Near real-time monitoring of tropical forest disturbance by fusion of Landsat, Sentinel-2, and Sentinel-1 data

Tang, Xiaojing; Bratley, Kelsee H.; Cho, Kangjoon; Bullock, Eric L.; Olofsson, Pontus; Woodcock, Curtis E.

doi:10.1016/j.rse.2023.113626

Cited by 17 publications

(13 citation statements)

References 38 publications

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“…The full FNRT algorithm is more complex than the "lite" version that we implemented in this lab. If interested, please refer to Tang et al (2023) for more details.…”

Section: Discussionmentioning

confidence: 99%

“…The code for importing and preprocessing input data will be provided in the example script, but it will not be discussed in detail in this chapter. To learn more about FNRT, refer to Tang et al (2023).…”

Section: Practicummentioning

confidence: 99%

“…The main challenge is the methodological approach for fusing the different datasets. The Fusion Near Real-Time (FNRT) algorithm (Tang et al 2023) is a monitoring algorithm for tropical forest disturbance that combines data from Landsat, Sentinel-2, and Sentinel-1. For each sensor system, the FNRT algorithm fits time-series models over data from a three-year training period prior to the one-year monitoring period.…”

Section: Introduction To Theorymentioning

confidence: 99%

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Deforestation Viewed from Multiple Sensors

Tang

2023

Cloud-Based Remote Sensing With Google Earth Engine

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Combining data from multiple sensors is the best way to increase data density and hence detect change faster. The purpose of this chapter is to demonstrate a simple method of combining Landsat, Sentinel-2, and Sentinel-1 data for monitoring tropical forest disturbance. You will learn how to import, preprocess, and fuse optical and synthetic aperture radar (SAR) remote sensing data. You will also learn how to monitor change using time-series models.

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“…The full FNRT algorithm is more complex than the "lite" version that we implemented in this lab. If interested, please refer to Tang et al (2023) for more details.…”

Section: Discussionmentioning

confidence: 99%

Section: Practicummentioning

confidence: 99%

Section: Introduction To Theorymentioning

confidence: 99%

See 1 more Smart Citation

Deforestation Viewed from Multiple Sensors

Tang

2023

Cloud-Based Remote Sensing With Google Earth Engine

Self Cite

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“…Near realtime monitoring can speed up the detection of forest disturbances and is now promising through the integration of multi-source time series data or VHR imagery. For example, the fusion of optical and SAR data from Landsat, Sentinel-2, and Sentinel-1 data has been found to be fast and effective in capturing tree losses in Amazon Basin (Tang et al, 2023). With a revisited time of 1-day, the VHR imagery (3 m resolution) from the PlanetScope nano-satellite constellation shows its capability to quickly identify forest disturbance activites (Francini et al, 2020).…”

Section: Perspectives Of Future Researchmentioning

confidence: 99%

Recent advances and challenges in monitoring and modeling of disturbances in tropical moist forests

He,

Li,

Zhao

et al. 2024

Front. Remote Sens.

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Tropical moist forests have been severely affected by natural and anthropogenic disturbances, leading to substantial changes in global carbon cycle and climate. These effects have received great attention in scientific research and debates. Here we review recent progress on drivers and ecological impacts of tropical moist forest disturbances, and their monitoring and modeling methods. Disturbances in tropical moist forests are primarily driven by clearcutting, selective logging, fire, extreme drought, and edge effects. Compound disturbances such as fire and edge effects aggravate degradation in the edge forests. Drought can result in terrestrial carbon loss via physiological impacts. These disturbances lead to direct carbon loss, biophysical warming and microclimate change. Remote sensing observations are promising for monitoring forest disturbances and revealing mechanisms, which will be useful for implementing disturbance processes in dynamic vegetation models. Yet, constrained spatiotemporal coverages and resolutions limit the application of these data in process-based models. It is also challenging to represent physical processes derived from fine-resolution remote sensing data in coarse-resolution models. We highlight the need to continuously integrate new datasets and physical processes in forest disturbance modeling to advance understanding of disturbance patterns and impacts. Interactions and impacts of climate change and anthropogenic activities should also be considered for modeling and assessing feedbacks of tropical moist forest disturbances.

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“…There is an increasing demand for hyperspectral data; conversely, the fusion of Landsat and MODIS images has been widely studied and provides a reasonable basis for developing fusion workflows for Sentinel-2 and Sentinel-3 data [42]. Recent studies show more applications of Sentinel data in ecological environments, such as the near-real-time monitoring of tropical forest disturbances fused with Landsat data [43], the monitoring of maize nitrogen concentration merged with radar (C-Sar), optical and sensor satellite data [44], and the fusion of multimodal satellite-borne Lidar data with visual images to estimate forest canopy height [45]. Light Detection and Ranging (LiDAR) and hyperspectral imagery (207 articles for 2014-2023) are two basic types of data used in remote sensing applications.…”

Section: Modelsmentioning

confidence: 99%

Comprehensive Analysis of Temporal–Spatial Fusion from 1991 to 2023 Using Bibliometric Tools

Cui,

Li,

et al. 2024

Atmosphere

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Due to budget and sensor technology constraints, a single sensor cannot simultaneously provide observational images with both a high spatial and temporal resolution. To solve the above problem, the spatiotemporal fusion (STF) method was proposed and proved to be an indispensable tool for monitoring land surface dynamics. There are relatively few systematic reviews of the STF method. Bibliometrics is a valuable method for analyzing the scientific literature, but it has not yet been applied to the comprehensive analysis of the STF method. Therefore, in this paper, we use bibliometrics and scientific mapping to analyze the 2967 citation data from the Web of Science from 1991 to 2023 in a metrological manner, covering the themes of STF, data fusion, multi-temporal analysis, and spatial analysis. The results of the literature analysis reveal that the number of articles displays a slow to rapid increase during the study period, but decreases significantly in 2023. Research institutions in China (1059 papers) and the United States (432 papers) are the top two contributors in the field. The keywords “Sentinel”, “deep learning” (DL), and “LSTM” (Long Short-Term Memory) appeared most frequently in the past three years. In the future, remote sensing spatiotemporal fusion research can address more of the limitations of heterogeneous landscapes and climatic conditions to improve fused images’ accuracy.

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Near real-time monitoring of tropical forest disturbance by fusion of Landsat, Sentinel-2, and Sentinel-1 data

Cited by 17 publications

References 38 publications

Deforestation Viewed from Multiple Sensors

Deforestation Viewed from Multiple Sensors

Recent advances and challenges in monitoring and modeling of disturbances in tropical moist forests

Comprehensive Analysis of Temporal–Spatial Fusion from 1991 to 2023 Using Bibliometric Tools

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