Melt Pond Retrieval Based on the LinearPolar Algorithm Using Landsat Data

The formation and distribution of melt ponds have an important influence on the Arctic climate. Therefore, it is necessary to obtain more accurate information on melt ponds on Arctic sea ice by remote sensing. The present large-scale melt pond products, especially the melt pond fraction (MPF), still require verification, and using very high resolution optical satellite remote sensing data is a good way to verify the large-scale retrieval of MPF products. Unlike most MPF algorithms using very high resolution data, the LinearPolar algorithm using Sentinel-2 data considers the albedo of melt ponds unfixed. In this paper, by selecting the best band combination, we applied this algorithm to Landsat 8 (L8) data. Moreover, Sentinel-2 data, as well as support vector machine (SVM) and iterative self-organizing data analysis technique (ISODATA) algorithms, are used as the comparison and verification data. The results show that the recognition accuracy of the LinearPolar algorithm for melt ponds is higher than that of previous algorithms. The overall accuracy and kappa coefficient results achieved by using the LinearPolar algorithm with L8 and Sentinel-2A (S2), the SVM algorithm, and the ISODATA algorithm are 95.38% and 0.88, 94.73% and 0.86, and 92.40%and 0.80, respectively, which are much higher than those of principal component analysis (PCA) and Markus algorithms. The mean MPF (10.0%) obtained from 80 cases from L8 data based on the LinearPolar algorithm is much closer to Sentinel-2 (10.9%) than the Markus (5.0%) and PCA algorithms (4.2%), with a mean MPF difference of only 0.9%, and the correlation coefficients of the two MPFs are as high as 0.95. The overall relative error of the LinearPolar algorithm is 53.5% and 46.4% lower than that of the Markus and PCA algorithms, respectively, and the root mean square error (RMSE) is 30.9% and 27.4% lower than that of the Markus and PCA algorithms, respectively. In the cases without obvious melt ponds, the relative error is reduced more than that of those with obvious melt ponds because the LinearPolar algorithm can identify 100% of dark melt ponds and relatively small melt ponds, and the latter contributes more to the reduction in the relative error of MPF retrieval. With a wider range and longer time series, the MPF from Landsat data are more efficient than those from Sentinel-2 for verifying large-scale MPF products or obtaining long-term monitoring of a fixed area.

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Melt pond retrieval based on LinearPolar algorithm using Landsat data

10.5194/egusphere-egu21-14243 ◽

2021 ◽

Author(s):

Yuqing Qin ◽

Jie Su ◽

Mingfeng Wang

Keyword(s):

Remote Sensing ◽

Sea Ice ◽

Relative Error ◽

Arctic Sea Ice ◽

The Arctic ◽

Landsat 8 ◽

Landsat Data ◽

Melt Pond ◽

Melt Ponds ◽

Sentinel 2

The formation and distribution of melt ponds also have an important influence on the Arctic climate. It is necessary to obtain more accurate information of melt ponds on Arctic sea ice by remote sensing. Present large-scale melt pond products, especially melt pond fraction (MPF), still need a lot of verification, and it is a good way to use the very high resolution optical satellite remote sensing data to verify the retrieval MPF of low-resolution melt pond results.Most MPF algorithm such as Markus (Markus, et al., 2003) and PCA (Rosel et al., 2011) relying on fixed melt pond albedo, LinearPolar algorithm (Wang et. al., 2020) considers that the albedo of melt ponds albedo is variable, it has been proved the retrieval results of this algorithm has a high accuracy of the MPF than that of the previous algorithm based on Sentinel-2 data in Wang et al.&#8217;s work. In this paper, we applied this algorithm to Landsat 8 data. Meanwhile, Sentinel-2 data as well as SVM and ISODATA method are used as the comparison and verification data. The results show that the MPF obtained from Landsat 8 using LinearPolar algorithm is the much more closer to Sentinel-2 than Markus and PCA algorithms, and the correlation coefficients of the two MPF is as high as 0.95. The overall relative error of LinearPolar algorithm is 53.5% and 46.4% lower than Markus and PCA algorithms, respectively. And in the cases without obvious melt ponds, the relative error is reduced more than that with obvious melt ponds. This is because LinearPolar algorithm can identify 100% dark melt ponds and relatively small-scale melt ponds, and the latter contributes more to MPF retrieval.The application of LinearPolar algorithm on Landsat can cover a wider range than Sentinel and enhance the verification efficiency. Moreover, because of the longer time series of Landsat data than Sentinel data, the long-term variation trend of sea ice in fixed areas can be monitored.

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A mechanism that produces dichotomy in melt pond coverage in sea ice floes

10.5194/tc-2016-7 ◽

2016 ◽

Author(s):

Predrag Popović ◽

Dorian S. Abbot

Keyword(s):

Fixed Point ◽

Sea Ice ◽

Large Scale ◽

Bimodal Distribution ◽

Melting Behavior ◽

The Arctic ◽

Scale Model ◽

Melt Pond ◽

Melt Ponds ◽

Ice Floes

Abstract. Late in the melt season, sea ice floes in the Arctic have been observed to exhibit a large range in melt pond coverage, from heavily ponded to almost pond free. Some of these observations are consistent with a bimodal distribution in pond coverage with few intermediately ponded ice floes. We present a model for the evolution of melt ponds on sea ice floes in which conservation of hydrostatic balance in response to melt creates an unstable fixed point in pond coverage: if the initial pond coverage is below a threshold value the floe becomes unponded, and if it is above the threshold the floe becomes heavily ponded. Whether the fixed point is physically realistic depends on the differential melting rates of different points on the ice: ice at the perimeter of ponds needs to melt sufficiently slower than bare ice on average. Interestingly, this shows that the melting behavior of the narrow boundary between bare ice and melt ponds can govern the melt pond evolution of the entire ice floe. Since melt pond coverage is one of the key parameters controlling the albedo of sea ice, understanding the mechanisms that control the distribution of pond coverage will help us improve large-scale model parameterizations and sea ice forecasts in a warming climate.

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Very-high-resolution mapping of river-immersed topography by remote sensing

Progress in Physical Geography Earth and Environment ◽

10.1177/0309133308096030 ◽

2008 ◽

Vol 32 (4) ◽

pp. 403-419 ◽

Cited By ~ 72

Author(s):

Denis Feurer ◽

Jean-Stéphane Bailly ◽

Christian Puech ◽

Yann Le Coarer ◽

Alain A. Viau

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Large Scale ◽

Aerial Image ◽

Depth Measurement ◽

Very High Spatial Resolution ◽

Remote Sensing Techniques ◽

Logarithmic Relationship ◽

Ground Penetrating ◽

Very High

Remote sensing has been used to map river bathymetry for several decades. Non-contact methods are necessary in several cases: inaccessible rivers, large-scale depth mapping, very shallow rivers. The remote sensing techniques used for river bathymetry are reviewed. Frequently, these techniques have been developed for marine environment and have then been transposed to riverine environments. These techniques can be divided into two types: active remote sensing, such as ground penetrating radar and bathymetric lidar; or passive remote sensing, such as through-water photogrammetry and radiometric models. This last technique — which consists of finding a logarithmic relationship between river depth and image values — appears to be the most used. Fewer references exist for the other techniques, but lidar is an emerging technique. For each depth measurement method, we detail the physical principles and then a review of the results obtained in the field. This review shows a lack of data for very shallow rivers, where a very high spatial resolution is needed. Moreover, the cost related to aerial image acquisition is often huge. Hence we propose an application of two techniques, radiometric models and through-water photogrammetry, with very- high-resolution passive optical imagery, light platforms, and off-the-shelf cameras. We show that, in the case of the radiometric models, measurement is possible with a spatial filtering of about 1 m and a homogeneous river bottom. In contrast, with through-water photogrammetry, fine ground resolution and bottom textures are necessary.

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Multiscale assessment of northern forest characteristics based on ultra-high resolution data

Abstracts of the ICA ◽

10.5194/ica-abs-1-246-2019 ◽

2019 ◽

Vol 1 ◽

pp. 1-1

Author(s):

Andrey Medvedev ◽

Arseny Kudikov ◽

Natalia Telnova ◽

Olga Tutubalina ◽

Elena Golubeva ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Forest Ecosystems ◽

Point Clouds ◽

Tree Canopy ◽

Digital Terrain ◽

Dense Point ◽

Individual Trees ◽

Very High ◽

Sentinel 2

Abstract. The algorithms for quantitative estimates of various structural and functional parameters of forest ecosystems, particularly boreal forests, on high resolution remote sensing data are actively developing since the mid-2000s. For monitoring of forest ecosystems located at the Northern limit of distribution, effective not only lidar data but also the optical data obtained by unmanned aerial vehicles (UAV’s) with ultra-low altitude photography and derived products resulting from modern algorithms for the photogrammetric processing.High-detail remote sensing from UAV’s is a key level of monitoring of Northern forests at a large-scale level, ensuring the correct transition from sub - satellite ground-based studies to thematic products obtained from multi-time Hyper-and multispectral data of medium and relatively high resolution (MODIS, LANDSAT, Sentinel-2).When planning and conducting specific case studies based on UAV data, special attention should be paid to the justification of the survey methodology. In particular, the choice of a strictly defined high-altitude echelon of the survey determines the recognition of the objects of study and the possibility of reliable determination of its properties and features. To study the parameters of forest ecosystems at the level of individual trees and at the level of forest plantations, we selected two different-height echelons of survey from ultra-low altitudes: from 50 m, which allowed us to obtain ultra-high-detailed data for each sample area provided by detailed ground-based studies with sub-tree account, and from 100 m-to obtain derived characteristics of forest communities within the area equivalent to 3 pixels of thematic MODIS products with a spatial resolution of 250 m. The data of optical survey with UAV were obtained in July 2018 for 22 plots located in the central part of the Kola Peninsula and representative of different types of North taiga stands and their dynamics under climate change.At the stage of preprocessing images were obtained dense point clouds, characterizing both vertical and horizontal structure of stands. Digital terrain and terrain models and tree canopy models were obtained after cloud filtering and classification. Algorithms of automated segmentation and classification have been developed and tested to obtain such characteristics of stands as the height of individual trees, the area of crown projections, the projective cover of the tree-shrub layer. The obtained characteristics are aggregated by cells of a regular network with the dimension corresponding to the spatial resolution of Sentinel-2 and Landsat-8 data.The main results of the works are digital spatial datasets for 22 sample plots: raw data with very high resolution imagery (optical images with very high resolution, dense point clouds, RGB-orthophoto) and create based on a thematic derivative products (digital terrain model, topography, tree canopy cover; map of the heights and projections of the crowns of trees, percent cover of tree and shrub vegetation).

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A simple model for the evolution of melt pond coverage on permeable Arctic sea ice

The Cryosphere ◽

10.5194/tc-11-1149-2017 ◽

2017 ◽

Vol 11 (3) ◽

pp. 1149-1172 ◽

Cited By ~ 1

Author(s):

Predrag Popović ◽

Dorian Abbot

Keyword(s):

Simple Model ◽

Sea Ice ◽

Large Scale ◽

Arctic Sea Ice ◽

Rate Of Change ◽

The Arctic ◽

Scale Model ◽

Physical Parameters ◽

Melt Pond ◽

Melt Ponds

Abstract. As the melt season progresses, sea ice in the Arctic often becomes permeable enough to allow for nearly complete drainage of meltwater that has collected on the ice surface. Melt ponds that remain after drainage are hydraulically connected to the ocean and correspond to regions of sea ice whose surface is below sea level. We present a simple model for the evolution of melt pond coverage on such permeable sea ice floes in which we allow for spatially varying ice melt rates and assume the whole floe is in hydrostatic balance. The model is represented by two simple ordinary differential equations, where the rate of change of pond coverage depends on the pond coverage. All the physical parameters of the system are summarized by four strengths that control the relative importance of the terms in the equations. The model both fits observations and allows us to understand the behavior of melt ponds in a way that is often not possible with more complex models. Examples of insights we can gain from the model are that (1) the pond growth rate is more sensitive to changes in bare sea ice albedo than changes in pond albedo, (2) ponds grow slower on smoother ice, and (3) ponds respond strongest to freeboard sinking on first-year ice and sidewall melting on multiyear ice. We also show that under a global warming scenario, pond coverage would increase, decreasing the overall ice albedo and leading to ice thinning that is likely comparable to thinning due to direct forcing. Since melt pond coverage is one of the key parameters controlling the albedo of sea ice, understanding the mechanisms that control the distribution of pond coverage will help improve large-scale model parameterizations and sea ice forecasts in a warming climate.

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CURRENT CHALLENGES IN OPERATIONAL VERY HIGH RESOLUTION LAND-COVER MAPPING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b2-2020-703-2020 ◽

2020 ◽

Vol XLIII-B2-2020 ◽

pp. 703-710

Author(s):

C. Mallet ◽

A. Le Bris

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Land Cover ◽

Large Scale ◽

Spatial Scales ◽

Remote Sensing Data ◽

End Users ◽

Land Cover Mapping ◽

Human In The Loop ◽

Very High

Abstract. Many land-cover products have been made available for a large range of end-users over the last ten years, even at global scales. In particular, remote sensing data analysis has proved to be the most feasible solution for automation purposes, at multiple spatial scales. However, current solutions are not sufficient for designing better products, adapted to real-case applications, operational constraints, and the generation of services, built upon these core layers. In this paper, we review the main requirements and the recent changes in remote sensing for the specific case of very high resolution land-cover mapping. We also comment current and evaluate challenges for the optimal exploitation of Earth Observation images with the aim of automatically generating maps tailored to specific end-users’ needs. We advocate for more challenging large-scale benchmarks and for human-in-the-loop solutions.

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Enhancement of Detecting Permanent Water and Temporary Water in Flood Disasters by Fusing Sentinel-1 and Sentinel-2 Imagery Using Deep Learning Algorithms: Demonstration of Sen1Floods11 Benchmark Datasets

Remote Sensing ◽

10.3390/rs13112220 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2220

Author(s):

Yanbing Bai ◽

Wenqi Wu ◽

Zhengxin Yang ◽

Jinze Yu ◽

Bo Zhao ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Large Scale ◽

Learning Algorithms ◽

Flood Events ◽

Test Set ◽

Source Data ◽

Flood Disasters ◽

Temporary Water ◽

Sentinel 2

Identifying permanent water and temporary water in flood disasters efficiently has mainly relied on change detection method from multi-temporal remote sensing imageries, but estimating the water type in flood disaster events from only post-flood remote sensing imageries still remains challenging. Research progress in recent years has demonstrated the excellent potential of multi-source data fusion and deep learning algorithms in improving flood detection, while this field has only been studied initially due to the lack of large-scale labelled remote sensing images of flood events. Here, we present new deep learning algorithms and a multi-source data fusion driven flood inundation mapping approach by leveraging a large-scale publicly available Sen1Flood11 dataset consisting of roughly 4831 labelled Sentinel-1 SAR and Sentinel-2 optical imagery gathered from flood events worldwide in recent years. Specifically, we proposed an automatic segmentation method for surface water, permanent water, and temporary water identification, and all tasks share the same convolutional neural network architecture. We utilize focal loss to deal with the class (water/non-water) imbalance problem. Thorough ablation experiments and analysis confirmed the effectiveness of various proposed designs. In comparison experiments, the method proposed in this paper is superior to other classical models. Our model achieves a mean Intersection over Union (mIoU) of 52.99%, Intersection over Union (IoU) of 52.30%, and Overall Accuracy (OA) of 92.81% on the Sen1Flood11 test set. On the Sen1Flood11 Bolivia test set, our model also achieves very high mIoU (47.88%), IoU (76.74%), and OA (95.59%) and shows good generalization ability.

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Estimating Forest Canopy Cover by Multiscale Remote Sensing in Northeast Jiangxi, China

Land ◽

10.3390/land10040433 ◽

2021 ◽

Vol 10 (4) ◽

pp. 433

Author(s):

Xiaolan Huang ◽

Weicheng Wu ◽

Tingting Shen ◽

Lifeng Xie ◽

Yaozu Qin ◽

...

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Forest Canopy ◽

Canopy Cover ◽

Google Earth ◽

Tree Canopy ◽

Landsat Data ◽

High Resolution Data ◽

Modis Ndvi ◽

Tree Canopy Cover

This research was focused on estimation of tree canopy cover (CC) by multiscale remote sensing in south China. The key aim is to establish the relationship between CC and woody NDVI (NDVIW) or to build a CC-NDVIW model taking northeast Jiangxi as an example. Based on field CC measurements, this research used Google Earth as a complementary source to measure CC. In total, 63 sample plots of CC were created, among which 45 were applied for modeling and the remaining 18 were employed for verification. In order to ascertain the ratio R of NDVIW to the satellite observed NDVI, a 20-year time-series MODIS NDVI dataset was utilized for decomposition to obtain the NDVIW component, and then the ratio R was calculated with the equation R = (NDVIW/NDVI) *100%, respectively, for forest (CC >60%), medium woodland (CC = 25–60%) and sparse woodland (CC 1–25%). Landsat TM and OLI images that had been orthorectified by the provider USGS were atmospherically corrected using the COST model and used to derive NDVIL. R was multiplied for the NDVIL image to extract the woody NDVI (NDVIWL) from Landsat data for each of these plots. The 45 plots of CC data were linearly fitted to the NDVIWL, and a model with CC = 103.843 NDVIW + 6.157 (R2 = 0.881) was obtained. This equation was applied to predict CC at the 18 verification plots and a good agreement was found (R2 = 0.897). This validated CC-NDVIW model was further applied to the woody NDVI of forest, medium woodland and sparse woodland derived from Landsat data for regional CC estimation. An independent group of 24 measured plots was utilized for validation of the results, and an accuracy of 83.0% was obtained. Thence, the developed model has high predictivity and is suitable for large-scale estimation of CC using high-resolution data.

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Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy

Sensors ◽

10.3390/s21123982 ◽

2021 ◽

Vol 21 (12) ◽

pp. 3982

Author(s):

Giacomo Lazzeri ◽

William Frodella ◽

Guglielmo Rossi ◽

Sandro Moretti

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Fire Severity ◽

Experimental Methodology ◽

Vegetation Recovery ◽

Burned Area ◽

Remotely Sensed Data ◽

The Impact ◽

Sentinel 2

Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote sensing techniques such as multispectral Sentinel-2, PRISMA hyperspectral satellite, and UAV (unmanned aerial vehicle) remotely-sensed data for the multitemporal mapping of burned areas by soil–vegetation recovery analysis in two test sites in Portugal and Italy. In case study one, innovative multiplatform data classification was performed with the correlation between Sentinel-2 RBR (relativized burn ratio) fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison leading to a converging classification. In the adopted methodology, RBR burned area analysis and vegetation recovery was tested for accordance with biophysical vegetation parameters (LAI, fCover, and fAPAR). In case study two, a UAV-sensed NDVI index was adopted for high-resolution mapping data collection. At a large scale, the Sentinel-2 RBR index proved to be efficient for burned area analysis, from both fire severity and vegetation recovery phenomena perspectives. Despite the elapsed time between the event and the acquisition, PRISMA hyperspectral converging classification based on Sentinel-2 was able to detect and discriminate different spectral signatures corresponding to different fire severity classes. At a slope scale, the UAV platform proved to be an effective tool for mapping and characterizing the burned area, giving clear advantage with respect to filed GPS mapping. Results highlighted that UAV platforms, if equipped with a hyperspectral sensor and used in a synergistic approach with PRISMA, would create a useful tool for satellite acquired data scene classification, allowing for the acquisition of a ground truth.

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