Predicting the Extent of Sidoarjo Mud Flow Using Remote Sensing

The Sidoarjo mud flow in East Java is the result of a natural phenomenon in which hot mudflow occurs due to volcanic activity. The Sidoarjo mud flow resulted in a considerable ecological disaster in the area. In this study, by using the Modification of Normalized Difference Water Index (MNDWI) technique we measured the extension of the mudflow area from 2013 to 2020 using Landsat 8 satellite data imagery. This study is meant to predict the extension of the mud flow area in the research site by comparing regression and neural network techniques in order to find the best approach. The RPROP MLP neural network technique was used to predict the Sidoarjo mud-flowing area in 2021 to 2025. Surprisingly the results of these calculations showed that the RPROP MLP neural network with three hidden layers and 20 neurons performed the best, with an R square value for training of 0.77915565 and for testing of 0.78321550

Remote Sensing of Floodpath Lakes and Wetlands: A Challenging Frontier in the Monitoring of Changing Environments

Monitoring of changing lake and wetland environments has long been among the primary focus of scientific investigation, technology innovation, management practice, and decision-making analysis. Floodpath lakes and wetlands are the lakes and associated wetlands affected by seasonal variations of water level and water surface area. Floodpath lakes and wetlands are, in particular, sensitive to natural and anthropogenic impacts, such as climate change, human-induced intervention on hydrological regimes, and land use and land cover change. Rapid developments of remote sensing science and technologies, provide immense opportunities and capacities to improve our understanding of the changing lake and wetland environments. This special issue on Remote Sensing of Floodpath Lakes and Wetlands comprise featured articles reporting the latest innovative research and reflects the advancement in remote sensing applications on the theme topic. In this editorial paper, we review research developments using state-of-the-art remote sensing technologies for monitoring dynamics of floodpath lakes and wetlands; discuss challenges of remote sensing in inventory, monitoring, management, and governance of floodpath lakes and wetlands; and summarize the highlights of the articles published in this special issue

Tracking Multidecadal Lake Water Dynamics with Landsat Imagery and Topography/Bathymetry

An edited version of this paper was published by AGU. Copyright 2019 American Geophysical Union.Water resource management is of critical importance due to its close relationship with nearly every industry, field, and lifeform on this planet. The success of future water management will rely upon having detailed data of current and historic water dynamics. This research leverages Google Earth Engine and uses Landsat 5 imagery in conjunction with bathymetry and Shuttle Radar Topography Mission digital elevation model data to analyze long‐term lake dynamics (water surface elevation, surface area, volume, volume change, and frequency) for Lake McConaughy in Nebraska, USA. Water surface elevation was estimated by extracting elevation values from underlying bathymetry and digital elevations models using 5,994 different combinations of water indices, water boundaries, and statistics for 100 time periods spanning 1985–2009. Surface elevation calculations were as accurate as 0.768 m root mean square error (CI95% [0.657, 0.885]). Water volume change calculations found a maximum change of 1.568 km3 and a minimum total volume of only 23.97% of the maximum reservoir volume. Seasonal and long‐term trends were identified, which have major affects regarding regional agriculture, local recreation, and lake water quality. This research fills an existing gap in optical remote sensing‐based monitoring of lakes and reservoirs, is more robust and outperforms other commonly used monitoring techniques, increases the number of water bodies available for long‐term studies, introduces a scalable framework deployable within Google Earth Engine, and enables data collection of both gauged and ungauged water bodies, which will substantially increase our knowledge and understanding of these critical ecosystems

The impact of climate change and climate variability on coastal wetland ecosystem dynamics

Magister Artium - MAThis thesis investigates the influence of climate change and climatic variability on wetland ecosystems (coastal and inland wetlands) on the Agulhas coastal plain. Firstly, this research examines coastal wetland ecosystem resilience to sea level rise by modelling sea level rise trajectories for the Droё River wetland. The rate of sediment accretion was modelled relative to IPCC sea level rise estimates for multiple RCP scenarios. For each scenario, inundation by neap and spring tide and the 2-, 4- and 8-year recurrence interval water level was modelled over a period of 200 years. When tidal variation is considered, the rate of sediment accretion exceeds rising sea levels associated with climate change, resulting in no major changes in terms of inundation. When sea level rise scenarios were modelled in conjunction with the recurrence interval water levels, flooding of the coastal wetland was much greater than current levels for the 1 in 4 and 1 in 8 year events. The study suggests that for this wetland, variability of flows may be a key factor contributing to wetland resilience. Secondly, the thesis examines the variability of open wetland water surface areas and their relation to rainfall to determine wetland hydrological inputs for the Nuwejaars wetland system and respective wetlands. A remote sensing approach was adopted, Landsat 5 TM and 8 OLI multispectral imagery were used to detect changes of water surfaces for the period 1989 to 2017. Water surfaces were enhanced and extracted using the Modified Normalized Difference Water Index of Xu (2006). The coefficient of variation of wetland water surface area was determined. The variability ranges from low to high for respective wetlands. A correlation analysis of wetland water surfaces and local and catchment rainfall for the preceding 1, 3, 6, 9, 12 and 24 months was undertaken. The preceding month and associated inputs explains the annual variability of surface waters. The study suggests that, the variability of wetland water surface area are related to variations to water inputs and groundwater, as well as variations in water outputs such as evapotranspiration and an outlet channel

Tracking Multi-Decadal Lake Dynamics using Optical Imagery, Digital Elevation Models, and Bathymetric Datasets

The goal of this research is to review the current state of long-term, multi-decadal lake dynamic monitoring and develop novel techniques for scalable analysis at local, regional, and global levels. This dissertation is comprised of three chapters formatted as journal manuscripts with each chapter progressively addressing some key limitation in current lake dynamic monitoring methodologies. Chapter 1 tracks lake dynamics (surface elevation, surface area, volume, and volume change) for a single water body, Lake McConaughy, which is the largest lake and reservoir in the state of Nebraska, using the cloud-based geospatial analysis platform Google Earth Engine. Lake dynamics were estimated using bathymetric survey data, the Shuttle Radar Topography Mission 30-meter digital elevation model, and Landsat 5 image composites for 100 time periods between 1984 and 2009. Water surface elevation was estimated and assessed for 5,994 different combinations of water indices, segmentation thresholds, water boundaries, and statistics and produced elevations as accurate as 0.768 m CI95% [0.657, 0.885] root-mean-square-error. The method also detected seasonal and long-term trends which would have major implications for regional agriculture, recreation, and water quality. Chapter 1 was published as an article in the peer-reviewed journal Water Resources Research in October 2019. Chapter 2 expands and improves upon the techniques explored in Chapter 1 in multiple ways. First, the techniques were improved to remove image contamination sources such as snow, ice, cloud cover, shadow, and sensor error for individual images using the Pixel Quality Assurance (QA) band available as a part of the Landsat 4, 5, 7, and 8 Top-of-Atmosphere Tier-1 Collection-1 archives. Using the Pixel QA band information, image contamination was removed from each image between August 1982 and December 2017 and water surface elevation was estimated with the remaining visible water boundary extents overlaying merged National Elevation Dataset digital elevation model and bathymetric survey data resampled to 30-meters which resulted in enhanced temporal resolution compared to the techniques used in Chapter 1. Second, the analysis was expanded from a single water body to fifty-two lakes/reservoirs to provide a better understanding of how the techniques generalize to imagery and water bodies encompassing a wide range of ecotypes, geologies, climates, and management strategies. A variety of common water indices, such as the Modified Normalized Difference Water Index, naïve and dynamic water indices, water boundary types, and filtering strategies were tested and individual lake accuracies are as low as 0.191m RMSE CI95%[0.129, 0.243], and 45 of the 52 lakes produced sub-meter root-mean-squared-error accuracies. Furthermore, accuracy of surface elevation estimates is highly correlated with the mean slope of surrounding terrain with low-slope shorelines having greater accuracy than high-slope shorelines such as those in canyon-filled reservoirs or in mountainous regions. Overall, the improved techniques extend our ability to track long-term lake dynamics to lakes with bathymetric datasets while lacking in-situ hydrological stations, provide a framework for scale-able analysis in Google Earth Engine, and balance a need between high-accuracy estimates and maximum temporal resolution. Bathymetric survey data, such as that used in Chapters 1 and 2 is, unfortunately, not available for most water bodies at regional and global scales. Chapter 3 introduces a method of tracking long-term lake dynamics without bathymetry data and only using available digital elevation models such as Shuttle Radar Topography Mission, the National Elevation Dataset, and Advanced Land Observing Satellite. In digital elevation models, the water surface is often, but not always, hydroflattened producing a flat surface approximating the surface of the water at the time of the data capture which precludes using water boundaries like those in Chapter 1 and Chapter 2 to estimate water level when it is lower than the hydroflattened surface in the digital elevation model. However, using hypsometric relationships developed from the digital elevation models, subsurface water dynamics can still be estimated by extrapolating the low water levels using regression, albeit with increased uncertainty compared to levels above the hydroflattened surface. Using multiple digital elevation models, the lowest hydroflattened surface can be identified for each water body which reduces uncertainty for low water levels by reducing the extrapolation distance to those values while simultaneously increasing the number of above hydroflattened surface estimates. In addition to low-level uncertainty, hypsometric techniques are highly impacted by image contamination such as cloud, cloud shadow, snow, ice, and sensor error which reduces the observable water surface area resulting in erroneous surface elevation, volume, and volume change estimates. To help alleviate this issue, a technique of using proportional hypsometry was developed to remove contamination effects. Together, using the lowest hydroflattened surface and proportional hypsometry, this research produced 12,680 additional water surface elevation estimates for 46 lakes in comparison to traditional hypsometric techniques, reduced the number of sub-hydroflattened water surface estimates by 549 or more compared to individually using any of the three digital elevation models assessed, and lays the groundwork for regional and global scale surface water dynamic research without bathymetric survey data

Application of Landsat Imagery to Investigate Lake Area Variations and Relict Gull Habitat in Hongjian Lake, Ordos Plateau, China

Lakes in arid and semi-arid regions have an irreplaceable and important role in the local environment and wildlife habitat protection. Relict Gull (Larus relictus), which is listed as a “vulnerable” bird species in the IUCN Red List, uses only islands in lakes for habitat. The habitat with the largest colonies in Hongjian Lake (HL), which is located in Shaanxi Province in China, has been severely threatened by persistent lake shrinkage, yet the variations in the area of the lake and the islands are poorly understood due to a lack of in situ observations. In this study, using the Modified Normalized Difference Water Index, 336 Landsat remote sensing images from 1988–2015 were used to extract the monthly HL water area and lake island area, and the driving factors were investigated by correlation analysis. The results show that the lake area during 1988–2015 exhibited large fluctuations and an overall downward trend of −0.94 km2/year, and that the lake area ranged from 55.02 km2 in 1997 to 30.90 km2 in 2015. The cumulative anomaly analysis diagnosed the lake variations as two sub-periods with different characteristics and leading driving factors. The average and change trend were 52.88 and 0.21 km2/year during 1988–1998 and 38.85 and −1.04 km2/year during 1999–2015, respectively. During 1988–1998, the relatively high precipitation, low evapotranspiration, and low levels of human activity resulted in a weak increase in the area of HL. However, in 1999–2015, the more severe human activity as well as climate warming resulted in a fast decrease in the area of HL. The variations in lake island area were dependent on the area of HL, which ranged from 0.02 km2 to 0.22 km2. As the lake size declined, the islands successively outcropped in the form of the four island zones, and the two zones located in Northwest and South of HL were the most important habitats for Relict Gull. The formation of these island zones can provide enough space for Relict Gull breeding