scholarly journals Deriving Planform Morphology and Vegetation Coverage From Remote Sensing to Support River Management Applications

2021 ◽  
Vol 9 ◽  
Author(s):  
Richard J. Boothroyd ◽  
Michael Nones ◽  
Massimo Guerrero
Keyword(s):  
Satellite Imagery ◽  
Flood Risk ◽  
Vegetation Dynamics ◽  
River Management ◽  
Remotely Sensed ◽  
Google Earth ◽  
Geographical Information ◽  
Vegetation Coverage ◽  
Spatiotemporal Resolution ◽  
River Planform

With the increasing availability of big geospatial data (e.g., multi-spectral satellite imagery) and access to platforms that support multi-temporal analyses (e.g., cloud-based computing, Geographical Information Systems, GIS), the use of remotely sensed information for monitoring riverine hydro-morpho-biodynamics is growing. Opportunities to map, quantify and detect changes in the wider riverscape (i.e., water, sediment and vegetation) at an unprecedented spatiotemporal resolution can support flood risk and river management applications. Focusing on a reach of the Po River (Italy), satellite imagery from Landsat 5, 7, and 8 for the period 1988–2018 were analyzed in Google Earth Engine (GEE) to investigate changes in river planform morphology and vegetation dynamics associated with transient hydrology. An improved understanding of these correlations can help in managing sediment transport and riparian vegetation to reduce flood risk, where biogeomorphic processes are commonly overlooked in flood risk mapping. In the study, two established indices were analyzed: the Modified Normalized Difference Water Index (MNDWI) for monitoring changes in the wetted river planform morphology, inferring information about sediment dynamics, and the Normalized Difference Vegetation Index (NDVI) for evaluating changes in vegetation coverage. Results suggest that planform changes are highly localized with most parts of the reach remaining stable. Using the wetted channel occurrence as a measure of planform stability, almost two-thirds of the wetted channel extent (total area = 86.4 km2) had an occurrence frequency >90% (indicating stability). A loss of planform complexity coincided with the position of former secondary channels, or zones where the active river channel had narrowed. Time series analysis of vegetation dynamics showed that NDVI maxima were recorded in May/June and coincided with the first peak in the hydrological regime (occurring in late spring and associated with snowmelt). Seasonal variation in vegetation coverage is potentially important for local hydrodynamics, influencing flood risk. We suggest that remotely sensed information can provide river scientists with new insights to support the management of highly anthropized watercourses.

scholarly journals Eleven Years of Mangrove–Mudflat Dynamics on the Mud Volcano-Induced Prograding Delta in East Java, Indonesia: Integrating UAV and Satellite Imagery

2021 ◽  
Vol 13 (6) ◽  
pp. 1084
Author(s):  
Sebrian Mirdeklis Beselly ◽  
Mick van der Wegen ◽  
Uwe Grueters ◽  
Johan Reyns ◽  
Jasper Dijkstra ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Vegetation Indices ◽  
Google Earth ◽  
Biophysical Properties ◽  
Spatiotemporal Resolution ◽  
Essential Value ◽  
A Value ◽  
Novel Approach ◽  
Aerial Vehicle ◽  
Mangrove Dynamics

This article presents a novel approach to explore mangrove dynamics on a prograding delta by integrating unmanned aerial vehicle (UAV) and satellite imagery. The Porong Delta in Indonesia has a unique geographical setting with rapid delta development and expansion of the mangrove belt. This is due to an unprecedented mud load from the LUSI mud volcanic eruption. The mangrove dynamics analysis combines UAV-based Structure from Motion (SfM) photogrammetry and 11 years (2009–2019) satellite imagery cloud computing analysis by Google Earth Engine (GEE). Our analysis shows unique, high-spatiotemporal-resolution mangrove extent maps. The SfM photogrammetry analysis leads to a 3D representation of the mangrove canopy and an estimate of mangrove biophysical properties with accurate height and individual position of the mangroves stand. GEE derived vegetation indices resulted in high (three-monthly) resolution mangrove coverage dynamics over 11 years (2009–2019), yielding a value of more than 98% for the overall, producer and consumer accuracy. Combining the satellite-derived age maps and the UAV-derived spatial tree structure allowed us to monitor the mangrove dynamics on a rapidly prograding delta along with its structural attributes. This analysis is of essential value to ecologists, coastal managers, and policymakers.

scholarly journals Flood Risk Mapping by Remote Sensing Data and Random Forest Technique

Water ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3115
Author(s):  
Hadi Farhadi ◽  
Mohammad Najafzadeh
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Flood Risk ◽  
Google Earth ◽  
Risk Mapping ◽  
Geographical Information ◽  
Slope Aspect ◽  
Landsat 8 ◽  
Risk Indices ◽  
Flood Risk Mapping

Detecting effective parameters in flood occurrence is one of the most important issues that has drawn more attention in recent years. Remote Sensing (RS) and Geographical Information System (GIS) are two efficient ways to spatially predict Flood Risk Mapping (FRM). In this study, a web-based platform called the Google Earth Engine (GEE) (Google Company, Mountain View, CA, USA) was used to obtain flood risk indices for the Galikesh River basin, Northern Iran. With the aid of Landsat 8 satellite imagery and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), 11 risk indices (Elevation (El), Slope (Sl), Slope Aspect (SA), Land Use (LU), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Topographic Wetness Index (TWI), River Distance (RD), Waterway and River Density (WRD), Soil Texture (ST]), and Maximum One-Day Precipitation (M1DP)) were provided. In the next step, all of these indices were imported into ArcMap 10.8 (Esri, West Redlands, CA, USA) software for index normalization and to better visualize the graphical output. Afterward, an intelligent learning machine (Random Forest (RF)), which is a robust data mining technique, was used to compute the importance degree of each index and to obtain the flood hazard map. According to the results, the indices of WRD, RD, M1DP, and El accounted for about 68.27 percent of the total flood risk. Among these indices, the WRD index containing about 23.8 percent of the total risk has the greatest impact on floods. According to FRM mapping, about 21 and 18 percent of the total areas stood at the higher and highest risk areas, respectively.

scholarly journals LARGE SCALE CITY MAPPING USING SATELLITE IMAGERY / KOSMINIŲ NUOTRAUKŲ IŠ GOOGLE EARTH, TAIKOMŲ MIESTAMS KARTOGRAFUOTI STAMBIUOJU MASTELIU, REKTIFIKAVIMAS / РЕКТИФИКАЦИЯ КОСМИЧЕСКИХ СНИМКОВ ИЗ GOOGLE EARTH ДЛЯ КРУПНОМАСШТАБНОГО КАРТОГРАФИРОВАНИЯ ГОРОДОВ

2012 ◽  
Vol 37 (4) ◽  
pp. 168-171 ◽  
Author(s):  
Birutė Ruzgienė ◽  
Qian Yi Xiang ◽  
Silvija Gečytė
Keyword(s):  
Satellite Imagery ◽  
Large Scale ◽  
Modern Technology ◽  
Google Earth ◽  
Aerial Images ◽  
Control Points ◽  
Image Rectification ◽  
Image Deformation ◽  
Map Construction ◽  
Short Period

The rectification of high resolution digital aerial images or satellite imagery employed for large scale city mapping is modern technology that needs well distributed and accurately defined control points. Digital satellite imagery, obtained using widely known software Google Earth, can be applied for accurate city map construction. The method of five control points is suggested for imagery rectification introducing the algorithm offered by Prof. Ruan Wei (tong ji University, Shanghai). Image rectification software created on the basis of the above suggested algorithm can correct image deformation with required accuracy, is reliable and keeps advantages in flexibility. Experimental research on testing the applied technology has been executed using GeoEye imagery with Google Earth builder over the city of Vilnius. Orthophoto maps at the scales of 1:1000 and 1:500 are generated referring to the methodology of five control points. Reference data and rectification results are checked comparing with those received from processing digital aerial images using a digital photogrammetry approach. The image rectification process applying the investigated method takes a short period of time (about 4-5 minutes) and uses only five control points. The accuracy of the created models satisfies requirements for large scale mapping. Santrauka Didelės skiriamosios gebos skaitmeninių nuotraukų ir kosminių nuotraukų rektifikavimas miestams kartografuoti stambiuoju masteliu yra nauja technologija. Tai atliekant būtini tikslūs ir aiškiai matomi kontroliniai taškai. Skaitmeninės kosminės nuotraukos, gautos taikant plačiai žinomą programinį paketą Google Earth, gali būti naudojamos miestams kartografuoti dideliu tikslumu. Siūloma nuotraukas rektifikuoti Penkių kontrolinių taskų metodu pagal prof. Ruan Wei (Tong Ji universitetas, Šanchajus) algoritmą. Moksliniam eksperimentui pasirinkta Vilniaus GeoEye nuotrauka iš Google Earth. 1:1000 ir 1:500 mastelio ortofotografiniai žemėlapiai sudaromi Penkių kontrolinių taškų metodu. Rektifikavimo duomenys lyginami su skaitmeninių nuotraukų apdorojimo rezultatais, gautais skaitmeninės fotogrametrijos metodu. Nuotraukų rektifikavimas Penkių kontrolinių taskų metodu atitinka kartografavimo stambiuoju masteliu reikalavimus, sumažėja laiko sąnaudos. Резюме Ректификация цифровых и космических снимков высокой резолюции для крупномасштабного картографирования является новой технологией, требующей точных и четких контрольных точек. Цифровые космические снимки, полученные с использованием широкоизвестного программного пакета Google Earth, могут применяться для точного картографирования городов. Для ректификации снимков предложен метод пяти контрольных точек с применением алгоритма проф. Ruan Wei (Университет Tong Ji, Шанхай). Для научного эксперимента использован снимок города Вильнюса GeoEye из Google Earth. Ортофотографические карты в масштабе 1:1000 и 1:500 генерируются с применением метода пяти контрольных точек. Полученные результаты и данные ректификации сравниваются с результатами цифровых снимков, полученных с применением метода цифровой фотограмметрии. Ректификация снимков с применением метода пяти контрольных точек уменьшает временные расходы и удовлетворяет требования, предъявляемые к крупномасштабному картографированию.

scholarly journals Characterizing the Spatial and Temporal Availability of Very High Resolution Satellite Imagery in Google Earth and Microsoft Bing Maps as a Source of Reference Data

Land ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 118 ◽  
Author(s):  
Myroslava Lesiv ◽  
Linda See ◽  
Juan Laso Bayas ◽  
Tobias Sturn ◽  
Dmitry Schepaschenko ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Reference Data ◽  
Temporal Distribution ◽  
Google Earth ◽  
Training Data ◽  
Visual Interpretation ◽  
The Usa ◽  
Very High

Very high resolution (VHR) satellite imagery from Google Earth and Microsoft Bing Maps is increasingly being used in a variety of applications from computer sciences to arts and humanities. In the field of remote sensing, one use of this imagery is to create reference data sets through visual interpretation, e.g., to complement existing training data or to aid in the validation of land-cover products. Through new applications such as Collect Earth, this imagery is also being used for monitoring purposes in the form of statistical surveys obtained through visual interpretation. However, little is known about where VHR satellite imagery exists globally or the dates of the imagery. Here we present a global overview of the spatial and temporal distribution of VHR satellite imagery in Google Earth and Microsoft Bing Maps. The results show an uneven availability globally, with biases in certain areas such as the USA, Europe and India, and with clear discontinuities at political borders. We also show that the availability of VHR imagery is currently not adequate for monitoring protected areas and deforestation, but is better suited for monitoring changes in cropland or urban areas using visual interpretation.

Fortresses of the Tur Abdin and the confrontation between Rome and Persia

2017 ◽  
Vol 67 ◽  
pp. 181-229
Author(s):  
Anthony Comfort
Keyword(s):  
Roman Empire ◽  
Satellite Imagery ◽  
Building Materials ◽  
Google Earth ◽  
Seventh Century ◽  
Crucial Importance ◽  
Substantial Risk ◽  
Urgent Necessity ◽  
Late Roman

AbstractAlthough research is currently impossible on the ground, satellite photographs allow some further information to be gleaned concerning the region of the Tur Abdin, of crucial importance during the wars between the late Roman Empire and Sassanian Persia in the fourth to seventh century AD. This article examines the ancient sources and the reports of visitors to the area in the light of what is now visible to all via Google Earth and other suppliers of free satellite imagery. Apart from describing the remains of the fortresses and their role in defending an important redoubt against Persian attacks, it draws attention to the urgent necessity for proper ground surveys of what remains of the fortifications of various periods before these are completely destroyed by looting and reuse of building materials. Dams also present a substantial risk to some of the monuments discussed here.

scholarly journals A Comparison of Methods for Determining Forest Composition from High-Spatial-Resolution Remotely Sensed Imagery

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1290
Author(s):  
Benjamin T. Fraser ◽  
Russell G. Congalton
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remotely Sensed ◽  
Local Scale ◽  
Google Earth ◽  
Forest Composition ◽  
Visual Interpretation ◽  
Individual Tree ◽  
Remotely Sensed Imagery ◽  
Composition Classes

Remotely sensed imagery has been used to support forest ecology and management for decades. In modern times, the propagation of high-spatial-resolution image analysis techniques and automated workflows have further strengthened this synergy, leading to the inquiry into more complex, local-scale, ecosystem characteristics. To appropriately inform decisions in forestry ecology and management, the most reliable and efficient methods should be adopted. For this reason, our research compares visual interpretation to digital (automated) processing for forest plot composition and individual tree identification. During this investigation, we qualitatively and quantitatively evaluated the process of classifying species groups within complex, mixed-species forests in New England. This analysis included a comparison of three high-resolution remotely sensed imagery sources: Google Earth, National Agriculture Imagery Program (NAIP) imagery, and unmanned aerial system (UAS) imagery. We discovered that, although the level of detail afforded by the UAS imagery spatial resolution (3.02 cm average pixel size) improved the visual interpretation results (7.87–9.59%), the highest thematic accuracy was still only 54.44% for the generalized composition groups. Our qualitative analysis of the uncertainty for visually interpreting different composition classes revealed the persistence of mislabeled hardwood compositions (including an early successional class) and an inability to consistently differentiate between ‘pure’ and ‘mixed’ stands. The results of digitally classifying the same forest compositions produced a higher level of accuracy for both detecting individual trees (93.9%) and labeling them (59.62–70.48%) using machine learning algorithms including classification and regression trees, random forest, and support vector machines. These results indicate that digital, automated, classification produced an increase in overall accuracy of 16.04% over visual interpretation for generalized forest composition classes. Other studies, which incorporate multitemporal, multispectral, or data fusion approaches provide evidence for further widening this gap. Further refinement of the methods for individual tree detection, delineation, and classification should be developed for structurally and compositionally complex forests to supplement the critical deficiency in local-scale forest information around the world.

scholarly journals Pengembangan Prototipe Sistem Informasi Jaringan Telekomunikasi PT. Telkom Untuk Penentuan Rute Optimal Dalam Penanganan Gangguan Berdasarkan Algoritma Floyd - Warshall

2017 ◽  
Vol 7 (1) ◽  
pp. 1
Author(s):  
Ircham Habib Anggara ◽  
Florence Elfriede Silalahi ◽  
Barandi Sapta Widartono
Keyword(s):  
Information System ◽  
Travel Time ◽  
Geographical Information Systems ◽  
Google Earth ◽  
Travel Distance ◽  
Telephone Network ◽  
Geographical Information ◽  
Optimal Route ◽  
Error Handling ◽  
Route Network

<p align="center"><strong><em>ABSTRAK</em></strong></p><p><em>Saat ini banyak operator telekomunikasi yang bermunculan di Indonesia sehingga menyebabkan terjadinya persaingan yang tinggi antar operator telekomunikasi. PT. Telkom selaku badan usaha yang berwenang dalam pembangunan dan pengembangan sektor telekomunikasi khususnya untuk telepon kabel, juga menyadarinya dan berupaya untuk meningkatkan pelayanan kepada pelanggan. Penelitian ini bertujuan membuat suatu basis data spasial dan model sistem informasi jaringan telepon PT. Telkom yang interaktif dengan memanfaatkan citra Quickbird yang bersumber dari Google Earth, Global Positiong System (GPS) dan Sistem Informasi Geografis (SIG) untuk penentuan rute optimal penanganan gangguan jaringan telepon PT. Telkom berdasarkan Algoritma Floyd-Warshall. Penentuan rute optimal didasarkan atas variabel impedensi, berupa jarak tempuh dan waktu tempuh yang diturunkan dari panjang jalan dibagi dengan kecepatan rata-rata kendaraan per ruas jalan. Hasil penelitian ini berupa Sistem Informasi Rute Optimal Telkom Bantul (SIROTOL) yang berbasis dekstop dan dapat berdiri sendiri tanpa adanya software SIG yang lain. Rute optimal program SIROTOL mampu digunakan untuk menentukan rute optimal penanganan gangguan jaringan telepon PT. Telkom Bantul dengan hasil yang akurat atau mendekati kondisi di lapangan. Hal tersebut dibuktikan dengan hasil validasi lapangan yang memiliki nilai uji akurasi rute optimal berdasarkan jarak tempuh sebesar 97.06% dan nilai uji akurasi rute optimal berdasarkan waktu tempuh sebesar 96.14%.</em></p><p><em> </em></p><p align="center"><strong><em>ABSTRACT</em></strong></p><p><em>Nowdays, many providers are emerging in Indonesia so that they lead high competition among telecommunication operators. As a state owned company that has authorities on the development of telecommunications sector, especially for cables telephone, PT. TELKOM also realize that, so they strive for a better service to the customers.This research aims to create a spatial database and interactive telephone network information system model of PT. Telkom by using Quickbird imagery derived from Google Earth, Global Position System (GPS) and Geographical Information Systems (GIS) to determine the optimal route telephone network for error handling based on Floyd-Warshall algorithm. Determination of the optimal route is based on the variable impedance of the travel distance and travel time derived from the length of road divided by the average speed of vehicles per road segment. Subsequent tissue analysis results are integrated with GPS navigation technology to help a network technician search for location of interference and network technicians to assist the movement towards the location of the phone to crash in the field. The result of the research is Telkom Bantul Optimal Route Information System (SIROTOL) desktop based and stand alone application. SIROTOL optimal route program can be applied to determine the optimal route accurately on Telkom Bantul’s error handling or at least close to field conditions. It can be proved by field validation results which resulted in accurate optimal route test value based on travel distance of 97.06% and travel time of 96.14%</em><em>.</em><em></em></p><p><em>Keywords: optimal route, network analysis, Floyd-Warshall algorithm, telephone network</em></p>

Sign in / Sign up

Export Citation Format

Share Document