Flooding applications enabled by high resolution, high cadence imagery from the Planet constellation of satellites

2020 ◽  
Author(s):  
Brittany Zajic ◽  
Samapriya Roy ◽  
Joseph Mascaro
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Financial Services ◽  
Situational Awareness ◽  
Private Companies ◽  
Spectral Bands ◽  
Global Cost ◽  
Earth Observation Satellites ◽  
New Applications ◽  
High Cadence

<p>Flooding is the most common and costliest global natural disaster, accounting for 43% of all recorded events in the last 20 years and increasing the global cost of flooding tenfold by 2030. Satellite imagery has proven beneficial for numerous flood use cases from historical modeling, situational awareness and extent, to risk forecasting. The addition of high resolution, high cadence satellite imagery from Planet has been widely adopted by the flood community, from researchers in academia to private companies in the insurance and financial services. </p><p>Planet Labs, Inc. currently operates over 140 satellites, comprising of the largest constellation of Earth observation satellites. The PlanetScope dataset consists of broad coverage, always-on imaging of the entire landmass by 120+ Dove satellites at 3.7 meter resolution. Complementary to PlanetScope, the SkySat dataset includes 15 high resolution satellites imaging at .72 meter resolution with the ability to image any location on Earth twice daily via tasking commands. Next-Generation PlanetScope imagery powered by SuperDove will introduce new spectral bands and interoperability positioned for the increased utilization of Planet imagery by the flood community for both existing and new applications.</p>

scholarly journals OBJECT-BASED GREENHOUSE CLASSIFICATION FROM HIGH RESOLUTION SATELLITE IMAGERY: A CASE STUDY ANTALYA-TURKEY

2016 ◽  
Vol XLI-B7 ◽  
pp. 183-187 ◽  
Author(s):  
M. Coslu ◽  
N. K. Sonmez ◽  
D. Koc-San
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Accuracy Assessment ◽  
Digital Data ◽  
Classification Method ◽  
Classification Methods ◽  
Spectral Bands ◽  
Object Based ◽  
Rule Sets ◽  
High Resolution Satellite Imagery

Pixel-based classification method is widely used with the purpose of detecting land use and land cover with remote sensing technology. Recently, object-based classification methods have begun to be used as well as pixel-based classification method on high resolution satellite imagery. In the studies conducted, it is indicated that object-based classification method has more successful results than other classification methods. While pixel-based classification method is performed according to the grey value of pixels, object-based classification process is executed by generating imagery segmentation and updatable rule sets. In this study, it was aimed to detect and map the greenhouses from object-based classification method by using high resolution satellite imagery. The study was carried out in the Antalya province which includes greenhouse intensively. The study consists of three main stages including segmentation, classification and accuracy assessment. At the first stage, which was segmentation, the most important part of the object-based imagery analysis; imagery segmentation was generated by using basic spectral bands of high resolution Worldview-2 satellite imagery. At the second stage, applying the nearest neighbour classifier to these generated segments classification process was executed, and a result map of the study area was generated. Finally, accuracy assessments were performed using land studies and digital data of the area. According to the research results, object-based greenhouse classification using high resolution satellite imagery had over 80% accuracy.

Water monitoring with Very High Resolution satellite imagery

2021 ◽  
Author(s):  
Freddie Kalaitzis ◽  
Gonzalo Mateo Garcia ◽  
Giovanni Marchisio
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Flood Risk ◽  
Time Lag ◽  
Water Monitoring ◽  
In Situ Monitoring ◽  
Flood Mapping ◽  
Water Volume ◽  
Rapid Urbanization ◽  
High Cadence

<div>The water volume on Earth's surface constantly varies with precipitation: an excess of water might lead to flooding, while its absence indicates upcoming droughts. We cannot afford in-situ monitoring devices on all rivers and streams worldwide, and free satellite imagery lacks the spatial and temporal resolution for continuous monitoring.</div><div> </div><div>This talk will provide several examples of water monitoring using PlanetScope daily imagery. The global daily coverage of Planet's data presents new opportunities for developing robust models of flood hazard, providing timely mapping in support of relief operations, and applying near real time predictive models for river flow estimation based on simultaneous measurements over entire river basins.</div><div> </div><div>First, we will describe how satellite data enable quantitative urban flood risk analysis by intersecting building segmentation maps with high risk flood zones. Rapid urbanization in developing countries is often unplanned and carries substantial risk for critical infrastructures. More frequent and severe flooding caused by climate change is exacerbating this. We capture rapid urbanization trends in African cities from high cadence imagery, and use flood risk data to quantify the humanitarian risk from flooding.</div><div> </div><div>Second, we will show hurricane Harvey risk areas and demonstrate flood mapping. Flood mapping through high-cadence data provides vital information to first respondents on the ground on the damage of road networks and infrastructure.</div><div> </div><div>Third, we will present Pix2Streams: a methodology to estimate water occurrence at the stream level developed in partnership with Frontier Development Lab and the USGS.</div><div> </div><div>Pix2Streams is a pipeline that consists of</div><div>i) a water segmentation model that fuses several days of 3m PlanetScope imagery with 1m LiDAR data that is able to detect streams 5-7m wide,</div><div>ii) integration of the output of this model with a DEM-derived flow-line map to estimate water % coverage at the stream level.</div><div> </div><div>Applying Pix2Streams across 2 years of daily PlanetScope imagery produces the first high-resolution dynamic map of stream flow frequency.</div><div>This is a new map that - if applied over entire watersheds - could fundamentally improve how we manage our water resources around the world, and may also evolve into an early warning system for floods or droughts. In particular, calibration and validation of measurements from space against USGS gage measurements downstream and the associated time lag could be a topic of future research.</div>

scholarly journals OBJECT-BASED GREENHOUSE CLASSIFICATION FROM HIGH RESOLUTION SATELLITE IMAGERY: A CASE STUDY ANTALYA-TURKEY

2016 ◽  
Vol XLI-B7 ◽  
pp. 183-187 ◽  
Author(s):  
M. Coslu ◽  
N. K. Sonmez ◽  
D. Koc-San
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Accuracy Assessment ◽  
Digital Data ◽  
Classification Method ◽  
Classification Methods ◽  
Spectral Bands ◽  
Object Based ◽  
Rule Sets ◽  
High Resolution Satellite Imagery

Pixel-based classification method is widely used with the purpose of detecting land use and land cover with remote sensing technology. Recently, object-based classification methods have begun to be used as well as pixel-based classification method on high resolution satellite imagery. In the studies conducted, it is indicated that object-based classification method has more successful results than other classification methods. While pixel-based classification method is performed according to the grey value of pixels, object-based classification process is executed by generating imagery segmentation and updatable rule sets. In this study, it was aimed to detect and map the greenhouses from object-based classification method by using high resolution satellite imagery. The study was carried out in the Antalya province which includes greenhouse intensively. The study consists of three main stages including segmentation, classification and accuracy assessment. At the first stage, which was segmentation, the most important part of the object-based imagery analysis; imagery segmentation was generated by using basic spectral bands of high resolution Worldview-2 satellite imagery. At the second stage, applying the nearest neighbour classifier to these generated segments classification process was executed, and a result map of the study area was generated. Finally, accuracy assessments were performed using land studies and digital data of the area. According to the research results, object-based greenhouse classification using high resolution satellite imagery had over 80% accuracy.

SHALLOW WATER HABITAT MAPPING AND REEF FISH STOCK ESTIMATION USING HIGH RESOLUTION SATELLITE DATA

2013 ◽  
Vol 5 (2) ◽  
Author(s):  
Vincentius P. Siregar ◽  
Sam Wouthuyzen ◽  
Andriani Sunuddin ◽  
Ari Anggoro ◽  
Ade Ayu Mustika
Keyword(s):  
High Resolution ◽  
Reef Fish ◽  
Satellite Imagery ◽  
Field Survey ◽  
Coral Cover ◽  
Fish Density ◽  
Fish Stock ◽  
Benthic Habitat ◽  
Live Coral ◽  
Stock Estimation

Shallow marine waters comprise diverse benthic types forming habitats for reef fish community, which important for the livelihood of coastal and small island inhabitants. Satellite imagery provide synoptic map of benthic habitat and further utilized to estimate reef fish stock. The objective of this research was to estimate reef fish stock in complex coral reef of Pulau Pari, by utilizing high resolution satellite imagery of the WorldView-2 in combination with field data such as visual census of reef fish. Field survey was conducted between May-August 2013 with 160 sampling points representing four sites (north, south, west, and east). The image was analy-zed and grouped into five classes of benthic habitats i.e., live coral (LC), dead coral (DC), sand (Sa), seagrass (Sg), and mix (Mx) (combination seagrass+coral and seagrass+sand). The overall accuracy of benthic habitat map was 78%. Field survey revealed that the highest live coral cover (58%) was found at the north site with fish density 3.69 and 1.50 ind/m2at 3 and 10 m depth, respectively. Meanwhile, the lowest live coral cover (18%) was found at the south site with fish density 2.79 and 2.18  ind/m2 at 3 and 10 m depth, respectively. Interpolation on fish density data in each habitat class resulted in standing stock reef fish estimation:  LC (5,340,698 ind), DC (56,254,356 ind), Sa (13,370,154 ind), Sg (1,776,195 ind) and Mx (14,557,680 ind). Keywords: mapping, satellite imagery, benthic habitat, reef fish, stock estimation

Estimations for Percentage of Impervious Area by the Use of Satellite Remote Sensing Imagery

1994 ◽  
Vol 29 (1-2) ◽  
pp. 135-144 ◽  
Author(s):  
C. Deguchi ◽  
S. Sugio
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Imagery ◽  
Satellite Remote Sensing ◽  
Aerial Photographs ◽  
Estimation Errors ◽  
Remote Sensing Imagery ◽  
Impervious Area ◽  
Urbanized Areas

This study aims to evaluate the applicability of satellite imagery in estimating the percentage of impervious area in urbanized areas. Two methods of estimation are proposed and applied to a small urbanized watershed in Japan. The area is considered under two different cases of subdivision; i.e., 14 zones and 17 zones. The satellite imageries of LANDSAT-MSS (Multi-Spectral Scanner) in 1984, MOS-MESSR(Multi-spectral Electronic Self-Scanning Radiometer) in 1988 and SPOT-HRV(High Resolution Visible) in 1988 are classified. The percentage of imperviousness in 17 zones is estimated by using these classification results. These values are compared with the ones obtained from the aerial photographs. The percent imperviousness derived from the imagery agrees well with those derived from aerial photographs. The estimation errors evaluated are less than 10%, the same as those obtained from aerial photographs.

scholarly journals Identifying Urban Poverty Using High-Resolution Satellite Imagery and Machine Learning Approaches: Implications for Housing Inequality

Land ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 648
Author(s):  
Guie Li ◽  
Zhongliang Cai ◽  
Yun Qian ◽  
Fei Chen
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Satellite Imagery ◽  
Low Income ◽  
Urban Poverty ◽  
Image Features ◽  
Housing Affordability ◽  
Learning Approaches ◽  
Housing Inequality ◽  
High Resolution Satellite Imagery

Enriching Asian perspectives on the rapid identification of urban poverty and its implications for housing inequality, this paper contributes empirical evidence about the utility of image features derived from high-resolution satellite imagery and machine learning approaches for identifying urban poverty in China at the community level. For the case of the Jiangxia District and Huangpi District of Wuhan, image features, including perimeter, line segment detector (LSD), Hough transform, gray-level cooccurrence matrix (GLCM), histogram of oriented gradients (HoG), and local binary patterns (LBP), are calculated, and four machine learning approaches and 25 variables are applied to identify urban poverty and relatively important variables. The results show that image features and machine learning approaches can be used to identify urban poverty with the best model performance with a coefficient of determination, R2, of 0.5341 and 0.5324 for Jiangxia and Huangpi, respectively, although some differences exist among the approaches and study areas. The importance of each variable differs for each approach and study area; however, the relatively important variables are similar. In particular, four variables achieved relatively satisfactory prediction results for all models and presented obvious differences in varying communities with different poverty levels. Housing inequality within low-income neighborhoods, which is a response to gaps in wealth, income, and housing affordability among social groups, is an important manifestation of urban poverty. Policy makers can implement these findings to rapidly identify urban poverty, and the findings have potential applications for addressing housing inequality and proving the rationality of urban planning for building a sustainable society.

scholarly journals Lake-to-Lake Cloud Bands: Frequencies and Locations

2007 ◽  
Vol 135 (12) ◽  
pp. 4202-4213 ◽  
Author(s):  
Yarice Rodriguez ◽  
David A. R. Kristovich ◽  
Mark R. Hjelmfelt
Keyword(s):  
High Resolution ◽  
Great Lakes ◽  
Satellite Imagery ◽  
Lake Superior ◽  
Great Lakes Region ◽  
Lake Effect ◽  
Time Period ◽  
High Resolution Satellite Imagery

Abstract Premodification of the atmosphere by upwind lakes is known to influence lake-effect snowstorm intensity and locations over downwind lakes. This study highlights perhaps the most visible manifestation of the link between convection over two or more of the Great Lakes lake-to-lake (L2L) cloud bands. Emphasis is placed on L2L cloud bands observed in high-resolution satellite imagery on 2 December 2003. These L2L cloud bands developed over Lake Superior and were modified as they passed over Lakes Michigan and Erie and intervening land areas. This event is put into a longer-term context through documentation of the frequency with which lake-effect and, particularly, L2L cloud bands occurred over a 5-yr time period over different areas of the Great Lakes region.

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