Dunes as palaeo-wind vanes: Investigating palaeowind regimes using semi-automated remote sensing approaches to dunefield mapping and orientation quantification 

2021 ◽  
Author(s):  
Maike Nowatzki ◽  
Kathryn Fitzsimmons ◽  
Hartwig Harder ◽  
Hans-Joachim Rosner
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Sand Transport ◽  
Human Land Use ◽  
Object Based ◽  
Digital Elevation ◽  
Normal Transport ◽  
Wind Regimes ◽  
Partial Consensus

<p>Many dryland regions of the world are at high risk of desertification from combined human land use and anthropogenic climate change. One symptom of desertification is the reactivation of previously stable dunefields. Since morphologies of stable dunes are thought to reflect wind regimes at the time of their formation, the degree to which dune orientation reflects modern winds may be one way to assess changes in wind regimes and the progression of desertification in a region.</p><p>Here we investigate the relationship between wind dynamics and desert dune orientation in one region at risk of desertification, southeast Kazakhstan in Central Asia, on the basis of open-source software and open-access datasets. Using Google Earth Engine, we map dunes or interdune spaces within six palaeo-dunefields in the Ili-Balkhash area, by performing a multi-layer object-based image analysis (OBIA) on satellite remote sensing data (Sentinel-2 optical imagery and SRTM digital elevation models). A semi-automated GIS approach is used to undertake data cleansing and the quantification of dominant palaeo-dunefield orientations. The resulting orientation trends are concurrent with the region’s topography: The dunefields within the Ili valley show a narrow, mostly E-W oriented trend concurrent with the course of the valley while the orientation ranges become broader towards the open pre-Balkhash area.</p><p>We then predict modern dune orientations by applying the maximum gross bedform-normal transport rule on reanalysed wind data for 2008-2018. This approach by Rubin and Hunter (1987) allows the deduction of sand transport and resulting bedform trends from wind direction frequencies. The predicted modern orientation trends for the dunefields in the Ili-Balkhash area yield only partial consensus with observed palaeo-bedform trends. We therefore propose that modern wind regimes are not exclusively responsible for existing dune morphologies in the region, and that dune orientation may be inherited from earlier wind regimes.</p>

scholarly journals Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine

2021 ◽  
Vol 13 (8) ◽  
pp. 1433
Author(s):  
Shobitha Shetty ◽  
Prasun Kumar Gupta ◽  
Mariana Belgiu ◽  
S. K. Srivastav
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Sampling ◽  
Sampling Design ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Multi Temporal ◽  
Google Earth Engine

Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.

Debris-induced stagnation and ensuing morphological evolution of a central Himalayan glacier

2021 ◽  
Author(s):  
Purushottam Kumar Garg ◽  
Aparna Shukla ◽  
Santosh Kumar Rai ◽  
Jairam Singh Yadav
Keyword(s):  
Remote Sensing ◽  
Morphological Evolution ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Lateral Moraine ◽  
Central Himalaya ◽  
Ablation Zone ◽  
Glacier Surface ◽  
Glacier System ◽  
Debris Cover

<p>This study presents field evidences (October 2018) and remote sensing measurements (2000-2020) to show stagnant conditions of lower ablation zone (LAZ) of the ‘companion glacier’, central Himalaya, India and its implication on the morphological evolution. The Companion glacier is named so as it accompanied the Chorabari glacier (widely studied benchmark glacier in the central Himalaya) in the distant past. Supraglacial debris thickness, supraglacial ponds anf other morphological features (e.g. lateral moraine height, supraglacial mounds) were measured/observed in the field. Glacier area, length, debris extent, surface elevation change and surface ice velocity were estimated using satellite remote sensing data from Landsat-TM/ETM+/OLI, Sentinel-MSI, Terra-ASTER and SRTM, Cartosat-1 and Google Earth images. Results show that the glacier has very small accumulation area and it is mainly fed by avalanches. The headwall of glacier is very steep which causes frequent avalanches leading to voluminous debris addition to the glacier system. Consequently, about 80% area of the glacier is debris-covered. The debris is very thick in the LAZ exceeding several meters in the LAZ and comprised of big boulders making debris thickness measurements practically impossible particularly in the snout region. However, debris thickness decreases with increasing distance from the snout and is in the order of 20-40 cm at about 2.5 km upglacier. The huge debris cover has protected the glacier ice from rapid melting. That’s why surface lowering of the glacier is less as compared to nearby Chorabari glacier. Moreover, due to (a) less mass supply from upper reaches and (b) huge debris cover, the glacier movement is very slow. The movement is too low that is allowed vegetation (some big grasses with wooded stems) to grow and survive on the glacier surface. The slow moving LAZ also causing bulging on the upper ablation zone (UAZ). Consequently, several mounds have developed on the UAZ. Thin debris slides down from mounds exposing the ice underneath for melting. Owing to these processes, spot melting is now a dominant mechanism of glacier wastage in the companion glacier. Thus, it can be summarized that careful field observations along with remote sensing estimates can be very important for understanding the glacier evolution.</p>

scholarly journals A Probabilistic Assessment of Soil Erosion Susceptibility in a Head Catchment of the Jemma Basin, Ethiopian Highlands

Geosciences ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 248 ◽  
Author(s):  
Mariaelena Cama ◽  
Calogero Schillaci ◽  
Jan Kropáček ◽  
Volker Hochschild ◽  
Alberto Bosino ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Remote Sensing Data ◽  
Gully Erosion ◽  
Entropy Model ◽  
Global Issues ◽  
Susceptibility Maps ◽  
Digital Elevation ◽  
Elevation Model ◽  
Color Composite

Soil erosion represents one of the most important global issues with serious effects on agriculture and water quality, especially in developing countries, such as Ethiopia, where rapid population growth and climatic changes affect widely mountainous areas. The Meskay catchment is a head catchment of the Jemma Basin draining into the Blue Nile (Central Ethiopia) and is characterized by high relief energy. Thus, it is exposed to high degradation dynamics, especially in the lower parts of the catchment. In this study, we aim at the geomorphological assessment of soil erosion susceptibilities. First, a geomorphological map was generated based on remote sensing observations. In particular, we mapped three categories of landforms related to (i) sheet erosion, (ii) gully erosion, and (iii) badlands using a high-resolution digital elevation model (DEM). The map was validated by a detailed field survey. Subsequently, we used the three categories as dependent variables in a probabilistic modelling approach to derive the spatial distribution of the specific process susceptibilities. In this study we applied the maximum entropy model (MaxEnt). The independent variables were derived from a set of spatial attributes describing the lithology, terrain, and land cover based on remote sensing data and DEMs. As a result, we produced three separate susceptibility maps for sheet and gully erosion as well as badlands. The resulting susceptibility maps showed good to excellent prediction performance. Moreover, to explore the mutual overlap of the three susceptibility maps, we generated a combined map as a color composite where each color represents one component of water erosion. The latter map yields useful information for land-use managers and planning purposes.

scholarly journals Agricultural Expansion in Mato Grosso from 1986–2000: A Bayesian Time Series Approach to Tracking Past Land Cover Change

2020 ◽  
Vol 12 (4) ◽  
pp. 688 ◽  
Author(s):  
Jacky Lee ◽  
Jeffrey A. Cardille ◽  
Michael T. Coe
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Spatial Resolution ◽  
Processing Speed ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Wide Audience ◽  
Data Set ◽  
Mato Grosso

Landsat 5 has produced imagery for decades that can now be viewed and manipulated in Google Earth Engine, but a general, automated way of producing a coherent time series from these images—particularly over cloudy areas in the distant past—is elusive. Here, we create a land use and land cover (LULC) time series for part of tropical Mato Grosso, Brazil, using the Bayesian Updating of Land Cover: Unsupervised (BULC-U) technique. The algorithm built backward in time from the GlobCover 2009 data set, a multi-category global LULC data set at 300 m resolution for the year 2009, combining it with Landsat time series imagery to create a land cover time series for the period 1986–2000. Despite the substantial LULC differences between the 1990s and 2009 in this area, much of the landscape remained the same: we asked whether we could harness those similarities and differences to recreate an accurate version of the earlier LULC. The GlobCover basis and the Landsat-5 images shared neither a common spatial resolution nor time frame, But BULC-U successfully combined the labels from the coarser classification with the spatial detail of Landsat. The result was an accurate fine-scale time series that quantified the expansion of deforestation in the study area, which more than doubled in size during this time. Earth Engine directly enabled the fusion of these different data sets held in its catalog: its flexible treatment of spatial resolution, rapid prototyping, and overall processing speed permitted the development and testing of this study. Many would-be users of remote sensing data are currently limited by the need to have highly specialized knowledge to create classifications of older data. The approach shown here presents fewer obstacles to participation and allows a wide audience to create their own time series of past decades. By leveraging both the varied data catalog and the processing speed of Earth Engine, this research can contribute to the rapid advances underway in multi-temporal image classification techniques. Given Earth Engine’s power and deep catalog, this research further opens up remote sensing to a rapidly growing community of researchers and managers who need to understand the long-term dynamics of terrestrial systems.

scholarly journals Garlic and Winter Wheat Identification Based on Active and Passive Satellite Imagery and the Google Earth Engine in Northern China

2020 ◽  
Vol 12 (21) ◽  
pp. 3539
Author(s):  
Haifeng Tian ◽  
Jie Pei ◽  
Jianxi Huang ◽  
Xuecao Li ◽  
Jian Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Satellite Imagery ◽  
Remote Sensing Data ◽  
Northern China ◽  
Google Earth ◽  
Kappa Coefficient ◽  
Landsat 8 ◽  
Winter Crops ◽  
Sentinel 2

Garlic and winter wheat are major economic and grain crops in China, and their boundaries have increased substantially in recent decades. Updated and accurate garlic and winter wheat maps are critical for assessing their impacts on society and the environment. Remote sensing imagery can be used to monitor spatial and temporal changes in croplands such as winter wheat and maize. However, to our knowledge, few studies are focusing on garlic area mapping. Here, we proposed a method for coupling active and passive satellite imagery for the identification of both garlic and winter wheat in Northern China. First, we used passive satellite imagery (Sentinel-2 and Landsat-8 images) to extract winter crops (garlic and winter wheat) with high accuracy. Second, we applied active satellite imagery (Sentinel-1 images) to distinguish garlic from winter wheat. Third, we generated a map of the garlic and winter wheat by coupling the above two classification results. For the evaluation of classification, the overall accuracy was 95.97%, with a kappa coefficient of 0.94 by eighteen validation quadrats (3 km by 3 km). The user’s and producer’s accuracies of garlic are 95.83% and 95.85%, respectively; and for the winter wheat, these two accuracies are 97.20% and 97.45%, respectively. This study provides a practical exploration of targeted crop identification in mixed planting areas using multisource remote sensing data.

scholarly journals Distinguishing Glaciers between Surging and Advancing by Remote Sensing: A Case Study in the Eastern Karakoram

2020 ◽  
Vol 12 (14) ◽  
pp. 2297 ◽  
Author(s):  
Mingyang Lv ◽  
Huadong Guo ◽  
Jin Yan ◽  
Kunpeng Wu ◽  
Guang Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Google Earth ◽  
High Mountain ◽  
Glacier Mass Balance ◽  
Visual Interpretation ◽  
Stable Mode ◽  
Optical Images ◽  
Digital Elevation ◽  
Glacier Surges ◽  
Elevation Model

The Karakoram has had an overall slight positive glacier mass balance since the end of 20th century, which is anomalous given that most other regions in High Mountain Asia have had negative changes. A large number of advancing, retreating, and surging glaciers are heterogeneously mixed in the Karakoram increasing the difficulties and inaccuracies to identify glacier surges. We found two adjacent glaciers in the eastern Karakoram behaving differently from 1995 to 2019: one was surging and the other was advancing. In order to figure out the differences existing between them and the potential controls on surges in this region, we collected satellite images from Landsat series, ASTER, and Google Earth, along with two sets of digital elevation model. Utilizing visual interpretation, feature tracking of optical images, and differencing between digital elevation models, three major differences were observed: (1) the evolution profiles of the terminus positions occupied different change patterns; (2) the surging glacier experienced a dramatic fluctuation in the surface velocities during and after the event, while the advancing glacier flowed in a stable mode; and (3) surface elevation of the surging glacier decreased in the reservoir and increased in the receiving zone. However, the advancing glacier only had an obvious elevation increase over its terminus part. These differences can be regarded as standards for surge identification in mountain ranges. After combining the differences with regional meteorological conditions, we suggested that changes of thermal and hydrological conditions could play a role in the surge occurrence, in addition, geomorphological characteristics and increasing warming climate might also be part of it. This research strongly contributes to the literatures of glacial motion and glacier mass change in the eastern Karakoram through remote sensing.

scholarly journals The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform

2018 ◽  
Vol 11 (1) ◽  
pp. 43 ◽  
Author(s):  
Masoud Mahdianpari ◽  
Bahram Salehi ◽  
Fariba Mohammadimanesh ◽  
Saeid Homayouni ◽  
Eric Gill
Keyword(s):  
Large Scale ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Wetland Mapping ◽  
Object Based ◽  
Wetland Inventory ◽  
Inventory Map ◽  
Google Earth Engine ◽  
Provincial Scale ◽  
Sentinel 2

Wetlands are one of the most important ecosystems that provide a desirable habitat for a great variety of flora and fauna. Wetland mapping and modeling using Earth Observation (EO) data are essential for natural resource management at both regional and national levels. However, accurate wetland mapping is challenging, especially on a large scale, given their heterogeneous and fragmented landscape, as well as the spectral similarity of differing wetland classes. Currently, precise, consistent, and comprehensive wetland inventories on a national- or provincial-scale are lacking globally, with most studies focused on the generation of local-scale maps from limited remote sensing data. Leveraging the Google Earth Engine (GEE) computational power and the availability of high spatial resolution remote sensing data collected by Copernicus Sentinels, this study introduces the first detailed, provincial-scale wetland inventory map of one of the richest Canadian provinces in terms of wetland extent. In particular, multi-year summer Synthetic Aperture Radar (SAR) Sentinel-1 and optical Sentinel-2 data composites were used to identify the spatial distribution of five wetland and three non-wetland classes on the Island of Newfoundland, covering an approximate area of 106,000 km2. The classification results were evaluated using both pixel-based and object-based random forest (RF) classifications implemented on the GEE platform. The results revealed the superiority of the object-based approach relative to the pixel-based classification for wetland mapping. Although the classification using multi-year optical data was more accurate compared to that of SAR, the inclusion of both types of data significantly improved the classification accuracies of wetland classes. In particular, an overall accuracy of 88.37% and a Kappa coefficient of 0.85 were achieved with the multi-year summer SAR/optical composite using an object-based RF classification, wherein all wetland and non-wetland classes were correctly identified with accuracies beyond 70% and 90%, respectively. The results suggest a paradigm-shift from standard static products and approaches toward generating more dynamic, on-demand, large-scale wetland coverage maps through advanced cloud computing resources that simplify access to and processing of the “Geo Big Data.” In addition, the resulting ever-demanding inventory map of Newfoundland is of great interest to and can be used by many stakeholders, including federal and provincial governments, municipalities, NGOs, and environmental consultants to name a few.

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