Results 141 to 150 of about 763 (163)
LandTrendr smoothed spectral profiles enhance woody encroachment monitoring [PDF]
Remote Sensing of Environment, 2021 This work was supported by project IMAGINE [CGL2016-80400-R] funded by the Spanish Science Foundation (FECYT) and by the research grant program Ajuts UdL, Jade Plus i Fundació Bancària La Caixa [Agreement 79/2018 of the Governing Council of the University of Lleida].
M Teresa Sebastiã +2 more
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Improving Urban Forest Expansion Detection with LandTrendr and Machine Learning
ForestsAnnual urban forest expansion dynamics are crucial for assessing the benefits and potential issues associated with vegetation accumulation over time. LandTrendr (Landsat-Based Detection of Trends in Disturbance and Recovery) can efficiently detect the dynamics of interannual land cover change, but it has difficulty distinguishing urban forest expansion
Liu, Zhe, Zhang, Yaru, Zheng, Xi
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Evaluating the Multidimensional Stability of Regional Ecosystems Using the LandTrendr Algorithm
Remote SensingStability is a key characteristic for understanding ecosystem processes and evolution. However, research on the stability of complex ecosystems often faces limitations, such as reliance on single parameters and insufficient representation of continuous changes.
Lijuan Li, Jiaqiang Du
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Remote Sensing, 2023 Disturbances to forests are getting worse with climate change and urbanization. Assessing the functionality of forest ecosystems is challenging because it requires not only a large amount of input data but also comprehensive estimation indicator methods. The object of the evaluation index of forest ecosystem restoration relies on the ecosystem function
Xiaoman Zheng, Shudi Zuo, Yin Ren
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Remote Sensing, 2022 China has implemented a series of forestry law, policies, regulations, and afforestation projects since the 1970s. However, their impacts on the spatial and temporal patterns of forests have not been fully assessed yet. The lack of an accurate, high-resolution, and long-term forest disturbance and recovery dataset has impeded this assessment.
Guangsheng Chen
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Forests, 2022 Monitoring disturbances in tropical forests is important for assessing disturbance-related greenhouse gas emissions and the ability of forests to sequester carbon, and for formulating strategies for sustainable forest management. Thanks to a long-term observation history, large spatial coverage, and support from powerful cloud platforms such as Google ...
Xiaowei Gu +2 more
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Monitoring Mining Disturbance and Restoration over RBM Site in South Africa Using LandTrendr Algorithm and Landsat Data [PDF]
Sustainability, 2019 Considering the negative impact of mining on ecosystems in mining areas, the South African government legislated the Mineral and Petroleum Resources Development Act (No.
Lubanzi Dlamini, Sifiso Xulu
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Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery [PDF]
Remote Sensing, 2019 Tracking cropland change and its spatiotemporal characteristics can provide a scientific basis for assessments of ecological restoration in reclamation areas. In 1998, an ecological restoration project (Converting Farmland to Lake) was launched in Dongting Lake, China, in which original lake areas reclaimed for cropland were converted back to lake or ...
Yibo Tang, Xiangnan Liu, Ling Wu
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ForestsForest disturbance mapping plays an important role in furthering our understanding of forest dynamics. The Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) algorithm is widely used in forest disturbance mapping.
A-Xing Zhu, Li He
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A LandTrendr multispectral ensemble for forest disturbance detection
Remote Sensing of Environment, 2018Abstract Monitoring and classifying forest disturbance using Landsat time series has improved greatly over the past decade, with many new algorithms taking advantage of the high-quality, cost free data in the archive. Much of the innovation has been focused on use of sophisticated workflows that consist of a logical sequence of processes and rules ...
Warren B. Cohen +4 more
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