Water Mapping and Change Detection Using Time Series Derived from the Continuous Monitoring of Land Disturbance Algorithm

• URL: http://arxiv.org/abs/2504.03170v1
• Date: Fri, 04 Apr 2025 04:59:46 GMT
• Title: Water Mapping and Change Detection Using Time Series Derived from the Continuous Monitoring of Land Disturbance Algorithm
• Authors: Huong Pham, Samuel Cheng, Tao Hu, Chengbin Deng,
• Abstract summary: The Continuous Monitoring of Land Disturbance (COLD) algorithm provides a valuable tool for real-time analysis of land changes.<n>This paper assesses the effectiveness of the algorithm to estimate water bodies and track pixel-level water trends over time.
• Score: 5.426703801207648
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Given the growing environmental challenges, accurate monitoring and prediction of changes in water bodies are essential for sustainable management and conservation. The Continuous Monitoring of Land Disturbance (COLD) algorithm provides a valuable tool for real-time analysis of land changes, such as deforestation, urban expansion, agricultural activities, and natural disasters. This capability enables timely interventions and more informed decision-making. This paper assesses the effectiveness of the algorithm to estimate water bodies and track pixel-level water trends over time. Our findings indicate that COLD-derived data can reliably estimate estimate water frequency during stable periods and delineate water bodies. Furthermore, it enables the evaluation of trends in water areas after disturbances, allowing for the determination of whether water frequency increases, decreases, or remains constant.

Related papers

• A Spatiotemporal Radar-Based Precipitation Model for Water Level Prediction and Flood Forecasting [0.9487148673655145]
  In July 2017, the cities of Goslar and G"ottingen experienced severe flood events characterized by short warning time of only 20 minutes.<n>This highlights the critical need for a more reliable and timely flood forecasting system.
  arXiv  Detail & Related papers  (2025-03-25T10:14:54Z)
• Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction [64.4881275941927]
  We present the first comprehensive evaluation of trustworthiness in a continental-scale multi-task LSTM model.<n>Our investigation uncovers systematic patterns of model performance disparities linked to basin characteristics.<n>This work serves as a timely call to action for advancing trustworthy data-driven methods for water resources management.
  arXiv  Detail & Related papers  (2025-03-13T01:50:50Z)
• Image-Based Relocalization and Alignment for Long-Term Monitoring of Dynamic Underwater Environments [57.59857784298534]
  We propose an integrated pipeline that combines Visual Place Recognition (VPR), feature matching, and image segmentation on video-derived images. This method enables robust identification of revisited areas, estimation of rigid transformations, and downstream analysis of ecosystem changes.
  arXiv  Detail & Related papers  (2025-03-06T05:13:19Z)
• SEN12-WATER: A New Dataset for Hydrological Applications and its Benchmarking [40.996860106131244]
  Climate and increasing droughts pose significant challenges to water resource management around the world. We present a new dataset, SEN12-WATER, along with a benchmark using a end-to-end Deep Learning framework for proactive drought-related analysis.
  arXiv  Detail & Related papers  (2024-09-25T16:50:59Z)
• Using Multi-Temporal Sentinel-1 and Sentinel-2 data for water bodies mapping [40.996860106131244]
  Climate change is intensifying extreme weather events, causing both water scarcity and severe rainfall unpredictability. This paper aims to provide valuable insights for comprehensive water resource monitoring under diverse meteorological conditions.
  arXiv  Detail & Related papers  (2024-01-05T18:11:08Z)
• Analysis of Rainfall Variability and Water Extent of Selected Hydropower Reservoir Using Google Earth Engine (GEE): A Case Study from Two Tropical Countries, Sri Lanka and Vietnam [0.0]
  This study presents a comprehensive remote sensing analysis of rainfall patterns and selected hydropower reservoir water extent in Vietnam and Sri Lanka. The average annual rainfall for both countries is determined, andtemporal variations in monthly average rainfall are examined. The results indicate a clear relationship between rainfall patterns and reservoir water extent, with increased precipitation during the monsoon season leading to higher water extents in the later months.
  arXiv  Detail & Related papers  (2023-10-09T12:51:46Z)
• Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
  High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach. Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
  arXiv  Detail & Related papers  (2023-09-12T13:28:06Z)
• An evaluation of deep learning models for predicting water depth evolution in urban floods [59.31940764426359]
  We compare different deep learning models for prediction of water depth at high spatial resolution. Deep learning models are trained to reproduce the data simulated by the CADDIES cellular-automata flood model. Our results show that the deep learning models present in general lower errors compared to the other methods.
  arXiv  Detail & Related papers  (2023-02-20T16:08:54Z)
• Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
  Climate change exacerbates the long-term soil management problem of groundwater contamination. We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO) In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
  arXiv  Detail & Related papers  (2022-11-20T06:46:35Z)
• Experimental study of time series forecasting methods for groundwater level prediction [0.0]
  We created a dataset of 1026 groundwater level time series. Each time series is made of daily measurements of groundwater levels and two variables, rainfall and evapotranspiration. We compared different predictors including local and global time series forecasting methods. Our result analysis shows that the best predictions are obtained by training a global method on past groundwater levels and rainfall data.
  arXiv  Detail & Related papers  (2022-09-28T08:58:55Z)
• Predictive Analytics for Water Asset Management: Machine Learning and Survival Analysis [55.41644538483948]
  We study a statistical and machine learning framework for the prediction of water pipe failures. We use a dataset containing the failure records of all pipes within the water distribution network in Barcelona, Spain. The results shed light on the effect of important risk factors, such as pipe geometry, age, material, and soil cover, among others.
  arXiv  Detail & Related papers  (2020-07-02T19:08:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.