Impact of Image-Processing Routines on Mapping Glacier Surface Facies from Svalbard and the Himalayas Using Pixel-Based Methods

Jawak, Shridhar D.; Wankhede, Sagar F.; Luis, Alvarinho J.; Balakrishna, Keshava

doi:10.3390/rs14061414

Cited by 13 publications

(15 citation statements)

References 155 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the face of global climate change, monitoring the health and degradations of ecosystems is becoming of crucial importance to understand phenomena, anticipate changes and engage appropriate actions [ 1 ]. A first approach consists of analyzing high-resolution satellite images to measure visible evolutions of some phenomena at a large scale (e.g., melting of mountain glaciers [ 2 ], reduction of surface waters [ 3 ], changes in land use and land cover [ 4 ]). A second one consists of deploying sensor nodes directly in the environment, with the aim to perform precise measurements on specific points and obtain information which is impossible to access from an aerial system.…”

Section: Introductionmentioning

confidence: 99%

Data Collection from Buried Sensor Nodes by Means of an Unmanned Aerial Vehicle

Cariou

Moiroux-Arvis

Pinet

et al. 2022

Sensors

View full text Add to dashboard Cite

The development of Wireless Underground Sensor Networks (WUSNs) is a recent research axis based on sensor nodes buried a few dozen centimeters deep. The communication ranges are, however, highly reduced due to the high attenuation of electromagnetic waves in soil, leading to issues of data collection. This paper proposes to embed a data collector on an Unmanned Aerial Vehicle (UAV) coming close to each buried sensor node. The whole system was developed (sensor nodes, data collector, gateway) and experimentations were carried out in real conditions. In hovering mode, the measurements on the RSSI levels with respect to the position of the UAV highlight the interest in maintaining a high altitude when the UAV is far from the node. In dynamic mode, the experimental results demonstrate the feasibility of carrying out the data collection task while the UAV is moving. The speed of the UAV has, however, to be adapted to the required time to collect the data. In the case of numerous buried sensor nodes, evolutionary algorithms are implemented to plan the trajectory of the UAV optimally. To the best of our knowledge, this paper is the first one that reports experiment results combining WUSN and UAV technologies.

show abstract

Section: Introductionmentioning

confidence: 99%

Data Collection from Buried Sensor Nodes by Means of an Unmanned Aerial Vehicle

Cariou

Moiroux-Arvis

Pinet

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Nevertheless, we present the results of MHD here to illustrate its performance. For further details of this experiment and more extensive analysis the reader is referred to our previous work 4,5,15 .…”

Section: Resultsmentioning

confidence: 99%

“…However, it delivers the maximum utility for visually identifying facies as the perception of the analyst is improved at finer pixel size in assigning training data either to machine learning (ML) algorithms or for developing segmentation/geographic object-based image analysis (GEOBIA) processes 4 . Optical mapping of glacier facies also depends on sensor properties, region, mapping methods, and available ancillary information 5 . To better understand the importance of processing mechanisms for monitoring glacier facies, a thorough analysis of every step of the methodology is necessary.…”

Section: Introductionmentioning

confidence: 99%

Modulating spectral reflectance and its impact on thematic mapping: an analysis on glacier facies using very high-resolution satellite imagery

Wankhede,

Pandit,

Jawak

et al. 2023

Earth Resources and Environmental Remote Sensing/Gis Applications XIV

Self Cite

View full text Add to dashboard Cite

Analysis of the spectral properties of glacier surface facies representing glaciological zones is an established method for characterizing facies. This necessitates a reliable estimation of spectral reflectance from image pixels using appropriate atmospheric correction methods. Improving spatial resolution by pansharpening may improve visual detection of facies, but the translation of visual perception to algorithmic classification is unclear. In this study, we analyzed the changes in spectral reflectance by comparing one model-based and two image-based methods of atmospheric corrections using very high-resolution (VHR) WorldView-2/3 satellite data for selected glaciers in Svalbard and the Himalayas. Two pansharpening methods were then applied followed by 12 machine learning and three object-based image analysis (OBIA) rule sets. A combination of wavelength ranges representing traditional and recent spectrums were tested using four different spectral band combinations. In total, we compared 8100 surface facies maps to understand the impact of every image processing routine and mapping method on the final thematic categorization of facies. Broad conclusions suggest that VHR data does not require shortwave infrared ranges; OBIA is more accurate and less sensitive to image processing variations; machine learning is more efficient but readily influenced by image processing. A spatial-spectral information OBIA ruleset yielded the highest overall accuracy (0.86). Segmentation parameters were found to be consistent across study regions and may be transferable outside the current study. This study demonstrates the need for testing image processing induced variations for cryospheric applications and may be assessed on open satellite data for long-term standardized mapping of glacier surface facies.

show abstract

“…While they found that enhanced spatial resolution does not necessarily improve mapping, Paul et al [17] suggested that improved resolution would deliver better final thematic products. However, Jawak et al [18] tested the impact that enhanced spatial resolution can have on conventional and advanced pixel-based image analysis (PBIA) for mapping facies using very high resolution (VHR) Worldview-2 (WV-2) satellite data. Their results suggest that pansharpening does not improve end classification.…”

Section: Introductionmentioning

confidence: 99%

“…The general recommendation for selection of atmospheric correction methods and pansharpening centers on the specific application of the study [25,36]. VNIR based PBIA using multiple processing routines is discussed in Jawak et al [18] (henceforward referred to as Paper 1). In Paper 1, it was observed that FLAASH delivers consistent results across the processing schemes, whereas pansharpening by HCS and GS degraded the classification results.…”

Section: Introductionmentioning

confidence: 99%

Multispectral Characteristics of Glacier Surface Facies (Chandra-Bhaga Basin, Himalaya, and Ny-Ålesund, Svalbard) through Investigations of Pixel and Object-Based Mapping Using Variable Processing Routines

Jawak¹,

Wankhede

Luis

et al. 2022

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Fundamental image processing methods, such as atmospheric corrections and pansharpening, influence the signal of the pixel. This morphs the spectral signature of target features causing a change in both the final spectra and the way different mapping methods may assign thematic classes. In the current study, we aim to identify the variations induced by popular image processing methods in the spectral reflectance and final thematic maps of facies. To this end, we have tested three different atmospheric corrections: (a) Quick Atmospheric Correction (QUAC), (b) Dark Object Subtraction (DOS), and (c) Fast Line-of-Sight Atmospheric Analysis of Hypercubes (FLAASH), and two pansharpening methods: (a) Hyperspherical Color Sharpening (HCS) and (b) Gram–Schmidt (GS). WorldView-2 and WorldView-3 satellite images over Chandra-Bhaga Basin, Himalaya, and Ny-Ålesund, Svalbard are tested via spectral subsets in traditional (BGRN1), unconventional (CYRN2), visible to near-infrared (VNIR), and the complete available spectrum (VNIR_SWIR). Thematic mapping was comparatively performed using 12 pixel-based (PBIA) algorithms and 3 object-based (GEOBIA) rule sets. Thus, we test the impact of varying image processing routines, effectiveness of specific spectral bands, utility of PBIA, and versatility of GEOBIA for mapping facies. Our findings suggest that the image processing routines exert an extreme impact on the end spectral reflectance. DOS delivers the most reliable performance (overall accuracy = 0.64) averaged across all processing schemes. GEOBIA delivers much higher accuracy when the QUAC correction is employed and if the image is enhanced by GS pansharpening (overall accuracy = 0.79). SWIR bands have not enhanced the classification results and VNIR band combination yields superior performance (overall accuracy = 0.59). The maximum likelihood classifier (PBIA) delivers consistent and reliable performance (overall accuracy = 0.61) across all processing schemes and can be used after DOS correction without pansharpening, as it deteriorates spectral information. GEOBIA appears to be robust against modulations in atmospheric corrections but is enhanced by pansharpening. When utilizing GEOBIA, we find that a combination of spatial and spectral object features (rule set 3) delivers the best performance (overall accuracy = 0.86), rather than relying only on spectral (rule set 1) or spatial (rule set 2) object features. The multiresolution segmentation parameters used here may be transferable to other very high resolution (VHR) VNIR mapping of facies as it yielded consistent objects across all processing schemes.

show abstract

Impact of Image-Processing Routines on Mapping Glacier Surface Facies from Svalbard and the Himalayas Using Pixel-Based Methods

Cited by 13 publications

References 155 publications

Data Collection from Buried Sensor Nodes by Means of an Unmanned Aerial Vehicle

Data Collection from Buried Sensor Nodes by Means of an Unmanned Aerial Vehicle

Modulating spectral reflectance and its impact on thematic mapping: an analysis on glacier facies using very high-resolution satellite imagery

Multispectral Characteristics of Glacier Surface Facies (Chandra-Bhaga Basin, Himalaya, and Ny-Ålesund, Svalbard) through Investigations of Pixel and Object-Based Mapping Using Variable Processing Routines

Contact Info

Product

Resources

About