Resolution Satellite Images Research Articles

Mapping Oyster Reef Distribution Using Kompsat-2/3 and Linear Spectral Unmixing Algorithm - A Case Study at Hwangdo Tidal Flat

Kim, K.-L. and Ryu, J.-H., 2020. Mapping oyster reef distribution using Kompsat-2/3 and linear spectral unmixing algorithm – A case study at Hwangdo tidal flat. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 246-253. Coconut Creek (Florida), ISSN 0749-0208.Oyster areas are widely used as species of biological indicator, and they are also the most important shellfish in terms of ecosystem economic valuation. Oysters are production on the west coast before 2007 accounted for about 5 % of the nation's total production, but the 2007 Hebei Spirit oil spill caused production to plunge to less than 1 %. Therefore, spatial distribution maps of oyster reefs are required to help local authorities define management strategies. In this study, Kompsat-2/3 was used to map oyster reef distribution and analyze the distribution of oyster reefs based on linear spctral unmixing in Hwangdo tidal flat. A spectral library, collected in situ for various conditions with a field spectroradiometer, was used to conduct linear spectral unmixing classifications on Kompsat-2/3 data. The classification result shows very high accuracy, with an overall accuracy of 95 % or more, and there were misclassifications in some areas. The most causes of misclassification are the similarity of spectral characteristics and the limitations of the spatial and spectral resolutions of satellite images. For this reason, it was difficult to distinguish between oyster reefs and area distributed with many macrobenthos, and small-young oyster reefs were difficult to classify due to very weak reflectivity. In addition, the sand bar, it was is difficult to distinguish between oyster reefs related to dead reefs and sandbars because of the high reflectivity of these areas in the imagery. As a result of analyzing the change in the oyster reef area, it increased in 2019 compared to that in 2015. Especially, the oyster reef area increased in 30-50 % sand content and decreased in 20-30 % and 60-70 %. The changes of sand sediment seem to affect the distribution of oyster reefs. This study may be useful for mapping the distribution of oyster reefs and understanding the spatial variation of their habitat.

Evapotranspiration Estimation with Small UAVs in Precision Agriculture.

Estimating evapotranspiration (ET) has been one of the most critical research areas in agriculture because of water scarcity, the growing population, and climate change. The accurate estimation and mapping of ET are necessary for crop water management. Traditionally, researchers use water balance, soil moisture, weighing lysimeters, or an energy balance approach, such as Bowen ratio or eddy covariance towers to estimate ET. However, these ET methods are point-specific or area-weighted measurements and cannot be extended to a large scale. With the advent of satellite technology, remote sensing images became able to provide spatially distributed measurements. However, the spatial resolution of multispectral satellite images is in the range of meters, tens of meters, or hundreds of meters, which is often not enough for crops with clumped canopy structures, such as trees and vines. Unmanned aerial vehicles (UAVs) can mitigate these spatial and temporal limitations. Lightweight cameras and sensors can be mounted on the UAVs and take high-resolution images. Unlike satellite imagery, the spatial resolution of the UAV images can be at the centimeter-level. UAVs can also fly on-demand, which provides high temporal imagery. In this study, the authors examined different UAV-based approaches of ET estimation at first. Models and algorithms, such as mapping evapotranspiration at high resolution with internalized calibration (METRIC), the two-source energy balance (TSEB) model, and machine learning (ML) are analyzed and discussed herein. Second, challenges and opportunities for UAVs in ET estimation are also discussed, such as uncooled thermal camera calibration, UAV image collection, and image processing. Then, the authors share views on ET estimation with UAVs for future research and draw conclusive remarks.

Slum Extraction from High Resolution Satellite Data using Mathematical Morphology based approach

ABSTRACT This paper proposes different mathematical morphology-based approaches to detect the urban slums from urban buildings using Very High Resolution (VHR) satellite images. Due to the rapid growth of urbanization activities, slum regions are the most evolving regions in South India whose locations are improper in official maps and statistics. Thus, automatic location identification of urban slums provides vital information for urban planners to formulate pro-poor policies and allocate resources properly. To detect the slums automatically from VHR satellite images, Multi Shape-Multi Size-Morphological Profile (MSh-MSi-MP) technique is proposed in this work. The development of this algorithm is motivated by modelling the urban slums using the conventional Morphological Profile (MP) which fails to model different structures in an image. On the other hand, the proposed MSh-MSi-MP is built by the sequence of morphological opening and closing profiles with different shapes and different sizes of structuring elements. Though MSh-MSi-MP yields high accuracy, some open areas in informal settlements and roofline structures of formal settlements are still classified as slums. To optimize these misclassification results, Morphological Spatial Pattern Analysis (MSPA) is used as a post-processing operation. The four different very high-resolution satellite images of Madurai city, South India acquired by WorldView-2 Sensor (1.84 m) proved the ability of the proposed approach to identify urban slums from other features by generating higher classification accuracy than any other methods.

Deep Learning Applications on Multitemporal SAR (Sentinel-1) Image Classification Using Confined Labeled Data: The Case of Detecting Rice Paddy in South Korea

The applicability of deep learning to remote sensing is rapidly increasing in accordance with the improvement in spatiotemporal resolution of satellite images. However, unlike satellite images acquired in near-real-time over wide areas, there are limited amount of labeled data used for model training. In this article, three kinds of deep learning applications—data augmentation, semisupervised classification, and domain-adapted architecture—were tested in an effort to overcome the limitation of insufficient labeled data. Among the diverse tasks that can be used for classification, rice paddy detection in South Korea was performed for its ability to fully utilize the advantages of deep learning and high spatiotemporal image resolution. In the process of designing each application, the domain knowledge of remote sensing and rice phenology was integrated. Then, all possible combinations of the three applications were examined and evaluated with pixel-based comparisons in various environments and city-level comparisons using national statistics. The results of this article indicated that all combinations of the applications can contribute to increase classification performance, even though the uncertainty involved in imitating or utilizing unlabeled data remains. As the effectiveness of the proposed applications was experimentally confirmed, enhancement in the applicability of deep learning was expected in various remote sensing areas. In particular, the proposed applications would be significant when they are applied to a wide range of study areas and high-resolution images, as they tend to require a large amount of learning data from diverse environments, owing to high intra-class heterogeneity.

Comparative analysis of classification algorithms for critical land prediction in agricultural cultivation areas

Currently, the identification of critical land, that has been physically, chemically, and biologically damaged, uses a geographic information system. However, it requires a high cost to get the high resolution of satellite images. In this study, a comparison framework is proposed to determine the performance of the classification algorithms, namely C.45, ID3, Random Forest, k-Nearest Neighbor, and Naïve Bayes. This research aims to find out the best algorithm for the classification of critical land in agricultural cultivation areas. The results show that the highest accuracy Random Forest algorithm was 93.10 % in predicting critical land, and the naïve Bayes has the lowest performance, with 89.32 % of accuracy in predicting critical land.

Impact of decomposition and kriging models on the solar irradiance downscaling accuracy in regions with complex topography

Abstract Many small island states are planning to invest heavily in solar photovoltaics in an attempt to curb their overreliance on fossil fuels for electricity generation. In order to efficiently exploit the abundant solar energy resource, these islands need reliable solar irradiance data. However, the orographic effects arising from their volcanic origins often result in strong variability and uncertainty in the solar resource. In this context, satellite-based models present an effective alternative to ground-based measurements. Different downscaling approaches have been applied that compensate for the large spatial resolution of satellite images and the terrain-related effects that they disregard. Nevertheless, the accuracy of these methods is influenced by the solar radiation decomposition model used. Moreover, the variogram model used in the kriging process to characterize the spatial dependence of the solar radiation has a significant effect on the results. In this study, we compare the performances of seven radiation decomposition models for the anisotropy analysis and seven variogram models for the spatial interpolation of the solar irradiance. A dense network of ground measurements at 43 stations is used to evaluate the accuracy of the different models. Results reveal that the Yao radiation model coupled with the Matern variogram provide the best results.

Temporal Changes Over Major Antarctic Ice Shelve Margins During 2001–2016

In spite of importance of the ice shelves as a probable sign of global climate change, temporal changes of Antarctic ice shelf margins are largely unknown. In the present article, an attempt has been made for chronological mapping of major four Antarctic ice shelf fronts for the period 2001–2016, using MODerate resolution Imaging Satellite images. Vector layers of individual ice shelves from 2001 to 2015 have been overlaid on the image of 2016. It is found that all the four ice shelves have advanced; the Filchner ice shelf advanced maximum (24 km) and the Ross ice shelf advanced minimum (16 km). Maximum range in yearly rate of advance is observed at Ronne Ice Shelf and the highest variability in rate of advance is observed over Filchner ice shelf. In terms of area change, maximum expansion occurred at Ronne ice shelf (8813 km2) and least expansion is observed over Amery ice shelf (2520 km2). Poor correlation between rates of change at different ice shelves suggests that different atmospheric and oceanic drivers are responsible for changes at individual ice shelves. Interestingly, the highest rate of advance as well as higher variability in rate of advance is observed over the latter half of the study period.

An Effective Method for Detecting Clouds in GaoFen-4 Images of Coastal Zones

Cloud-cover information is important for a wide range of scientific studies, such as the studies on water supply, climate change, earth energy budget, etc. In remote sensing, correct detection of clouds plays a crucial role in deriving the physical properties associated with clouds that exert a significant impact on the radiation budget of planet earth. Although the traditional cloud detection methods have generally performed well, these methods were usually developed specifically for particular sensors in a particular region with a particular underlying surface (e.g., land, water, vegetation, and man-made objects). Coastal regions are known to have a variety of underlying surfaces, which represent a major challenge in cloud detection. Therefore, there is an urgent requirement for developing a cloud detection method that could be applied to a variety of sensors, situations, and underlying surfaces. In the present study, a cloud detection method based on spatial and spectral uniformity of clouds was developed. In addition to having a spatially uniform texture, a spectrally approximate value was also present between the blue and green bands of the cloud region. The blue and green channel data appeared more uniform over the cloudy region, i.e., the entropy of the cloudy region was lower than that of the cloud-free region. On the basis of this difference in entropy, it would be possible to categorize the satellite images into cloud region images and cloud-free region images. Furthermore, the performance of the proposed method was validated by applying it to the data from various sensors across the coastal zone of the South China Sea. The experimental results demonstrated that compared to the existing operational algorithms, EN-clustering exhibited higher accuracy and scalability, and also performed robustly regardless of the spatial resolution of the different satellite images. It is concluded that the EN-clustering algorithm proposed in the present study is applicable to different sensors, different underlying surfaces, and different regions, with the support of NDSI and NDBI indices to remove the interference information from snow, ice, and man-made objects.

Ensemble Downscaling of Soil Moisture Data Using BMA and ATPRK

Soil moisture is essential information for meteorological and hydrological analyses. To date, many efforts have been made to achieve the two goals for soil moisture data, i.e., the improvement of accuracy and resolution, which is very challenging. We presented an ensemble downscaling method for quality improvement of gridded soil moisture data in terms of the accuracy and the spatial resolution by the integration of BMA (Bayesian model averaging) and ATPRK (area-to-point regression kriging). In the experiments, the BMA ensemble showed a 22% better accuracy than the data sets from ESA CCI (European Space Agency-Climate Change Initiative), ERA5 (ECMWF Reanalysis 5), and GLDAS (Global Land Data Assimilation System) in terms of RMSE (root mean square error). Also, the ATPRK downscaling could enhance the spatial resolution from 0.25o to 0.05o while preserving the improved accuracy and the spatial pattern of the BMA ensemble, without under- or over-estimation. The quality-improved data sets can contribute to a variety of local and regional applications related to soil moisture, such as agriculture, forest, hydrology, and meteorology. Because the ensemble downscaling method can be applied to the other land surface variables such as temperature, humidity, precipitation, and evapotranspiration, it can be a viable option to complement the accuracy and the spatial resolution of satellite images and numerical models.

Estimating Mo, Cu, Ni, Cd Contents in the Crop Leaves Growing on Small Land Plots Using Satellite Data

ABSTRACT The main goal of this research was to estimate heavy metals (HMs) (molybdenum (Mo), copper (Cu), nickel (Ni), cadmium (Cd)) contents in crop leaves through multispectral satellite imagery. During the acquisition of a SPOT 7 satellite image (28 July 2017) in situ sampling (38 samples) was done from the leaves of potatoes and beans growing close to the mining town of Kajaran (Armenia). To estimate HMs contents, multivariate regression (multiple linear regression (MLR), partial least squares regression (PLSR)), and artificial neural network (ANN) were used. As input data for the models raw, atmospherically corrected (Dark Object Subtraction (DOS)) and hyperspherical direction cosines (HSDC) normalized values of SPOT 7 spectral data in combination with one or combined log10, multiplicative scatter correction (MSC), standard normal variate transform (SNV) preprocessing methods were utilized. The best results were obtained for Cu using MLR (R2 cal. = 0.79, R2 CV = 0.70, RMSEcal. = 11.27, RMSECV = 13.47) and ANN (R2 Train ≈ 0.80, R2 Test ≈ 0.72, RMSETrain ≈ 11, RMSETest ≈ 13) models in case of bean leaves. The results are quite optimistic, however, further research with the use of high spatial/spectral resolution satellite images is needed to improve the accuracy of models.

Evaluating the effect of sediment color on water radiance and suspended sediment concentration using digital imagery

Satellite remote sensing has been extensively used for estimating the suspended sediment concentration (SSC) in coastal and inland waters. The application of this method to smaller waterbodies, however, encounters several limitations, such as the coarse temporal and spatial resolution of satellite images, cloud coverage intercession, and inaccessibility during emergency periods. Conventional digital cameras are used for monitoring the water quality parameters in inland waters, but their application for remote sensing of SSC in inland waters is still under development. The empirical models developed to estimate SSC from digital imagery are usually site-specific and do not consider the effect of the sediment properties such as grain size and shape, sediment color, and types of minerals. The performance of digital cameras for estimating SSC is assessed in this study, and the effect of sediment color on the accuracy of this method is investigated. A series of laboratory experiments were conducted using four different colors of sediment: light gray, dark gray, light brown, and dark brown. The results showed that the red waveband reflectance of digital imagery was more sensitive to the variation of SSC than that of blue and green waveband. The sediment color evidently affected the correlation between the measured SSC and waveband reflectance, even with the same particle size distribution. Sediment with darker color showed the lowest reflectance values, whereas lighter-color sediment has the highest reflectance values. The performance of the developed SSC-reflectance regression models for all sediment colors was assessed at two river sites, i.e., Village Creek, and the West Fork Trinity River. The correlation between measured and predicted SSC for Village Creek, that had the same sediment size and color as the light gray soil, was high (R2=0.98). The correlation was relatively low (R2=0.55) for the West Fork Trinity River and dark gray soil water samples which had a similar color and different sediment sizes. The difference in sediment sizes could explain why the SSC-reflectance model derived from the dark gray soil exhibited poor performance in predicting the SSC for this site. This study demonstrated that consideration of sediment color effect in developing a remote sensing algorithm from digital imagery would result in more accurate estimations of SSC in riverine environments.

Star sensor mounting for highly agile Earth imaging satellite

Star sensor (SS) is one of the most critical instrument on-board a spacecraft when it comes to attitude determination and control. Capability of providing precise attitude information makes it even more important for high resolution imaging satellite with stringent pointing requirements. At the same time, these instruments are highly sensitive to bright objects like Sun and Earth Albedo. In this paper, selection of star sensor mounting has been elaborately studied and presented for missions primarily performing highly agile Earth imaging. This paper addresses selection factors like Sun and Earth avoidance from bore-sight axis, orbit altitude and local time, angular separation between SS, sensitivity of SS on body axis and functional availability during imaging. Results are presented in the form of available mounting options and the way they satisfy geometrical constraints over a year.

Application of global satellite navigation system and telemetry image analysis technology to assess activity of potential large-scale landslide in forest land area – a case study on Taoyuan district no. D346, Kaohsiung city

The investigation, monitoring, and prevention of large-scale landslides, also known as deep seated landslides, are important research topics for slope disaster prevention. Some national forest land managed by the Forestry Bureau is located at the upstream watershed area, which has relatively unstable geological and topographical factors, and there are major large-scale landslides. However, those areas with protected objects are still the priority for investigation, governance, and management. There are 14 large-scale landslides with high and moderate risk located in the region of the Pingtung Forest District Management Office. For the implementation of future disaster prevention practices, the most important thing is to evaluate the activities of the large-scale landslides. This study takes Kaohsiung City-Taoyuan District-D346 as an example. The scars/linear structures in the potential area of a large-scale landslide are identified using high resolution LiDAR DEM. On-site global satellite navigation system monitoring and multi-sequence telemetry images are conducted to understand the activity and sliding direction of the block, meaning that the surface activity of the potential large-scale landslide area can be monitored. There are number of mature technological developments recently. Compared with the funding for underground geological exploration and subsequent project management, the funding for surface activity assessments with multiple telemetry images and monitoring equipment is relatively simple, after checking the activity of the large-scale landslide, the high resolution of LiDAR DEM, aerial photography and satellite images are the perfect tools to be applied before the follow-up key treatment and management funds are invested.

Spatial and Temporal Downscaling of TRMM Precipitation with Novel Algorithms

AbstractTropical Rainfall Measuring Mission (TRMM) satellite products constitute valuable precipitation datasets over regions with sparse rain gauge networks. Downscaling is an effective approach to estimating the precipitation over ungauged areas with high spatial resolution. However, a large bias and low resolution of original TRMM satellite images constitute constraints for practical hydrologic applications of TRMM precipitation products. This study contributes two precipitation downscaling algorithms by exploring the nonstationarity relations between precipitation and various environment factors [daytime surface temperature (LTD), terrain slope, normalized difference vegetation index (NDVI), altitude, longitude, and latitude] to overcome bias and low-resolution constraints of TRMM precipitation. Downscaling of precipitation is achieved with the geographically weighted regression model (GWR) and the backward-propagation artificial neural networks (BP_ANN). The probability density function (PDF) algorithm corrects the bias of satellite precipitation data with respect to spatial and temporal scales prior to downscaling. The principal component analysis algorithm (PCA) provides an alternative method of obtaining accurate monthly rainfall estimates during the wet rainfall season that minimizes the temporal uncertainties and upscaling effects introduced by direct accumulation (DA) of precipitation. The performances of the proposed downscaling algorithms are assessed by downscaling the latest version of TRMM3B42 V7 datasets within Hubei Province from 0.25° (about 25 km) to 1-km spatial resolution at the monthly scale. The downscaled datasets are systematically evaluated with in situ observations at 27 rain gauges from the years 2005 through 2010. This paper’s results demonstrate the bias correction is necessary before downscaling. The high-resolution precipitation datasets obtained with the proposed downscaling model with GWR relying on the NDVI and slope are shown to improve the accuracy of precipitation estimates. GWR exhibits more accurate downscaling results than BP_ANN coupled with the genetic algorithm (GA) in most dry and wet seasons.

Effect of spatial resolution of satellite images on estimating the greenness and evapotranspiration of urban green spaces

AbstractUrban green spaces (UGS), like most managed land covers, are getting progressively affected by water scarcity and drought. Preserving, restoring and expanding UGS require sustainable management of green and blue water resources to fulfil evapotranspiration (ET) demand for green plant cover. The heterogeneity of UGS with high variation in their microclimates and irrigation practices builds up the complexity of ET estimation. In oversized UGS, areas too large to be measured with in situ ET methods, remote sensing (RS) approaches of ET measurement have the potential to estimate the actual ET. Often in situ approaches are not feasible or too expensive. We studied the effects of spatial resolution using different satellite images, with high‐, medium‐ and coarse‐spatial resolutions, on the greenness and ET of UGS using Vegetation Indices (VIs) and VI‐based ET, over a 780‐ha urban park in Adelaide, Australia. We validated ET with the ground‐based ET method of Soil Water Balance. Three sets of imagery from WorldView2, Landsat and MODIS, and three VIs including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Enhanced Vegetation Index 2 (EVI2), were used to assess long‐term changes of VIs and ET calculated from the different imagery acquired for this study (2011–2018). We found high correspondence between ET‐MODIS and ET‐Landsat (R2 > 0.99 for all VIs). Landsat‐VIs captured the seasonal changes of greenness better than MODIS‐VIs. We used artificial neural network (ANN) to relate the RS‐ET and ground data, and ET‐MODIS (EVI2) showed the highest correlation (R2 = 0.95 and MSE =0.01 for validation). We found a strong relationship between RS‐ET and in situ measurements, even though it was not explicable by simple regressions; black box models helped us to explore their correlation. The methodology used in this research makes a strong case for the value of remote sensing in estimating and managing ET of green spaces in water‐limited cities.

Review: Forensic Architecture: Violence at the Threshold of Detectability, by Eyal Weizman

Review: <i>Forensic Architecture: Violence at the Threshold of Detectability</i>, by Eyal Weizman

Automatic Gully Detection: Neural Networks and Computer Vision

Transition from manual (visual) interpretation to fully automated gully detection is an important task for quantitative assessment of modern gully erosion, especially when it comes to large mapping areas. Existing approaches to semi-automated gully detection are based on either object-oriented selection based on multispectral images or gully selection based on a probabilistic model obtained using digital elevation models (DEMs). These approaches cannot be used for the assessment of gully erosion on the territory of the European part of Russia most affected by gully erosion due to the lack of national large-scale DEM and limited resolution of open source multispectral satellite images. An approach based on the use of convolutional neural networks for automated gully detection on the RGB-synthesis of ultra-high resolution satellite images publicly available for the test region of the east of the Russian Plain with intensive basin erosion has been proposed and developed. The Keras library and U-Net architecture of convolutional neural networks were used for training. Preliminary results of application of the trained gully erosion convolutional neural network (GECNN) allow asserting that the algorithm performs well in detecting active gullies, well differentiates gullies from other linear forms of slope erosion — rills and balkas, but so far has errors in detecting complex gully systems. Also, GECNN does not identify a gully in 10% of cases and in another 10% of cases it identifies not a gully. To solve these problems, it is necessary to additionally train the neural network on the enlarged training data set.

Transferable instance segmentation of dwellings in a refugee camp - integrating CNN and OBIA

ABSTRACT The availability and usage of optical very high spatial resolution (VHR) satellite images for efficient support of refugee/IDP (internally displaced people) camp planning and humanitarian aid are growing. In this research, an integrated approach was used for dwelling classification from VHR satellite images, which applied the preliminary results of a convolutional neural network (CNN) model as input data for an object-based image analysis (OBIA) knowledge-based semantic classification method. Unlike standard pixel-based classification methods that usually are applied for the CNN model, our integrated approach aggregates CNN results on separately delineated objects as the basic units of a rule-based classification, to include additional prior-knowledge and spatial concepts in the final instance segmentation. An object-based accuracy assessment methodology was used to assess the accuracy of the classified dwelling categories on a single object-level. Our findings reveal accuracies of more than 90% for each applied parameter of precision, recall and F1-score. We conclude that integrating the CNN models with the OBIA capabilities can be considered an efficient approach for dwelling extraction and classification, integrating not only sample derived knowledge but also prior-knowledge about refugee/IDP camp situations, like dwellings size constraints and additional context.

Self-adapting extraction of cropland phenological transitions of rotation agroecosystems using dynamically fused NDVI images.

Remote sensing can be used to monitor cropland phenological characteristics; however, tradeoffs between the spatial and temporal resolutions of cloudless satellite images limit the accuracy of their retrieval. In this study, an improved enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was applied to human-dominated Xiong'an New Area to develop a self-adapting algorithm automating the extraction of main phenological transition points (greenup, maturity, senescence, and dormancy). The analyses of cropland phenological characteristics were performed utilizing the Softmax classification method. By examining three different phases of fusion images, it was found that the improved ESTARFM was more accurate than the original ESTARFM (correlation coefficient > 0.76; relative root mean square error < 0.25; structural similarity index > 0.79). The reconstructed normalized difference vegetation indexes were consistent with that acquired by the Moderate Resolution Imaging Spectroradiometer (average discrepancy: 0.1136, median absolute deviation: 0.0110). The greenup, maturity, senescence, and dormancy points were monitored in 5-day resolution and 50-day length on a 30-m grid scale, and their average day of year (DOY) were 67, 119, 127, and 166 for wheat; 173, 224, 232, and 283 for single-season corn; and 189, 227, 232, and 285 for rotation corn, respectively. The corresponding median absolute deviations were 2, 3, 2, and 2days for wheat; 2, 5, 3, and 4days for single-season corn; and 2, 5, 2, and 2days for rotation corn, respectively, while all coefficients of variation did not exceed 6%. The proposed self-adapting approach can be used for identifying the planting modes at grid level in rotation agroecosystems and cropland phenological dynamics on a global or regional scale.

Finding Coastal Megaclast Deposits: A Virtual Perspective

Coastal megaclast deposits are dominated by detrital particles larger than 1 m in size. These attract significant attention of modern researchers because of the needs of sedimentary rock nomenclature development and interpretation of storm and tsunami signatures on seashores. If so, finding localities that exhibit coastal megaclast deposits is an important task. Field studies do not offer a quick solution, and, thus, remote sensing tools have to be addressed. The application of the Google Earth Engine has permitted to find four new localities, namely Hondarribia in northern Spain (Biscay Bay), the Ponza Island in Italy (Tyrrhenian Sea), the Wetar Island in eastern Indonesia (Banda Sea), and the Humboldt o Coredo Bay at the Colombia/Panama border (eastern Pacific). In these localities, coastal megaclast deposits consisting of blocks (1–10 m in size) and some megablocks (&gt;10 m in size) are delineated and preliminary described in regard to the dominant size of particles, package density, mode of occurrence, etc. The limitations of such virtual surveys of coastal megaclast deposits are linked to an insufficiently high resolution of satellite images, as well as ‘masking’ effects of vegetation cover and cliff shadows. However, these limitations do not diminish the importance of the Google Earth Engine for finding these deposits. Consideration of some tourism-related information, including photos captured by tourists and bouldering catalogues, facilitates search for promising areas for subsequent virtual surveying of megaclast distribution. It is also established that the Google Earth Engine permits quantitative analysis of composition of coastal megaclast deposits in some areas, as well as to register decade-long dynamics or stability of these deposits, which is important to interpret their origin. The current opportunities for automatic detection of coastal megaclast deposits seem to be restricted.