A first data-driven gully head density map of the world

Gully erosion has been recognized as a main driver of soil erosion and land degradation. While numerous studies have focussed on understanding gully erosion at local scales, we have very little insights into the patterns and controlling factors of gully erosion at a global scale. Overall, this process remains notoriously difficult to simulate and predict. A main reason for this is that the complex and threshold-dependent nature of gully formation leads to very high data requirements when aiming to simulate this process over larger areas.Here we help bridging this gap by presenting the first data-driven analysis of gully head densities at a global scale.&#160; We developed a grid-based scoring method that allows to quickly assess the range of gully head densities in a given area based on Google Earth imagery. Using this approach, we constructed a global database of mapped gully head densities for currently >7400 sites worldwide. Based on this dataset and globally available data layers on relevant environmental factors (topography, soil characteristics, land use) we explored which factors are dominant in explaining global patterns of gully head densities and propose a first global gully head density map.Our results indicate that there are ca. 1.7 to 2 billion gully heads worldwide. This estimate might underestimate the actual numbers of gully heads since ephemeral gullies (in cropland) and gullies under forest remain difficult to map. Our database and analyses further reveal clear regional patterns in the presence of gullies. Around 27% of the terrestrial surface (excluding Antarctica and Greenland) has a density of > 1 gully head/km&#178;, while an estimated 14% has a density of > 10 gully heads/km&#178; and 4% has even a density of > 100 gully heads/km&#178;. Major hotspots (with > 50 gully heads/km&#178;) include the Chinese loess plateau, but also Iran, large parts of the Sahara Desert, the Andes and Madagascar. In addition, gully erosion also frequently occurs (with typical densities of 1-50 gully heads/km&#178;) in the Mid-West USA, the African Rift, SE-Brazil, India, New-Zealand and Australia.These regional patterns are mainly explained by topography and climate in interaction with vegetation cover. Overall, the highest gully densities occur in regions with some topography and a (semi-)arid climate. Nonetheless, it is important to point out that not all gully heads are still actively retreating. Building on earlier insights into the magnitude and controlling factors of gully head retreat rates, we explore what our current results imply for assessing actual gully erosion rates at a global scale.

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Spatial modelling of gully initiation in the Abaya-Chamo lakes catchments, southern Ethiopia

10.5194/egusphere-egu21-13060 ◽

2021 ◽

Author(s):

Liuelsegad Belayneh ◽

Olivier Dewitte ◽

Guchie Gulie ◽

Jean Poesen ◽

Matthieu Kervyn

Keyword(s):

Water Conservation ◽

Google Earth ◽

Controlling Factors ◽

Gully Erosion ◽

Annual Rainfall ◽

Validation Dataset ◽

Southern Ethiopia ◽

Good Prediction ◽

Eastern Ethiopia ◽

Initiation Point

Lake Abaya and Lake Chamo are located within the rift valley that cuts across eastern Ethiopia. Severe soil erosion, predominantly gully erosion in the midlands and highlands, and flash flooding along rivers in the lowlands resulted in sediment and nutrient accumulation in the rift lakes. In this study, conducted in four river catchments on the Western border of the Abaya-Chamo rift, an inventory of gully channels is made and factors controlling the location of gullies are analysed. The inventory, which was prepared using Google Earth imagery and field surveys, consists of 7336 gullies over a study area of 1050 km&#178;, resulting in a high average gully density (1.56 km.km-&#178;) with specifically high densities (3.74 km.km-&#178;) &#160;in the Northern Shaf&#233; river catchment. Of all mapped gullies, 56% show signs of active erosion (i.e. mostly bare gully walls and bed, and/or fresh sediments deposited in the lower parts of the gully). In order to reduce the effects of gully erosion, it is vital to understand the factors controlling gully initiation and locations most susceptible to develop new gullies. Instead of using gully head, which due to head cut retreat might not be representative of the characteristics of the gully initiation point, a slope-area threshold (SA) is used to identify the most probable gully initiation point along existing gullies. The spatial susceptibility of these sites to gully initiation is then modelled using the frequency ratio and logistic regression methods using a set of 15 geo-environmental variables related to topography, soil texture, geology, rivers, knickpoints and land cover, as potential controlling factors. Active and inactive gullies are modelled separately. Slope, type of lithology, location of knickpoint rejuvenating the landscape through channel incision, distance from roads and mean annual rainfall are identified as very important controlling factors of gully initiation sites. The most susceptible gully erosion areas are observed in the steep midland, where limited population is living, and bare land and rangeland is dominant. The results show that the models are reliable and have a good prediction performance of gully initiation when using an independent validation dataset. The produced gully susceptibility maps highlight locations where soil and water conservation or other sustainable planning actions are required. Such maps are also needed to estimate the long-term contribution of gullies to the sediments delivered to the Abaya-Chamo Lakes.

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The climatic, topographic and litho-tectonic characteristics of badlands in Turkey

10.5194/egusphere-egu21-7788 ◽

2021 ◽

Author(s):

Aydogan Avcioglu ◽

Tolga Gorum ◽

Abdullah Akbas ◽

Mariano Moreno de las Heras ◽

Omer Yetemen

Keyword(s):

Critical Role ◽

Sedimentary Rocks ◽

Spatial Coherence ◽

Google Earth ◽

Controlling Factors ◽

Mean Annual Precipitation ◽

Rainfall Erosivity ◽

Data Set ◽

Anatolian Plateau ◽

Precipitation Seasonality

Badland areas are present in all continents, excluding Antarctica, and play a critical role in establishing local erosion and sedimentation rates. The presence of unconsolidated rocks (e.g., marls, sandstone, mudstone etc.) is a major driver controlling the distribution of badlands, which together with other environmental components, such as climate, tectonics, vegetation, and topography, determine their forms and processes. The mutual interaction of controlling factors in badlands areas provides a basis for developing a holistic approach to clarify their distribution patterns. Turkey's geodynamic evolution has led to the emergence of marine sedimentary rocks, pyroclastics, and continental clastics, especially in line with the uplift of the Anatolian Plateau and volcanism during the last 8 Ma.This study aims to explore the country-scale distribution of badlands and the controlling factors of this badland distribution in Turkey. Remarkably wide badlands landscapes (4494 km2) have been visually inspected using Google Earth ProTM to further digitize and extract geomorphological units by applying high-resolution multispectral images provided by WorldView-4/Maxar Technology and CNES/Airbus. To obtain exact boundaries, we eliminated contiguous flat areas surrounding the identified badlands by using red relief image map (RRIM) mosaics that express surface concavity and convexity combined with topographic slope derived from a digital elevation model of 5-m spatial resolution. Last, to determine the controlling factors of badlands distribution, we have compiled a global data set comprising 1-km resolution layers of mean annual precipitation, temperature and precipitation seasonality, aridity, NDVI, rainfall erosivity factor, elevation, and majority values of regional lithology in sub-catchments units. The enhanced investigation of the complex relationship that expresses the controlling factors of badlands distribution, has been conducted by K-means unsupervised cluster analysis.Our comprehensive regional analyses exploring the distribution and environmental attributes of major Turkish badlands identified five different groups or clusters of badlands that display spatial coherence with climatic and tectonic settings. We argue that Turkey's climatic and topographic transition zones, varying from Mediterranean climate dominated areas to the more arid Central Anatolian Plateau, and tectonically&#8209;induced topographic barriers play a relevant role in discriminating these groups of badlands. Moreover, the Anatolian diversity of sedimentary rocks, which consists of Neogene and Paleogene continental clastics, volcano clastics & pyroclastics, and lacustrine deposits, makes an essential contribution to the identified, extensive badland distribution.This study has been produced benefiting from the 2232 International Fellowship for Outstanding Researchers Program of the Scientific and Technological Research Council of Turkey (TUBITAK) through grant 118C329. The financial support received from TUBITAK does not mean that the content of the publication is approved in a scientific sense by TUBITAK.

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Understanding Skout users’ mobility patterns on a global scale: a data-driven study

World Wide Web ◽

10.1007/s11280-018-0551-8 ◽

2018 ◽

Vol 22 (6) ◽

pp. 2655-2673 ◽

Cited By ~ 1

Author(s):

Rong Xie ◽

Yang Chen ◽

Shihan Lin ◽

Tianyong Zhang ◽

Yu Xiao ◽

...

Keyword(s):

Global Scale ◽

Data Driven ◽

Mobility Patterns

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FREE GLOBAL DSM ASSESSMENT ON LARGE SCALE AREAS EXPLOITING THE POTENTIALITIES OF THE INNOVATIVE GOOGLE EARTH ENGINE PLATFORM

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

10.5194/isprs-archives-xlii-1-w1-627-2017 ◽

2017 ◽

Vol XLII-1/W1 ◽

pp. 627-633 ◽

Cited By ~ 5

Author(s):

A. Nascetti ◽

M. Di Rita ◽

R. Ravanelli ◽

M. Amicuzi ◽

S. Esposito ◽

...

Keyword(s):

High Performance ◽

Large Scale ◽

Global Scale ◽

Google Earth ◽

Observation Data ◽

Geometric Accuracy ◽

Computing Platform ◽

Aster Gdem ◽

Google Earth Engine ◽

Earth Observation Data

The high-performance cloud-computing platform Google Earth Engine has been developed for global-scale analysis based on the Earth observation data. In particular, in this work, the geometric accuracy of the two most used nearly-global free DSMs (SRTM and ASTER) has been evaluated on the territories of four American States (Colorado, Michigan, Nevada, Utah) and one Italian Region (Trentino Alto- Adige, Northern Italy) exploiting the potentiality of this platform. These are large areas characterized by different terrain morphology, land covers and slopes. The assessment has been performed using two different reference DSMs: the USGS National Elevation Dataset (NED) and a LiDAR acquisition. The DSMs accuracy has been evaluated through computation of standard statistic parameters, both at global scale (considering the whole State/Region) and in function of the terrain morphology using several slope classes. The geometric accuracy in terms of Standard deviation and NMAD, for SRTM range from 2-3 meters in the first slope class to about 45 meters in the last one, whereas for ASTER, the values range from 5-6 to 30 meters. In general, the performed analysis shows a better accuracy for the SRTM in the flat areas whereas the ASTER GDEM is more reliable in the steep areas, where the slopes increase. These preliminary results highlight the GEE potentialities to perform DSM assessment on a global scale.

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Collecting training data to map forest management at global scale

10.5194/egusphere-egu21-15297 ◽

2021 ◽

Author(s):

Myroslava Lesiv ◽

Dmitry Schepaschenko ◽

Martina Dürauer ◽

Marcel Buchhorn ◽

Ivelina Georgieva ◽

...

Keyword(s):

Forest Management ◽

High Resolution ◽

Remotely Sensed ◽

Global Scale ◽

Google Earth ◽

Training Data ◽

Tree Cover ◽

Data Set ◽

The World ◽

Very High

Spatially explicit information on forest management at a global scale is critical for understanding the current status of forests for sustainable forest management and restoration. Whereas remotely sensed based datasets, developed by applying ML and AI algorithms, can successfully depict tree cover and other land cover types, it has not yet been used to depict untouched forest and different degrees of forest management. We show for the first time that with sufficient training data derived from very high-resolution imagery a differentiation within the tree cover class of various levels of forest management is possible.In this session, we would like to present our approach for labeling forest related training data by using Geo-Wiki application (https://www.geo-wiki.org/). Moreover, we would like to share a new open global training data set on forest management we collected from a series of Geo-Wiki campaigns. In February 2019, we organized an expert workshop to (1) discuss the variety of forest management practices that take place in different parts of the world; (2) generalize the definitions for the application at global scale; (3) finalize the Geo-Wiki interface for the crowdsourcing campaigns; and (4) build a data set of control points (or the expert data set), which we used later to monitor the quality of the crowdsourced contributions by the volunteers. We involved forest experts from different regions around the world to explore what types of forest management information could be collected from visual interpretation of very high-resolution images from Google Maps and Microsoft Bing, in combination with Sentinel time series and Normalized Difference Vegetation Index (NDVI) profiles derived from Google Earth Engine (GEE). Based on the results of this analysis, we expanded these campaigns by involving a broader group of participants, mainly people recruited from remote sensing, geography and forest research institutes and universities.In total, we collected forest data for approximately 230 000 locations globally. These data are of sufficient density and quality and therefore could be used in many ML and AI applications for forests at regional and local scale.&#160; We also provide an example of ML application, a remotely sensed based global forest management map at a 100 m resolution (PROBA-V) for the year 2015. It includes such classes as intact forests, forests with signs of human impact, including clear cuts and logging, replanted forest, woody plantations with a rotation period up to 15 years, oil palms and agroforestry. The results of independent statistical validation show that the map&#8217;s overall accuracy is 81%.

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Object-Based Mapping of Gullies Using Optical Images: A Case Study in the Black Soil Region, Northeast of China

Remote Sensing ◽

10.3390/rs12030487 ◽

2020 ◽

Vol 12 (3) ◽

pp. 487 ◽

Cited By ~ 3

Author(s):

Biwei Wang ◽

Zengxiang Zhang ◽

Xiao Wang ◽

Xiaoli Zhao ◽

Ling Yi ◽

...

Keyword(s):

Regional Scale ◽

Black Soil ◽

Google Earth ◽

Gully Erosion ◽

Image Feature ◽

Gradient Boosting ◽

Light Gradient ◽

Optical Images ◽

Black Soil Region ◽

Object Based

Gully erosion is a widespread natural hazard. Gully mapping is critical to erosion monitoring and the control of degraded areas. The analysis of high-resolution remote sensing images (HRI) and terrain data mixed with developed object-based methods and field verification has been certified as a good solution for automatic gully mapping. Considering the availability of data, we used only open-source optical images (Google Earth images) to identify gully erosion through image feature modeling based on OBIA (Object-Based Image Analysis) in this paper. A two-end extrusion method using the optimal machine learning algorithm (Light Gradient Boosting Machine (LightGBM)) and eCognition software was applied for the automatic extraction of gullies at a regional scale in the black soil region of Northeast China. Due to the characteristics of optical images and the design of the method, unmanaged gullies and gullies harnessed in non-forest areas were the objects of extraction. Moderate success was achieved in the absence of terrain data. According to independent validation, the true overestimation ranged from 20% to 30% and was mainly caused by land use types with high erosion risks, such as bare land and farm lanes being falsely classified as gullies. An underestimation of less than 40% was adjacent to the correctly extracted gullied areas. The results of extraction in regions with geographical object categories of a low complexity were usually more satisfactory. The overall performance demonstrates that the present method is feasible for gully mapping at a regional scale, with high automation, low cost, and acceptable accuracy.

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SnowCloudMetrics: Snow Information for Everyone

Remote Sensing ◽

10.3390/rs12203341 ◽

2020 ◽

Vol 12 (20) ◽

pp. 3341

Author(s):

Ryan L. Crumley ◽

Ross T. Palomaki ◽

Anne W. Nolin ◽

Eric A. Sproles ◽

Eugene J. Mar

Keyword(s):

Snow Cover ◽

Data Storage ◽

Image Data ◽

Global Scale ◽

Google Earth ◽

Mountain Environments ◽

Resource Managers ◽

Moderate Resolution ◽

Snow Telemetry ◽

Moderate Resolution Imaging Spectroradiometer

Snow is a critical component of the climate system, provides fresh water for millions of people globally, and affects forest and wildlife ecology. Snowy regions are typically data sparse, especially in mountain environments. Remotely-sensed snow cover data are available globally but are challenging to convert into accessible, actionable information. SnowCloudMetrics is a web portal for on-demand production and delivery of snow information including snow cover frequency (SCF) and snow disappearance date (SDD) using Google Earth Engine (GEE). SCF and SDD are computed using the Moderate Resolution Imaging Spectroradiometer (MODIS) Snow Cover Binary 500 m (MOD10A1) product. The SCF and SDD metrics are assessed using 18 years of Snow Telemetry records at more than 750 stations across the Western U.S. SnowCloudMetrics provides users with the capacity to quickly and efficiently generate local-to-global scale snow information. It requires no user-side data storage or computing capacity, and needs little in the way of remote sensing expertise. SnowCloudMetrics allows users to subset by year, watershed, elevation range, political boundary, or user-defined region. Users can explore the snow information via a GEE map interface and, if desired, download scripts for access to tabular and image data in non-proprietary formats for additional analyses. We present global and hemispheric scale examples of SCF and SDD. We also provide a watershed example in the transboundary, snow-dominated Amu Darya Basin. Our approach represents a new, user-driven paradigm for access to snow information. SnowCloudMetrics benefits snow scientists, water resource managers, climate scientists, and snow related industries providing SCF and SDD information tailored to their needs, especially in data sparse regions.

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Optimized Segmentation Based on the Weighted Aggregation Method for Loess Bank Gully Mapping

Remote Sensing ◽

10.3390/rs12050793 ◽

2020 ◽

Vol 12 (5) ◽

pp. 793 ◽

Cited By ~ 2

Author(s):

Hu Ding ◽

Kai Liu ◽

Xiaozheng Chen ◽

Liyang Xiong ◽

Guoan Tang ◽

...

Keyword(s):

Loess Plateau ◽

Gully Erosion ◽

Fine Tuning ◽

Chinese Loess Plateau ◽

Gradient Boosting ◽

Catchment Scale ◽

Chinese Loess ◽

Extreme Gradient Boosting ◽

Elevation Model ◽

The Chinese Loess Plateau

The Chinese Loess Plateau suffers severe gully erosion. Gully mapping is a fundamental task for gully erosion monitoring in this region. Among the different gully types in the Loess Plateau, the bank gully is usually regarded as the most important source for the generation of sediment. However, approaches for bank gully extraction are still limited. This study put forward an integrated framework, including segmentation optimization, evaluation and Extreme Gradient Boosting (XGBoost)-based classification, for the bank gully mapping of Zhifanggou catchment in the Chinese Loess Plateau. The approach was conducted using a 1-m resolution digital elevation model (DEM), based on unmanned aerial vehicle (UAV) photogrammetry and WorldView-3 imagery. The methodology first divided the study area into different watersheds. Then, segmentation by weighted aggregation (SWA) was implemented to generate multi-level segments. For achieving an optimum segmentation, area-weighted variance (WV) and Moran’s I (MI) were adopted and calculated within each sub-watershed. After that, a new discrepancy metric, the area-number index (ANI), was developed for evaluating the segmentation results, and the results were compared with the multi-resolution segmentation (MRS) algorithm. Finally, bank gully mappings were obtained based on the XGBoost model after fine-tuning. The experiment results demonstrate that the proposed method can achieve superior segmentation compared to MRS. Moreover, the overall accuracy of the bank gully extraction results was 78.57%. The proposed approach provides a credible tool for mapping bank gullies, which could be useful for the catchment-scale gully erosion process.

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