

Timely and accurate information on tree species (TS) is crucial for developing strategies for sustainable management and conservation of artificial and natural forests. Over the last four decades, advances in remote sensing technologies have made TS classification possible. Since many studies on the topic have been conducted and their comprehensive results and novel findings have been published in the literature, it is necessary to conduct an updated review on the status, trends, potentials, and challenges and to recommend future directions. The review will provide an overview on various optical and light detection and ranging (LiDAR) sensors; present and assess current various techniques/methods for, and a general trend of method development in, TS classification; and identify limitations and recommend future directions. In this review, several concluding remarks were made. They include the following: (1) A large group of studies on the topic were using high-resolution satellite, airborne multi-/hyperspectral imagery, and airborne LiDAR data. (2) A trend of “multiple” method development for the topic was observed. (3) Machine learning methods including deep learning models were demonstrated to be significant in improving TS classification accuracy. (4) Recently, unmanned aerial vehicle- (UAV-) based sensors have caught the interest of researchers and practitioners for the topic-related research and applications. In addition, three future directions were recommended, including refining the three categories of “multiple” methods, developing novel data fusion algorithms or processing chains, and exploring new spectral unmixing algorithms to automatically extract and map TS spectral information from satellite hyperspectral data.

show abstract

“…The swath width of the GF-1 WFV can reach up to 800 km when the four wide-width cameras are combined [8]. Currently, the image of the GF-1 satellite has been widely used in the fields of forestry [9,10], agriculture [11][12][13], and ecological environment [14,15], making up to a certain extent for the shortcomings in the application of high-resolution satellite data in China.…”

Section: Introductionmentioning

confidence: 99%

A Methodological Approach for Gap Filling of WFV Gaofen-1 Images from Spatial Autocorrelation and Enhanced Weighting

Chen,

Yu,

Zhang

et al. 2024

Atmosphere

View full text Add to dashboard Cite

Clouds and cloud shadow cover cause missing data in some images captured by the Gaofen-1 Wide Field of View (GF-1 WFV) cameras, limiting the extraction and analysis of the image information and further applications. Therefore, this study proposes a methodology to fill GF-1 WFV images using the spatial autocorrelation and improved weighting (SAIW) method. Specifically, the search window size is adaptively determined using Getis-Ord Gi* as a metric. The spatial and spectral weights of the pixels are computed using the Chebyshev distance and spectral angle mapper to better filter the suitable similar pixels. Each missing pixel is predicted using linear regression with similar pixels on the reference image and the corresponding similar pixel located in the non-missing region of the cloudy image. Simulation experiments showed that the average correlation coefficient of the proposed method in this study is 0.966 in heterogeneous areas, 0.983 in homogeneous farmland, and 0.948 in complex urban areas. It suggests that SAIW can reduce the spread of errors in the gap-filling process to significantly improve the accuracy of the filling results and can produce satisfactory qualitative and quantitative fill results in a wide range of typical land cover types and has extensive application potential.

show abstract

Tree Species (Genera) Identification with GF-1 Time-Series in A Forested Landscape, Northeast China

Cited by 13 publications

References 99 publications

Mapping Coniferous Forest Distribution in a Semi-Arid Area Based on Multi-Classifier Fusion and Google Earth Engine Combining Gaofen-1 and Sentinel-1 Data: A Case Study in Northwestern Liaoning, China

Mapping Coniferous Forest Distribution in a Semi-Arid Area Based on Multi-Classifier Fusion and Google Earth Engine Combining Gaofen-1 and Sentinel-1 Data: A Case Study in Northwestern Liaoning, China

Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective

A Methodological Approach for Gap Filling of WFV Gaofen-1 Images from Spatial Autocorrelation and Enhanced Weighting

Contact Info

Product

Resources

About