Robust quantification of riverine land cover dynamics by high-resolution remote sensing

Milani, Gillian; Volpi, Michele; Tonolla, Diego; Doering, Michael; Robinson, Christopher T.; Kneubühler, Mathias; Schaepman, Michael E.

doi:10.1016/j.rse.2018.08.035

Cited by 31 publications

(16 citation statements)

References 69 publications

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“…eCognition Developer software and the Google Earth Engine (GEE) also facilitate automatic land use classification 45,46 , but automatic classification still has certain limitations. In particular, when analyzing dynamic changes, errors in land classification may spread to dynamic quantification 47 . In this study, manual visual means were used to correct the results of the automatic classification, which ensured the accuracy of the mapping and made the overall accuracy higher than 90%.…”

Section: Ecological Quality Evaluation Ecological Quality Is Used Tomentioning

confidence: 99%

Spatial–temporal dynamics and driving factor analysis of urban ecological land in Zhuhai city, China

Zhang

2020

Sci Rep

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Ecological land is a type of land that has considerable ecological value. Understanding the evolution of urban ecological land in Zhuhai, China, holds great significance for revealing the evolution of ecological land in the Dawan District of southern China. We explored the temporal and spatial variation in urban ecological land in Zhuhai using the transformation matrix, equivalent ecological land, landscape index and ecological land center of gravity migration methods. Multivariate logistic regression was used to analyze the mechanism of ecological land change, and a transition probability map of the ecological land in the study area was drawn. The results showed the following. (1) From 1991 to 2018, the area of ecological land in Zhuhai city continuously decreased, with a reduction in area of 274.8 km2, or 32.3%. Sharp changes mainly occurred from 1991 to 2000. (2) The ecological land in the study area has gradually become fragmented, and the degree of landscape heterogeneity has increased. Affected by the expansion of the outer edge of the city to the southwest and the construction of ecological land within the city, the center of gravity of the ecological land has shifted to the northeast by 1346 m. (3) The elevation, slope, distance from built-up land and growth rate of built-up land are important factors influencing the transformation of ecological land. In the future, rivers and shallow coastal waters, tidal flats, and grasslands in the study area have the highest probability of transformation. The Jinwan District and Xiangzhou District will face severe ecological land protection pressure. The method of spatial–temporal analysis of urban ecological land developed in this paper can be applied in similar studies on other cities, and the results obtained for Zhuhai, China, have reference value for future urban planning and ecological protection work.

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Section: Ecological Quality Evaluation Ecological Quality Is Used Tomentioning

confidence: 99%

Spatial–temporal dynamics and driving factor analysis of urban ecological land in Zhuhai city, China

Zhang

2020

Sci Rep

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“…As such, it includes a broad range of algorithms, encompassing everything from simple linear regressions to deep learning-based neural networks [52]. The application of AI/ML for geomorphic error thresholding purposes is novel, with existing studies within freshwater settings applying it predominantly for classification of land cover types from image-derived parameters (e.g., [53][54][55][56]) or for the identification of other specific features of interest such as buildings (e.g., [57]) and invasive species (e.g., [58]). As such, our ultimate aim is to create the first high resolution, spatially continuous SfM-derived topographic change models in submerged fluvial environments constrained by spatially variable error estimates.…”

mentioning

confidence: 99%

Quantifying Below-Water Fluvial Geomorphic Change: The Implications of Refraction Correction, Water Surface Elevations, and Spatially Variable Error

Woodget

Dietrich

Wilson³

2019

Remote Sensing

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Much of the geomorphic work of rivers occurs underwater. As a result, high resolutionquantification of geomorphic change in these submerged areas is important. Currently, to quantify thischange, multiple methods are required to get high resolution data for both the exposed and submergedareas. Remote sensing methods are often limited to the exposed areas due to the challenges imposedby the water, and those remote sensing methods for below the water surface require the collection ofextensive calibration data in-channel, which is time-consuming, labour-intensive, and sometimesprohibitive in dicult-to-access areas. Within this paper, we pioneer a novel approach for quantifyingabove- and below-water geomorphic change using Structure-from-Motion photogrammetry andinvestigate the implications of water surface elevations, refraction correction measures, and thespatial variability of topographic errors. We use two epochs of imagery from a site on the River Teme,Herefordshire, UK, collected using a remotely piloted aircraft system (RPAS) and processed usingStructure-from-Motion (SfM) photogrammetry. For the first time, we show that: (1) Quantification ofsubmerged geomorphic change to levels of accuracy commensurate with exposed areas is possiblewithout the need for calibration data or a dierent method from exposed areas; (2) there is minimaldierence in results produced by dierent refraction correction procedures using predominantlynadir imagery (small angle vs. multi-view), allowing users a choice of software packages/processingcomplexity; (3) improvements to our estimations of water surface elevations are critical for accuratetopographic estimation in submerged areas and can reduce mean elevation error by up to 73%;and (4) we can use machine learning, in the form of multiple linear regressions, and a Gaussian NaïveBayes classifier, based on the relationship between error and 11 independent variables, to generate ahigh resolution, spatially continuous model of geomorphic change in submerged areas, constrained byspatially variable error estimates. Our multiple regression model is capable of explaining up to 54%of magnitude and direction of topographic error, with accuracies of less than 0.04 m. With on-goingtesting and improvements, this machine learning approach has potential for routine application inspatially variable error estimation within the RPAS–SfM workflow.

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“…The first reach is located along the Sense River next to the village of Plaffeien, an investigation site of previous studies about its hydromorphology [35], floodplain habitats [36] and the use of aerial imagery [37], [38], [39]. The Sense River is one of the last rivers in Switzerland to have a near natural flow and sediment regime.…”

Section: A Study Sitesmentioning

confidence: 99%

“…Although floods are the main source of erosion of floodplain vegetation, they also create conditions favorable to the establishment of seedlings through the deposition of sediment and nutrients [82], [83]. The increase of NDVI and VF distributions after major floods may also be influenced by the deposition of woody debris on sediment bars, islands and terraces, as it occurs in the Sense River [38], introducing bursts of nutrients to these areas [84], [85]. Finally, the floodplains found in mountainous environment have been described as being very resistant and having a high recolonization capacity [86].…”

Section: A Relation Between Flow Variables and Remote Sensingmentioning

confidence: 99%

Characterizing Flood Impact on Swiss Floodplains Using Interannual Time Series of Satellite Imagery

Milani

Kneubühler

Tonolla

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Pressure on the biodiversity of ecosystems along many rivers is growing continuously due to the increasing number of hydropower facilities regulating downstream flow and sediment regimes. Despite a thorough understanding of the shortterm processes and interactions at this hydro-biosphere interface, long-term analyses of the impacts on floodplain dynamics are lacking. We used inter-annual Landsat 4, 5, 7 and 8 time series to analyze the effects of hydrological events on floodplain vegetation in four mountainous floodplains in the Swiss Alps. Using a spectral mixture analysis approach, we demonstrate that the floodplain vegetation dynamics of mountainous rivers can be recovered at a spatial resolution of 30 meters. Our results suggest that interactions between floods and floodplain vegetation are complex and not exclusively related to flood magnitude. Of the four reaches analyzed, only data gathered along the submountainous reach with a quasi-natural flow regime show a clear link between remotely sensed vegetation indices and floods. In addition, our 29-year time series shows a continuous upward trend in vegetation indices along the floodplains, strongest in the reaches affected by hydropower facilities. The approach presented in this study can be easily replicated in other mountain ranges by providing available flow data to verify the impact of hydropower on floodplain vegetation dynamics.

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Robust quantification of riverine land cover dynamics by high-resolution remote sensing

Cited by 31 publications

References 69 publications

Spatial–temporal dynamics and driving factor analysis of urban ecological land in Zhuhai city, China

Spatial–temporal dynamics and driving factor analysis of urban ecological land in Zhuhai city, China

Quantifying Below-Water Fluvial Geomorphic Change: The Implications of Refraction Correction, Water Surface Elevations, and Spatially Variable Error

Characterizing Flood Impact on Swiss Floodplains Using Interannual Time Series of Satellite Imagery

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