Dynamic Monitoring of Forest Land in Fuling District Based on Multi-Source Time Series Remote Sensing Images

Bai, Bingxin; Tan, Yumin; Guo, Dong; Xu, Bo

doi:10.3390/ijgi8010036

Cited by 14 publications

(12 citation statements)

References 28 publications

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“…It is more economical and effective to form high-frequency cloud-free data by combining sub-scenes. In addition, with more and more of remote sensing satellites launched, and increasing remote sensing data are available free of charge, such as the Sentinel series, fusion multi-source remote sensing data (Bai et al 2019), or adopt radar data (Reiche et al 2015) is also an option.…”

Section: Discussionmentioning

confidence: 99%

“…However, the most obvious challenge for their application is cloud cover (CC) and cloud shadows (Kovalskyy and Roy, 2013;Asner, 2001), particularly in some tropics. Although the Landsat satellite can overpass the same location on earth every 16 days, but due to cloud contamination, in some areas, the interval between two cloudless observations is often greater than 16 days, some even last as long as a year (Bai et al 2019). Thus, analysis the availability of Landsat images is a paramount prerequisite for many optical remote sensing applications.…”

Section: Introductionmentioning

confidence: 99%

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The Spatial and Temporal Availability Differences of Cloud-Free Landsat Images Over Three Gorges Reservoir Area

Bai

Tan

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Availability analysis of cloud-free optical remote sensing data is a prerequisite for remote sensing applications. In this study, spatio-temporal availability differences of cloud-free Landsat TM, ETM+, and OLI sensors images over Three Gorges Reservoir Area (TGRA) were analyzed from 1986 to 2019 based on the Google Earth Engine (GEE). The results show that: 1) in Summer, especially in August, the probabilities of obtaining Landsat images with no more than 30% cloud cover (CC) is higher. 2) the northeast of TGRA has higher probability of acquiring cloudless images than the southwest. 3) In TGRA, annual monitoring which require at least one cloud-free observation in a year largely unaffected by CC, but when considering seasonal monitoring, cloud contaminate will become a limitation, and monthly monitoring in this area is basically not feasible even if the three sensors data are combined. The results of this paper will provide important references for the research of using optical data in this area, and although the research area is relatively small, the analysis method and the program developed in this paper have no restrictions on the area.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Spatial and Temporal Availability Differences of Cloud-Free Landsat Images Over Three Gorges Reservoir Area

Bai

Tan

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Traditional forest monitoring research has been based mainly on remote satellite or aircraft images. The image resolution of these data is usually >1 m, and the temporal resolution is generally over 1 week [2,3]. For example, the latest Landsat 9 satellite had a temporal resolution of 16 days when it worked alone and 8 days when it worked with the Landsat 8 satellite [4].…”

Section: Introductionmentioning

confidence: 99%

High-Temporal-Resolution Forest Growth Monitoring Based on Segmented 3D Canopy Surface from UAV Aerial Photogrammetry

Zhang

Gao

Jiang

et al. 2022

Drones

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Traditional forest monitoring has been mainly performed with images or orthoimages from aircraft or satellites. In recent years, the availability of high-resolution 3D data has made it possible to obtain accurate information on canopy size, which has made the topic of canopy 3D growth monitoring timely. In this paper, forest growth pattern was studied based on a canopy point cloud (PC) reconstructed from UAV aerial photogrammetry at a daily interval for a year. Growth curves were acquired based on the canopy 3D area (3DA) calculated from a triangulated 3D mesh. Methods for canopy coverage area (CA), forest coverage rate, and leaf area index (LAI) were proposed and tested. Three spectral vegetation indices, excess green index (ExG), a combination of green indices (COM), and an excess red union excess green index (ExGUExR) were used for the segmentation of trees. The results showed that (1) vegetation areas extracted by ExGUExR were more complete than those extracted by the other two indices; (2) logistic fitting of 3DA and CA yielded S-shaped growth curves, all with correlation R2 > 0.92; (3) 3DA curves represented the growth pattern more accurately than CA curves. Measurement errors and applicability are discussed. In summary, the UAV aerial photogrammetry method was successfully used for daily monitoring and annual growth trend description.

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“…In recent years, the Google Earth Engine (GEE), a planetary-scale platform with the capability to process massive satellite images, facilitated the application of satellite image time series to large-scale land and water monitoring [3][4][5][6][7][8]. Therefore, time series-based research has developed considerably in the last decade [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Simple Spatio–Temporal Data Fusion Method Based on Linear Regression Coefficient Compensation

et al. 2020

Self Cite

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High spatio–temporal resolution remote sensing images are of great significance in the dynamic monitoring of the Earth’s surface. However, due to cloud contamination and the hardware limitations of sensors, it is difficult to obtain image sequences with both high spatial and temporal resolution. Combining coarse resolution images, such as the moderate resolution imaging spectroradiometer (MODIS), with fine spatial resolution images, such as Landsat or Sentinel-2, has become a popular means to solve this problem. In this paper, we propose a simple and efficient enhanced linear regression spatio–temporal fusion method (ELRFM), which uses fine spatial resolution images acquired at two reference dates to establish a linear regression model for each pixel and each band between the image reflectance and the acquisition date. The obtained regression coefficients are used to help allocate the residual error between the real coarse resolution image and the simulated coarse resolution image upscaled by the high spatial resolution result of the linear prediction. The developed method consists of four steps: (1) linear regression (LR), (2) residual calculation, (3) distribution of the residual and (4) singular value correction. The proposed method was tested in different areas and using different sensors. The results show that, compared to the spatial and temporal adaptive reflectance fusion model (STARFM) and the flexible spatio–temporal data fusion (FSDAF) method, the ELRFM performs better in capturing small feature changes at the fine image scale and has high prediction accuracy. For example, in the red band, the proposed method has the lowest root mean square error (RMSE) (ELRFM: 0.0123 vs. STARFM: 0.0217 vs. FSDAF: 0.0224 vs. LR: 0.0221). Furthermore, the lightweight algorithm design and calculations based on the Google Earth Engine make the proposed method computationally less expensive than the STARFM and FSDAF.

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Dynamic Monitoring of Forest Land in Fuling District Based on Multi-Source Time Series Remote Sensing Images

Cited by 14 publications

References 28 publications

The Spatial and Temporal Availability Differences of Cloud-Free Landsat Images Over Three Gorges Reservoir Area

The Spatial and Temporal Availability Differences of Cloud-Free Landsat Images Over Three Gorges Reservoir Area

High-Temporal-Resolution Forest Growth Monitoring Based on Segmented 3D Canopy Surface from UAV Aerial Photogrammetry

A Simple Spatio–Temporal Data Fusion Method Based on Linear Regression Coefficient Compensation

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