Ground Validation of Seismic Line Forest Regeneration Assessments Based on Visual Interpretation of Satellite Imagery

<p>Extensive road construction works were recently undertaken in the remote eastern part of the Peruvian Cordillera Blanca, aiming at better connecting isolated mountain communities with regional administrative centres. In the R&#237;o Lucma catchment, approximately 47 km of roads were constructed between 2015 and 2018, triggering several landslides that affected an approximate area of 32 ha. We identified and characterised these landslides by combining field mapping, visual interpretation and semi-automated analysis of satellite imagery (PlanetScope and RapidEye-2), and analysis of rainfall data from two stations of the Servicio Nacional de Meteorolog&#237;a e Hidrolog&#237;a del Per&#250; (SENAMHI). We investigated in detail three specific areas of interest, where we identified, delineated, and described 56 landslides. We classified the landslides in relation to their position to the road as: landslides downslope the roads (48.2%), complex landslides crossing the roads (46.4 %), and landslides onto the road (5.3%). According to the type of movement, we found that the slide-type movement (60.7%) prevails over the flow-type movement (39.3%). Timewise, we found that 75% of landslides were observed on satellite imagery simultaneously with road construction work, while the remaining 25% were identified between one week and seven months after the roads had been constructed. We analysed lagged cumulative rainfall data against the occurrence of these subsequent landslides, determining that a two-week rainfall accumulation can act as triggering factor of landslides after road construction work. In general, 51% of the landslides were observed during the wet season (November to April) while 41.1% occurred during El Ni&#241;o&#8211;Southern Oscillation (ENSO) strong cool phase or &#8220;La Ni&#241;a&#8221; period. We observed that the majority of mapped landslides were directly (e.g., landslides resulting from slope undercutting) or indirectly associated with road constructions (e.g., rainfall-induced landslides resulting from a combination of extreme precipitation over slopes with decreased stability) and that the road constructions also may set preconditions for subsequent rainfall-triggered landslides.</p>

Download Full-text

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 ◽

10.3390/land7040118 ◽

2018 ◽

Vol 7 (4) ◽

pp. 118 ◽

Cited By ~ 18

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.

Download Full-text

Post-Disaster Urban Recovery Monitoring in Pisco After the 2007 Peru Earthquake Using Satellite Image

Journal of Disaster Research ◽

10.20965/jdr.2014.p1059 ◽

2014 ◽

Vol 9 (6) ◽

pp. 1059-1068 ◽

Cited By ~ 11

Author(s):

Tomoyo Hoshi ◽

◽

Osamu Murao ◽

Kunihiko Yoshino ◽

Fumio Yamazaki ◽

...

Keyword(s):

Satellite Imagery ◽

Urban Areas ◽

Satellite Image ◽

Recovery Process ◽

Visual Interpretation ◽

Field Surveys ◽

Recovery Processes ◽

The City ◽

Post Disaster

Pisco was the area most damaged by the 2007 Peru earthquake. The purpose of this research is to develop possibilities of using satellite imagery to monitor postdisaster urban recovery processes, focusing on the urban change in Pisco between 2007 and 2011. To this end, the authors carried out field surveys in the city in 2012 and 2013 and also examined previous surveys to determine that building reconstruction peaked between 2008 and 2009. After analyzing the five-year recovery process, the authors compared its reconstruction conditions by visual interpretation with those by image analysis using satellite image. An accuracy of 71.2% was achieved for the visual interpretation results in congested urban areas, and that for developed districts was about 60%. The result shows that satellite imagery can be a useful tool for monitoring and understanding post-disaster urban recovery processes in the areas in which conducting long-term field survey is difficult.

Download Full-text

Coral reef monitoring in Panjang Island, Central Bangka

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/926/1/012099 ◽

2021 ◽

Vol 926 (1) ◽

pp. 012099

Author(s):

W Adi ◽

I Akhrianti ◽

M Hudatwi

Keyword(s):

Remote Sensing ◽

Coral Reef ◽

Coral Reefs ◽

Satellite Imagery ◽

Good Condition ◽

Aerial Photographs ◽

Visual Interpretation ◽

Composite Band ◽

Sentinel 2A ◽

Color Composite

Abstract Bangka Island is the largest tin producer in Indonesia and since the granting of tin mining freedom in 2000, unconventional tin mining (TI) is increasingly prevalent. The existence of mining activities will directly or indirectly damage the environment both on land and at sea. Especially the high biodiversity of coral reef ecosystem. The purpose of this research was to analyze a map of the distribution of coral reef based on Sentinel 2A satellite imagery data. Analyze the extent of the coral reefs in shallow waters of Putri Island, and analyze of the condition coral reefs (percentage cover, mortality index and genus diversity) with using collaboration betwen the coral diving data and remote sensing data. Studies of changes in coral reef ecosystems have been ongoing since several decades ago. The combination of satellite imagery and aerial photographs is capable of making long-term and continuous observations on mapping and change detection. Remote sensing technology has several advantages overconventional sampling to monitor a large area in time almost simultaneously and continuously including the difficult to explore areas. This research was conducted with visual interpretation by using standard true color composite band (483) and false color composite band (843) of Sentinel 2A and also using lyzenga transformation. Estimation of coral reefs area based on result is 475,96 ha (2016) and decreased to 475 ha (2021). The condition of coral reefs at the research location is a good condition.

Download Full-text

Evaluation of Satellite Imagery for Monitoring Pacific Walruses at a Large Coastal Haulout

Remote Sensing ◽

10.3390/rs13214266 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4266

Author(s):

Anthony S. Fischbach ◽

David C. Douglas

Keyword(s):

Satellite Imagery ◽

Chukchi Sea ◽

Late Summer ◽

Visual Interpretation ◽

Odobenus Rosmarus Divergens ◽

Radar Imagery ◽

Moderate Resolution ◽

Satellite Sensors ◽

Sar Imagery ◽

Sentinel 2

Pacific walruses (Odobenus rosmarus divergens) are using coastal haulouts in the Chukchi Sea more often and in larger numbers to rest between foraging bouts in late summer and autumn in recent years, because climate warming has reduced availability of sea ice that historically had provided resting platforms near their preferred benthic feeding grounds. With greater numbers of walruses hauling out in large aggregations, new opportunities are presented for monitoring the population. Here we evaluate different types of satellite imagery for detecting and delineating the peripheries of walrus aggregations at a commonly used haulout near Point Lay, Alaska, in 2018–2020. We evaluated optical and radar imagery ranging in pixel resolutions from 40 m to ~1 m: specifically, optical imagery from Landsat, Sentinel-2, Planet Labs, and DigitalGlobe, and synthetic aperture radar (SAR) imagery from Sentinel-1 and TerraSAR-X. Three observers independently examined satellite images to detect walrus aggregations and digitized their peripheries using visual interpretation. We compared interpretations between observers and to high-resolution (~2 cm) ortho-corrected imagery collected by a small unoccupied aerial system (UAS). Roughly two-thirds of the time, clouds precluded clear optical views of the study area from satellite. SAR was unaffected by clouds (and darkness) and provided unambiguous signatures of walrus aggregations at the Point Lay haulout. Among imagery types with 4–10 m resolution, observers unanimously agreed on all detections of walruses, and attained an average 65% overlap (sd 12.0, n 100) in their delineations of aggregation boundaries. For imagery with ~1 m resolution, overlap agreement was higher (mean 85%, sd 3.0, n 11). We found that optical satellite sensors with moderate resolution and high revisitation rates, such as PlanetScope and Sentinel-2, demonstrated robust and repeatable qualities for monitoring walrus haulouts, but temporal gaps between observations due to clouds were common. SAR imagery also demonstrated robust capabilities for monitoring the Point Lay haulout, but more research is needed to evaluate SAR at haulouts with more complex local terrain and beach substrates.

Download Full-text

Analysis of Rice Field Area Conversion in Sleman Regency from 2000 to 2015, Using High-Resolution Satellite Imagery (Case Study: Ngaglik, Mlati and Depok Sub-District)

Journal of Applied Geospatial Information ◽

10.30871/jagi.v3i1.1223 ◽

2019 ◽

Vol 3 (1) ◽

pp. 195-203

Author(s):

Prelin Leunupun ◽

Frederik Samuel Papilaya

Keyword(s):

High Resolution ◽

Satellite Imagery ◽

Rice Field ◽

Research Area ◽

Visual Interpretation ◽

High Resolution Satellite Imagery ◽

Interpretation Method ◽

Field Area ◽

Yogyakarta City

The purpose of this research is to find out how much area of rice fields which is reduced due to being converted into built-up land in Sleman Regency, especially in sub-districts which adjacent to Yogyakarta City, such as Depok Sub-district, Mlati Sub-district and Ngaglik Sub-district, from 2000 to 2015. Classification method used in this research is visual interpretation method which utilized on-screen digitization. The output of this research is a Map of Rice Field Conversion into Built-up Land at Depok, Mlati and Ngaglik Sub-district in Sleman Regency from 2000 to 2015. The results of this research prove that GIS can be used to determine the extent of changes in a rice field at Ngaglik, Depok and Mlati sub-districts. The area of rice field that was converted into built-up land in the research area is 864.45 ha.

Download Full-text

Experiences from Recent Geo-Wiki Citizen Science Campaigns in the Creation and Sharing of New Reference Data Sets on Land Cover and Land Use

10.5194/egusphere-egu21-10871 ◽

2021 ◽

Author(s):

Juan Carlos Laso Bayas ◽

Linda See ◽

Myroslava Lesiv ◽

Martina Dürauer ◽

Ivelina Georgieva ◽

...

Keyword(s):

Land Use ◽

Data Collection ◽

Citizen Science ◽

Satellite Imagery ◽

Reference Data ◽

Scientific Publication ◽

Agricultural Fields ◽

Data Sets ◽

Visual Interpretation ◽

Data Set

<div> <p>Geo-Wiki is an online platform for involving citizens in the visual interpretation of very high-resolution satellite imagery to collect reference data on land cover and land use. Instead of being an ongoing citizen science project, short intensive campaigns are organized in which citizens participate. The advantage of this approach is that large amounts of data are collected in a short amount of time with a clearly defined data collection target to reach. Participants can also schedule their time accordingly, with their past feedback indicating that this intensive approach was preferred. The reference data are then used in further scientific research to answer a range of questions such as: How much of the land&#8217;s surface is wild or impacted by humans?&#160; What is the size of agricultural fields globally? The campaigns are organized as competitions with prizes that include Amazon vouchers and co-authorship on a scientific publication. The scientific publication is the mechanism by which the data are openly shared so that other researchers can use this reference data set in other applications. The publication is usually in the form of a data paper, which explains the campaign in detail along with the data set collected. The data are uploaded to a repository such as Pangaea, ZENODO or IIASA&#8217;s own data repository, DARE.&#160; This approach from data collection, to opening up the data, to documentation via a scientific data paper also ensures transparency in the data collection process. There have been several Geo-Wiki citizen science campaigns that have been run over the last decade. Here we provide examples of experiences from five recent campaigns: (i) the Global Cropland mapping campaign to build a cropland validation data set; (ii) the Global Field Size campaign to characterize the size of agricultural fields around the world; (iii) the Human Impact on Forests campaign to produce the first global map of forest management; (iv) the Global Built-up Surface Validation campaign to collect data on built-up surfaces for validation of global built-up products such as the Global Human Settlement Layer (https://ghsl.jrc.ec.europa.eu/); and (v) the Drivers of Tropical Forest Loss campaign, which collected data on the main causes of deforestation in the tropics. In addition to outlining the campaign, the data sets collected and the sharing of the data online, we provide lessons learned from these campaigns, which have built upon experiences collected over the last decade. These include insights related to the quality and consistency of the classifications of the volunteers including different volunteer behaviors; best practices in creating control points for use in the gamification and quality assurance of the campaigns; different methods for training the volunteers in visual interpretation; difficulties in the interpretation of some features, which may need expert input instead as well as the inability of some features to be recognized from satellite imagery; and limitations in the approach regarding change detection due to temporal availability of open satellite imagery, among several others.&#160;</p> </div>

Download Full-text

Monitoring Selective Logging in Tropical Evergreen Forests Using Landsat: Multitemporal Regional Analyses in Mato Grosso, Brazil

Earth Interactions ◽

10.1175/ei142.1 ◽

2005 ◽

Vol 9 (24) ◽

pp. 1-24 ◽

Cited By ~ 22

Author(s):

Eraldo A. T. Matricardi ◽

David L. Skole ◽

Mark A. Cochrane ◽

Jiaguo Qi ◽

Walter Chomentowski

Keyword(s):

Forest Regeneration ◽

Visual Detection ◽

Selective Logging ◽

Visual Interpretation ◽

Mato Grosso ◽

Annual Variations ◽

Evergreen Forests ◽

Regional Analyses ◽

Semiautomated Method ◽

Rapid Regrowth

Abstract Selective logging degrades tropical forests. Logging operations vary in timing, location, and intensity. Evidence of this land use is rapidly obscured by forest regeneration and ongoing deforestation. A detailed study of selective logging operations was conducted near Sinop, State of Mato Grosso, Brazil, one of the key Amazonian logging centers. An 11-yr series of annual Lansdat images (1992–2002) was used to detect and track logged forests across the landscape. A semiautomated method was applied and compared to both visual interpretation and field data. Although visual detection provided precise delineation of some logged areas, it missed many areas. The semiautomated technique provided the best estimates of logging extent that are largely independent of potential user bias. Multitemporal analyses allowed the authors to analyze the annual variations in logging and deforestation, as well as the interaction between them. It is shown that, because of both rapid regrowth and deforestation, evidence of logging activities often disappeared within 1–3 yr. During the 1992–2002 interval, a total of 11 449 km2 of forest was selectively logged. Around 17% of these logged forests had been deforested by 2002. An intra-annual analysis was also conducted using four images spread over a single year. Nearly 3% of logged forests were rapidly deforested during the year in which logging occurred, indicating that even annual monitoring will underestimate logging extent. Great care will need to be taken when inferring logging rates from observations greater than a year apart because of the partial detection of previous years of logging activity.

Download Full-text

COASTLINE CHANGES AT BUNGUS BAY PADANG CITY, WEST SUMATERA PROVINCE BASED ON SATELLITE IMAGERY ANALYSES

Jurnal Ilmu dan Teknologi Kelautan Tropis ◽

10.29244/jitkt.v5i2.7570 ◽

2014 ◽

Vol 5 (2) ◽

Author(s):

Yulius Yulius ◽

M. Ramdhan ◽

M. Ramdhan

Keyword(s):

Surface Area ◽

Satellite Imagery ◽

Image Texture ◽

Visual Interpretation ◽

Coastline Change ◽

Moderate Change

The Bungus Bay with 21,050 meters of coastline length and 1,383.86 ha of surface area confines with a rounded shape surface. This study aimed to determine coastline changes in the Bungus Bay based on overlay analyses of satellite imagery of 2000, 2006, 2010, and 2011. The method used in this research was visual interpretation using four key interpretation such as hue image, texture association, and shape. The results showed that in general there were abrasion processes in the Bungus Bay. The abrasion processes were more dominant in the Buo Bay, Kaluang Bay, and Kabuang Bay. The largest coastline changes occurred in the northern Bungus Bay for 26 m/yr, while in the Kaluang Bay and Kabuang Bay exhibited a moderate change of 9 m/yr. In general, the rate of coastline change in the Bungus Bay was 5.9 m/yr.Keywords: abration, accretion, coastline changes, Bungus Bay

Download Full-text

A pilot study of methodology for the development of farmland habitat reports for sustainability assessments

Irish Journal of Agricultural and Food Research ◽

10.15212/ijafr-2020-0103 ◽

2020 ◽

Author(s):

J.A. Finn ◽

P. Moran

Keyword(s):

Satellite Imagery ◽

Pilot Project ◽

Visual Assessment ◽

Value Assessment ◽

Visual Interpretation ◽

Habitat Features ◽

Level Ii ◽

Challenges And Opportunities ◽

Certification And Accreditation ◽

Farm Scale

The inclusion of farm maps of habitat features is becoming an urgent requirement for assessments of farm-scale sustainability and for compliance or benchmarking with national and international sustainability certification and accreditation schemes. Traditional methods of habitat assessment rely strongly on field-based surveys, which are logistically demanding and relatively costly. We describe and investigate a process that relies on information technology to develop a scalable method that can be applied across multiple farms to reduce the significant logistical challenges and financial costs of traditional habitat surveys. A key impediment to the routine development of farm habitat maps is the lack of information on the type of habitats that occur on a land parcel. Within a pilot project comprising 187 farms, we developed and implemented a process for creating farm habitat reports and investigate the accuracy of visual interpretation of satellite imagery by an ecologist aiming to identify habitat types. We generated customised farm reports that included a colour-coded farm habitat map and habitat information (type, area, relative wildlife importance). Visual assessment of satellite imagery achieved an overall accuracy of 96% in its ability to discriminate between land parcels with habitats categorised by this study as being of either high or low nature conservation value. Assessment of satellite imagery achieved an overall accuracy of 90% in its ability to discriminate among Fossitt level II habitat classes, and an overall accuracy of 81% when using individual habitat classes (Fossitt level III). There was, however, considerable variation in the accuracy associated with individual habitat classes. We conclude that this methodology based on satellite imagery is sufficiently accurate to be used for the incorporation of farmland habitats into farm-scale sustainability assurance, but should, at most, use Fossitt level II habitat classes. We discuss future challenges and opportunities for the development of farm habitat maps and plans for their use in sustainability certification schemes.

Download Full-text