scholarly journals SYNERGETIC USE OF SENTINEL-1 AND SENTINEL-2 DATA FOR EXTRACTION OF BUILT-UP AREA IN A ROCKY DESERT OASIS, EXAMPLE FOR DRAA TAFILALT, SOUTH-EAST OF MOROCCO

2019 ◽  
Vol XLII-4/W12 ◽  
pp. 65-68 ◽  
Author(s):  
L. Eddahby ◽  
A. A. Kozlova ◽  
M. A. Popov ◽  
N. S. Lubskiy ◽  
D. Mezzane ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Weather Conditions ◽  
Processing Technique ◽  
Remote Sensing Technique ◽  
Additional Information ◽  
Signal Features ◽  
High Resolution Imagery ◽  
Urbanized Areas ◽  
Sar Data

<p><strong>Abstract.</strong> Synthetic Aperture Radar (SAR) is an active remote sensing technique capable of providing high-resolution imagery independent from daytime and to great extent unimpaired by weather conditions. Unlike the passive remote sensing active radars receive its' own reflected signal. Features of received signal make able to obtain additional information about surface objects and covers. Because of signal, double reflecting upon vertical surfaces like walls, it become common to study urbanized areas using SAR data. Because of mostly similar spectral characteristic of the typical buildings' roofs and sandy soils, that are distinguishing for Morocco, classification using visible and NIR multispectral remote sensing data is complicated. Thus, SAR data processing technique is rather useful while application to deserted area studying and urbanized areas identification.</p>

scholarly journals Sentinel-1 and -2 Based near Real Time Inland Excess Water Mapping for Optimized Water Management

2020 ◽  
Vol 12 (7) ◽  
pp. 2854 ◽  
Author(s):  
Boudewijn van Leeuwen ◽  
Zalán Tobak ◽  
Ferenc Kovács
Keyword(s):  
Water Management ◽  
Remote Sensing ◽  
Remote Sensing Data ◽  
Weather Conditions ◽  
Human Interaction ◽  
Aerial Photographs ◽  
Sensing Data ◽  
Weather Patterns ◽  
Excess Water ◽  
Frequent Monitoring

Changing climate is expected to cause more extreme weather patterns in many parts of the world. In the Carpathian Basin, it is expected that the frequency of intensive precipitation will increase causing inland excess water (IEW) in parts of the plains more frequently, while currently the phenomenon already causes great damage. This research presents and validates a new methodology to determine the extent of these floods using a combination of passive and active remote sensing data. The method can be used to monitor IEW over large areas in a fully automated way based on freely available Sentinel-1 and Sentinel-2 remote sensing imagery. The method is validated for two IEW periods in 2016 and 2018 using high-resolution optical satellite data and aerial photographs. Compared to earlier remote sensing data-based methods, our method can be applied under unfavorite weather conditions, does not need human interaction and gives accurate results for inundations larger than 1000 m2. The overall accuracy of the classification exceeds 99%; however, smaller IEW patches are underestimated due to the spatial resolution of the input data. Knowledge on the location and duration of the inundations helps to take operational measures against the water but is also required to determine the possibilities for storage of water for dry periods. The frequent monitoring of the floods supports sustainable water management in the area better than the methods currently employed.

scholarly journals POTENSI PEMANFAATAN DRONE UNTUK PENYEDIAAN DATA WILAYAH PESISIR

OSEANA ◽  
2018 ◽  
Vol 43 (1) ◽  
pp. 44-52
Author(s):  
Bayu Prayudha
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Coastal Area ◽  
Service Providers ◽  
Remote Sensing Data ◽  
Unmanned Aircraft ◽  
High Resolution Imagery ◽  
Potential Use ◽  
Vast Area ◽  
Imagery Data

POTENTIAL USE OF DRONE FOR PROVIDING DATA ON COASTAL AREA. The accurate data and information are needed for the decision maker to manage coastal area. However, the data and information of the coastal area are still lack because Indonesia has vast area and some of the locations are difficult to reach. Remote sensing is a technology that can be utilized to answer those needs. Some of the remote sensing data, especially satellite imagery can be freely acquired from various service providers using online media. Nevertheless, high resolution imagery data is still not available freely because it takes high cost and not always available at any time. One of the potential vehicle to acquire high resolution imagery data of coastal area is Unmanned Aircraft Vehicle (UAV) or widely known as drone.

scholarly journals POSSIBILITIES OF THE JOINT USE OF OPTICAL AND RADAR DATA IN FLOOD SPACE MONITORING

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 67-73
Author(s):  
O. P. Arkhipkin ◽  
G. N. Sagatdinova
Keyword(s):  
Spatial Resolution ◽  
Complex Analysis ◽  
Remote Sensing Data ◽  
Weather Conditions ◽  
High Water ◽  
Radar Data ◽  
Time Of Day ◽  
Optical Data ◽  
Additional Information ◽  
Good Spatial Resolution

<p><strong>Abstract.</strong> The article gives a brief description of the system of space monitoring of high water and floods. Its main tasks are the operational dynamics of snow and ice cover melting and the passage of flood waters. The solution of these tasks is carried out in three levels corresponding to the low, medium and high resolution of remote sensing data. An important role in monitoring is given to radar data. This is due to the features of the radar survey: independence from weather conditions and time of day, regularity, good spatial resolution, the possibility of using polarimetric properties (including phase information). The use of radar data also provides additional information, including the allocation of wet soils, flooded vegetation and infrastructure. The presence of large time periods of repeated survey, interference (cloudiness, haze, noise, etc.), different spatial resolution necessitates a complex analysis of optical and radar data in flood space monitoring. Such analysis makes it possible to better observe the flood dynamics, more precisely identify of flooding zones and determine their structure. Features of radar survey (transparency of dry snow and change of reflected signal during snowmelt) allow using them to determine the beginning of snow melt and determine the degree of water content in it. Optical data are also used to determine the area and structure of the snow cover. Method of detecting the beginning of the snowmelt period consists in the comparison of the current radar image with a base image created as an average image from the winter images with dry snow.</p>

scholarly journals OBJECT-BASED ANALYSIS OF LIDAR GEOMETRIC FEATURES FOR VEGETATION DETECTION IN SHADED AREAS

2016 ◽  
Vol XLI-B1 ◽  
pp. 43-46
Author(s):  
Yu-Ching Lin ◽  
ChinSu Lin ◽  
Ming-Da Tsai ◽  
Chun-Lin Lin
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Surface Characteristics ◽  
Point Clouds ◽  
Unit Weight ◽  
Waveform Data ◽  
Remote Sensing Technique ◽  
Object Based

The extraction of land cover information from remote sensing data is a complex process. Spectral information has been widely utilized in classifying remote sensing images. However, shadows limit the use of multispectral images because they result in loss of spectral radiometric information. In addition, true reflectance may be underestimated in shaded areas. In land cover classification, shaded areas are often left unclassified or simply assigned as a shadow class. Vegetation indices from remote sensing measurement are radiation-based measurements computed through spectral combination. They indicate vegetation properties and play an important role in remote sensing of forests. Airborne light detection and ranging (LiDAR) technology is an active remote sensing technique that produces a true orthophoto at a single wavelength. This study investigated three types of geometric lidar features where NDVI values fail to represent meaningful forest information. The three features include echo width, normalized eigenvalue, and standard deviation of the unit weight observation of the plane adjustment, and they can be derived from waveform data and discrete point clouds. Various feature combinations were carried out to evaluate the compensation of the three lidar features to vegetation detection in shaded areas. Echo width was found to outperform the other two features. Furthermore, surface characteristics estimated by echo width were similar to that by normalized eigenvalues. Compared to the combination of only NDVI and mean height difference, those including one of the three features had a positive effect on the detection of vegetation class.

scholarly journals DETECTING THE LAVA FLOW DEPOSITS FROM 2018 ANAK KRAKATAU ERUPTION USING DATA FUSION LANDSAT-8 OPTIC AND SENTINEL-1 SAR

2019 ◽  
Vol 15 (2) ◽  
pp. 157
Author(s):  
NFn Suwarsono ◽  
Indah Prasasti ◽  
Jalu Tejo Nugroho ◽  
Jansen Sitorus ◽  
Djoko Triyono
Keyword(s):  
Data Fusion ◽  
Lava Flow ◽  
Remote Sensing Data ◽  
Weather Conditions ◽  
Landsat 8 ◽  
Major Disaster ◽  
Sar Data ◽  
Using Data ◽  
Display Quality ◽  
Krakatau Volcano

The increasing volcanic activity of Anak Krakatau volcano has raised concerns about a major disaster in the area around the Sunda Strait. The objective of the research is to fuse Landsat-8 OLI (Operational Land Imager) and Sentinel-1 TOPS (Terrain Observation with Progressive Scans), an integration of SAR and optic remote sensing data, in observing the lava flow deposits resulted from Anak Krakatau eruption during the middle 2018 eruption. RGBI and the Brovey transformation were conducted to merge (fuse) the optical and SAR data.  The results showed that optical and SAR data fusion sharpened the appearance of volcano morphology and lava flow deposits. The regions are often constrained by cloud cover and volcanic ash, which occurs at the time of the volcanic eruption.  The RGBI-VV and Brovey RGB-VV methods provide better display quality results in revealing the morphology of volcanic cone and lava deposits. The entire slopes of Anak Krakatau Volcano, with a radius of about 1 km from the crater is an area prone to incandescent lava and pyroclastic falls. The direction of the lava flow has the potential to spread in all directions. The fusion method of optical Landsat-8 and Sentinel-1 SAR data can be used continuously in monitoring the activity of Anak Krakatau volcano and other volcanoes in Indonesia both in cloudy and clear weather conditions.

scholarly journals Discriminating Wet Snow and Firn for Alpine Glaciers Using Sentinel-1 Data: A Case Study at Rofental, Austria

Geosciences ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 69 ◽  
Author(s):  
Achim Heilig ◽  
Anna Wendleder ◽  
Andreas Schmitt ◽  
Christoph Mayer
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Optical Data ◽  
Optical Remote Sensing ◽  
Mountain Ranges ◽  
Snow Covered Area ◽  
Covered Area ◽  
Wet Snow ◽  
Sar Data ◽  
Sar Analysis

Continuous monitoring of glacier changes supports our understanding of climate related glacier behavior. Remote sensing data offer the unique opportunity to observe individual glaciers as well as entire mountain ranges. In this study, we used synthetic aperture radar (SAR) data to monitor the recession of wet snow area extent per season for three different glacier areas of the Rofental, Austria. For four glaciological years (GYs, 2014/2015–2017/2018), Sentinel-1 (S1) SAR data were acquired and processed. For all four GYs, the seasonal snow retreated above the elevation range of perennial firn. The described processing routine is capable of discriminating wet snow from firn areas for all GYs with sufficient accuracy. For a short in situ transect of the snow—firn boundary, SAR derived wet snow extent agreed within an accuracy of three to four pixels or 30–40 m. For entire glaciers, we used optical remote sensing imagery and field data to assess reliability of derived wet snow covered area extent. Differences in determination of snow covered area between optical data and SAR analysis did not exceed 10% on average. Offsets of SAR data to results of annual field assessments are below 10% as well. The introduced workflow for S1 data will contribute to monitoring accumulation area extent for remote and hazardous glacier areas and thus improve the data basis for such locations.

scholarly journals An Improved Method for Impervious Surface Mapping Incorporating LiDAR Data and High-Resolution Imagery at Different Acquisition Times

2018 ◽  
Vol 10 (9) ◽  
pp. 1349 ◽  
Author(s):  
Hui Luo ◽  
Le Wang ◽  
Chen Wu ◽  
Lei Zhang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Impervious Surface ◽  
Sensor Data ◽  
Lidar Data ◽  
Landscape Changes ◽  
Surface Mapping ◽  
High Resolution Imagery ◽  
High Resolution Images

Impervious surface mapping incorporating high-resolution remote sensing imagery has continued to attract increasing interest, as it can provide detailed information about urban structure and distribution. Previous studies have suggested that the combination of LiDAR data and high-resolution imagery for impervious surface mapping yields better performance than the use of high-resolution imagery alone. However, due to LiDAR data’s high cost of acquisition, it is difficult to obtain LiDAR data that was acquired at the same time as the high-resolution imagery in order to conduct impervious surface mapping by multi-sensor remote sensing data. Consequently, the occurrence of real landscape changes between multi-sensor remote sensing data sets with different acquisition times results in misclassification errors in impervious surface mapping. This issue has generally been neglected in previous works. Furthermore, observation differences that were generated from multi-sensor data—including the problems of misregistration, missing data in LiDAR data, and shadow in high-resolution images—also present obstacles to achieving the final mapping result in the fusion of LiDAR data and high-resolution images. In order to resolve these issues, we propose an improved impervious surface-mapping method incorporating both LiDAR data and high-resolution imagery with different acquisition times that consider real landscape changes and observation differences. In the proposed method, multi-sensor change detection by supervised multivariate alteration detection (MAD) is employed to identify the changed areas and mis-registered areas. The no-data areas in the LiDAR data and the shadow areas in the high-resolution image are extracted via independent classification based on the corresponding single-sensor data. Finally, an object-based post-classification fusion is proposed that takes advantage of both independent classification results while using single-sensor data and the joint classification result using stacked multi-sensor data. The impervious surface map is subsequently obtained by combining the landscape classes in the accurate classification map. Experiments covering the study site in Buffalo, NY, USA demonstrate that our method can accurately detect landscape changes and unambiguously improve the performance of impervious surface mapping.

scholarly journals Do users benefit from additional information in support of operational drought management decisions in the Ebro basin?

2018 ◽  
Vol 22 (11) ◽  
pp. 5901-5917 ◽  
Author(s):  
Clara Linés ◽  
Ana Iglesias ◽  
Luis Garrote ◽  
Vicente Sotés ◽  
Micha Werner
Keyword(s):  
Remote Sensing ◽  
Decision Model ◽  
Remote Sensing Data ◽  
Irrigation District ◽  
Irrigation Season ◽  
Risk Averse ◽  
Drought Management ◽  
Ebro Basin ◽  
Management Decisions ◽  
Additional Information

Abstract. We follow a user-based approach to examine how information supports operational drought management decisions in the Ebro basin and how these can benefit from additional information such as from remote sensing data. First we consulted decision-makers at basin, irrigation district and farmer scale to investigate the drought-related decisions they make and the information they use to support their decisions. This allowed us to identify the courses of action available to the farmers and water managers, and to analyse their choices as a function of the information they have available to them. Based on the findings of the consultation, a decision model representing the interrelated decisions of the irrigation association and the farmers was built. The purpose of the model is to quantify the effect of additional information on the decisions made. The modelled decisions, which consider the allocation of water, are determined by the expected availability of water during the irrigation season. This is currently informed primarily by observed reservoir level data. The decision model was then extended to include additional information on snow cover from remote sensing. The additional information was found to contribute to better decisions in the simulation and ultimately higher benefits for the farmers. However, the ratio between the cost of planting and the market value of the crop proved to be a critical aspect in determining the best course of action to be taken and the value of the (additional) information. Risk-averse farmers were found to benefit least from the additional information, while less risk-averse farmers stand to benefit most as the additional information helps them take better informed decisions when weighing their options.

scholarly journals Use of remote sensing to understand the terroir of the Niagara Peninsula. Applications in a Riesling vineyard

OENO One ◽  
2015 ◽  
Vol 49 (1) ◽  
pp. 1 ◽  
Author(s):  
Matthieu Marciniak ◽  
Ralph Brown ◽  
Andrew Reynolds ◽  
Marilyne Jollineau
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Water Status ◽  
Remote Sensing Data ◽  
Titratable Acidity ◽  
Weather Conditions ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Berry Composition

<p style="text-align: justify;"><strong>Aim:</strong> The purpose of this study was to determine if multispectral high spatial resolution airborne imagery could be used to segregate zones in vineyards to target fruit of highest quality for premium winemaking. We hypothesized that remotely sensed data would correlate with vine size and leaf water potential (ψ), as well as with yield and berry composition.</p><p style="text-align: justify;"><strong>Methods and results:</strong> Hypotheses were tested in a 10-ha Riesling vineyard [Thirty Bench Winemakers, Beamsville (Ontario)]. The vineyard was delineated using GPS and 519 vines were geo-referenced. Six sub-blocks were delineated for study. Four were identified based on vine canopy size (low, high) with remote sensing in 2005. Airborne images were collected with a four-band digital camera every 3-4 weeks over 3 seasons (2007-2009). Normalized difference vegetation index (NDVI) values (NDVI-red, green) and greenness ratio were calculated from the images. Single-leaf reflectance spectra were collected to compare vegetation indices (VIs) obtained from ground-based and airborne remote-sensing data. Soil moisture, leaf ψ, yield components, vine size, and fruit composition were also measured. Strong positive correlations were observed between VIs and vine size throughout the growing season. Vines with higher VIs during average to dry years had enhanced fruit maturity (higher °Brix and lower titratable acidity). Berry monoterpenes always had the same relationship with remote sensing variables regardless of weather conditions.</p><p style="text-align: justify;"><strong>Conclusions:</strong> Remote sensing images can assist in delineating vineyard zones where fruit will be of different maturity levels, or will have different concentrations of aroma compounds. Those zones could be considered as sub-blocks and processed separately to make wines that reflect those terroir differences. Strongest relationships between remotely sensed VIs and berry composition variables occurred when images were taken around veraison.</p><strong>Significance and impact of the study:</strong> Remote sensing may be effective to quantify spatial variation in grape flavour potential within vineyards, in addition to characteristics such as water status, yield, and vine size. This study was unique by employing remote sensing in cover-cropped vineyards and using protocols for excluding spectral reflectance contributed by inter-row vegetation.

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