Inland Water levels… Globally. Copernicus did it again!

The BlueDot Water Observatory provides timely information about water levels of lakes, dams, reservoirs, wetlands and similar water bodies globally. It is based on the Copernicus satellite imagery, acquired using sentinelhub Python package, which uses Sentinel Hub services.

The key benefit of the service is the accumulation of global current and historical water level data in one place. Due to its cost-effective approach, anyone is able to access water level information freely; not only authorities, but also citizens can now better understand the state of their local and global environment.

With over three quarters of water covering the planet and 70% of human body consisting of water, this wonder liquid permeates both land and body, making it an essential substance both for the development and nourishment of life and the sustenance of the environment. For human needs—apart from our dependence on it for survival—water also lies at the heart of economic and social development. In times of abundance, water enables economic growth, which in turn reduces poverty, and in times of scarcity, it can cause life-threatening crises. To be able to predict such disasters, water monitoring is of utmost importance.

The Water Observatory is an Earth-observation-based solution that provides reliable and timely information about surface water levels of waterbodies across the globe. All observations are provided and can be explored interactively via the Water Observatory Dashboard or via RESTful API. The Water Observatory provides a valuable service to local authorities, governmental agencies, natural parks and reserves, agricultural ministries and agencies, stakeholders in food and energy production, and citizens alike.

The global database of water bodies – lakes, dams, reservoirs – is built on top of existing databases:

​Unfortunately, the accuracy of polygons outlining the nominal extent of the water bodies in these databases is insufficient (see Figures below). We therefore used these datasets as collection of potentially interesting water bodies and extracted their polygons from the OpenStreetMap. The database is available here.

At the moment, our database consists of over 40000 waterbodies, out of which around 7000 are monitored and displayed in the Water Observatory Dashboard.

Nominal outlines of Alarcon Dam in Spain from BlueDot’s water bodies database (blue), GRanD (green), and GLWD (orange). Imagery from Digital Globe, 2018, Google.

The Water Observatory extracts the surface water levels from Sentinel-2 optical satellite imagery provided by the Copernicus program. The algorithm is implemented in Python and among other consists of the following steps:

  1. download single-band image of a Normalized Difference Water Index (NDWI) using Sentinel Hub‘s WCS service
  2. detect clouds using Sinergise’s s2cloudless cloud detector
    • ​reject all images that are too cloudy
  3. ​run Canny edge detector on the NDWI single-band image and dilate the edges
  4. derive binary water mask with Otsu’s method using only the dilated-edge-pixels
  5. polygonize water mask with rasterio

The Water Observatory results can be explored using its dashboard. Simply navigate the map (pan and zoom) [3] to select a waterbody (dots in the map). After the waterbody is selected all other elements in the dashboard get updated:

  • Search console, name of the waterbody and country, date of observation, water coverage and total number of all valid observations for selected waterbody.
  • True color image of selected waterbody with nominal water extent and observed water extent. 
  • Surface water levels since end of the 2015 up to the most recent Sentinel-2 image from few days ago. Selection of an observation on another date updates the image.

This is the way the dashboard works. Almost magic.

Done using GIMP, Autoclicker and Auto Screen Capture

And this other GIF playing inversely (amazing how this minor visualization change helps us to understand what is going on very quickly, also how important using up-to-date vectors is).

Done using GIMP, Autoclicker and Auto Screen Capture

Sources:

https://water.blue-dot-observatory.com/20019/2019-12-03
https://www.blue-dot-observatory.com/aboutwaterobservatory
https://medium.com/sentinel-hub/bluedot-eo-solution-for-water-resources-monitoring-d7663c21af16
https://medium.com/sentinel-hub/global-earth-observation-service-from-your-laptop-23157680cf5e
https://github.com/sentinel-hub/water-observatory-backend

Software used:
GIMP
Autoclicker
Auto Screen Capture

Hope you like this,

Alberto
GIS Analyst

NO AL PELOTAZO DE LA ERMITA DEL SANTO

Esta semana pasada he estado colaborando puntualmente con la Plataforma “planermitadelsanto”, vecinos como yo que se han (nos hemos) organizado para echar abajo un plan de recalificación urbanística que no es ni mucho menos del interés general. He preparado para ellos algunos mapas en 3D (georeferenciando planos, extruyendo manualmente usando datos del Plan propuesto, realizando los mapas de Sombras y visualizando en Google Earth, Global Mapper y Sketchup). Aquí abajo os muestro algunos más datos así como recortes de prensa. ¡Os iré contando!!!.

(…)
Un fuerte movimiento vecinal ha surgido rápidamente en contra del plan que el Ayuntamiento de Madrid pretende aprobar en el centro comercial Ermita del Santo:

  • 600 viviendas en 21 bloques
  • Torres de hasta 28 pisos de altura (Estimación de 84m altura)
  • 1089 plazas de aparcamiento
  • Cero servicios públicos (centros de salud, educativos y de mayores, transporte, instalaciones deportivas y culturales)
3D realizado por Geovisualization.net (Global Mapper +Google Earth)

Revestido de un presunto interés general, con la “Modificación puntual del Plan General de ordenación urbana”, el ayuntamiento solo atiende al interés privado de los dueños de los terrenos recalificados, propiedad de “Desarrollos Ermita del Santo Socimi S.A.”, que opera bajo el régimen fiscal especial SOCIMI.

La retórica del proyecto es un ejemplo clarísimo de greenwashing para disfrazar pura especulación y lucro privado en perjuicio de vecinos, que sufrirán masificación, deterioro del paisaje, colapso de los escasos recursos básicos y de los servicios públicos del barrio, congestión del tráfico, contaminación ambiental, contaminación acústica…

Geovisualization.net

Los argumentos que el ayuntamiento esgrime para la modificación del plan general, tal y como se exponen en el resumen ejecutivo del proyecto, son claramente incoherentes con la realidad del barrio y las necesidades actuales, tradicionalmente desatendidas por el consistorio.

Nos quieren colar un barrio infinitamente peor y no vamos a permitirlo.

Estamos corriendo contrarreloj para presentar las mejores alegaciones posibles antes del 3 de octubre, fecha en la que termina el plazo.

Hemos contactado con todos los grupos políticos municipales, del distrito y del pleno, interpelándoles sobre su postura y pidiendo que nos acompañen y que se opongan a un modelo de ciudad que no queremos y pretenden imponernos.

3D realizado por Geovisualization.net (Global Mapper +Google Earth)
3D realizado por Geovisualization.net (Global Mapper +Google Earth)
Sketch up. Análisis 3D de Sombras. Ermita del Santo. 20220926 (día completo)

Pero el 3 de octubre solo es el principio. Estamos en plena forma y vamos a darlo todo para que no nos roben el barrio.

(…)

https://www.eldiario.es/madrid/somos/cientos-vecinos-madrid-rio-oponen-pelotazo-ermita-santo-pura-especulacion_1_9570670.html

https://www.telemadrid.es/programas/buenos-dias-madrid/Vecinos-de-Latina-se-movilizan-contra-la-construccion-de-600-viviendas-en-el-centro-comercial-la-Ermita-del-Santo-2-2491270853–20220927084045.html

#PelotazoErmita
@PelotazoErmita
planermitadelsanto@gmail.com

Google Earth Engine and Dynamic World

Let me please introduce you this “new” LULC source I have come across with recently. The potential of this 10m “clutter” source is being able to acquire data from a few days ago instead of using outdated “very old” 2020 vintage datasets. I know, if these days something 2020 is very old then myself, born in 1972, I’m older than the riverside, older than peeing in a wall, even older than Methuselah (all these are spanish sayings) Yes, that’s the way it is nowadays.

Google Earth Engine

Google Earth Engine is a geospatial processing service where you can perform geospatial processing at scale, powered by Google Cloud Platform. The purpose of Earth Engine is to:

Provide an interactive platform for geospatial algorithm development at scale
Enable high-impact, data-driven science
Make substantive progress on global challenges that involve large geospatial datasets

Google Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities. Scientists, researchers, and developers use Earth Engine to detect changes, map trends, and quantify differences on the Earth’s surface. Earth Engine is now available for commercial use, and remains free for academic and research use.

Google Earth Engine – Dynamic World

Dynamic World is a near realtime land cover dataset for our constantly changing planet. The real world is as dynamic as the people and natural processes that shape it. Dynamic World is a near realtime 10m resolution global land use land cover dataset, produced using deep learning, freely available and openly licensed. It is the result of a partnership between Google and the World Resources Institute, to produce a dynamic dataset of the physical material on the surface of the Earth. Dynamic World is intended to be used as a data product for users to add custom rules with which to assign final class values, producing derivative land cover maps.

Key innovations of Dynamic World

https://dynamicworld.app/explore

Near realtime data. Over 5000 Dynamic World image are produced every day, whereas traditional approaches to building land cover data can take months or years to produce. As a result of leveraging a novel deep learning approach, based on Sentinel-2 Top of Atmosphere, Dynamic World offers global land cover updating every 2-5 days depending on location.
Per-pixel probabilities across 9 land cover classes. A major benefit of an AI-powered approach is the model looks at an incoming Sentinel-2 satellite image and, for every pixel in the image, estimates the degree of tree cover, how built up a particular area is, or snow coverage if there’s been a recent snowstorm, for example.

Which is the layer that interests you the most?

Ten meter resolution. As a result of the European Commission’s Copernicus Programme making European Space Agency Sentinel data freely and openly available, products like Dynamic World are able to offer 10m resolution land cover data. This is important because quantifying data in higher resolution produces more accurate results for what’s really on the surface of the Earth.
Dynamic World is produced using the Google Earth Engine and AI Platform. We developed the Dynamic World dataset in alignment with Google’s AI Principles.

Explore Dynamic World in Resource Watch by the World Resources Institute, and learn more about WRI’s Land & Carbon Lab.

Read the Nature Scientific Data publication:
Dynamic World, Near real-time global 10m land use land cover mapping (you can find the link in sources section below).

Dynamic World is a 10m near-real-time (NRT) Land Use/Land Cover (LULC) dataset that includes class probabilities and label information for nine classes. Dynamic World predictions are available for the Sentinel-2 L1C collection from 2015-06-27 to present. The revisit frequency of Sentinel-2 is between 2-5 days.

Dynamic World predictions are available for the Sentinel-2 L1C collection from 2015-06-27 to present. The revisit frequency of Sentinel-2 is between 2-5 days depending on latitude. Dynamic World predictions are generated for images for Sentinel-2 L1C images with CLOUDY_PIXEL_PERCENTAGE <= 35%. Predictions are masked to remove clouds and cloud shadows using a combination of S2 Cloud Probability, Cloud Displacement Index, and Directional Distance Transform.

Images in the Dynamic World collection have names matching the individual Sentinel-2 L1C asset names from which they were derived, e.g:

ee.Image(‘COPERNICUS/S2/20160711T084022_20160711T084751_T35PKT’)

has a matching Dynamic World image named: ee.Image(‘GOOGLE/DYNAMICWORLD/V1/20160711T084022_20160711T084751_T35PKT’).

All probability bands except the “label” band collectively sum to 1.

To learn more about the Dynamic World dataset and see examples for generating composites, calculating regional statistics, and working with the time series, see the Introduction to Dynamic World tutorial series.

Given Dynamic World class estimations are derived from single images using a spatial context from a small moving window, top-1 “probabilities” for predicted land covers that are in-part defined by cover over time, like crops, can be comparatively low in the absence of obvious distinguishing features. High-return surfaces in arid climates, sand, sunglint, etc may also exhibit this phenomenon.

To select only pixels that confidently belong to a Dynamic World class, it is recommended to mask Dynamic World outputs by thresholding the estimated “probability” of the top-1 prediction.

Now it’s time for you to put your hands on. Create a Google Earth Engine account, copy paste this code below, change dates, minor parameters here and there, find how to export to your format… Let’s get started!!!!

https://code.earthengine.google.com/

Google Earth Engine

————Explore in GEE———————-

// Construct a collection of corresponding Dynamic World and Sentinel-2 for
// inspection. Filter the DW and S2 collections by region and date.
var COL_FILTER = ee.Filter.and(
    ee.Filter.bounds(ee.Geometry.Point(20.6729, 52.4305)),
    ee.Filter.date('2021-04-02', '2021-04-03'));

var dwCol = ee.ImageCollection('GOOGLE/DYNAMICWORLD/V1').filter(COL_FILTER);
var s2Col = ee.ImageCollection('COPERNICUS/S2').filter(COL_FILTER);

// Join corresponding DW and S2 images (by system:index).
var DwS2Col = ee.Join.saveFirst('s2_img').apply(dwCol, s2Col,
    ee.Filter.equals({leftField: 'system:index', rightField: 'system:index'}));

// Extract an example DW image and its source S2 image.
var dwImage = ee.Image(DwS2Col.first());
var s2Image = ee.Image(dwImage.get('s2_img'));

// Create a visualization that blends DW class label with probability.
// Define list pairs of DW LULC label and color.
var CLASS_NAMES = [
    'water', 'trees', 'grass', 'flooded_vegetation', 'crops',
    'shrub_and_scrub', 'built', 'bare', 'snow_and_ice'];

var VIS_PALETTE = [
    '419BDF', '397D49', '88B053', '7A87C6',
    'E49635', 'DFC35A', 'C4281B', 'A59B8F',
    'B39FE1'];

// Create an RGB image of the label (most likely class) on [0, 1].
var dwRgb = dwImage
    .select('label')
    .visualize({min: 0, max: 8, palette: VIS_PALETTE})
    .divide(255);

// Get the most likely class probability.
var top1Prob = dwImage.select(CLASS_NAMES).reduce(ee.Reducer.max());

// Create a hillshade of the most likely class probability on [0, 1];
var top1ProbHillshade =
    ee.Terrain.hillshade(top1Prob.multiply(100))
    .divide(255);

// Combine the RGB image with the hillshade.
var dwRgbHillshade = dwRgb.multiply(top1ProbHillshade);

// Display the Dynamic World visualization with the source Sentinel-2 image.
Map.setCenter(20.6729, 52.4305, 12);
Map.addLayer(
    s2Image,
    {min: 0, max: 3000, bands: ['B4', 'B3', 'B2']},
    'Sentinel-2 L1C');
Map.addLayer(
    dwRgbHillshade,
    {min: 0, max: 0.65},
    'Dynamic World');

————Explore in GEE———————-

Summarizing, these days a lot of new 10m land use datasets have just arrived, which one is the best?. this article explains it well:

Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover (you can find the link in sources section below).

Abstract: The European Space Agency’s Sentinel satellites have laid the foundation for global
land use land cover (LULC) mapping with unprecedented detail at 10 m resolution. We present a
cross-comparison and accuracy assessment of Google’s Dynamic World (DW), ESA’s World Cover
(WC) and Esri’s Land Cover (Esri) products for the first time in order to inform the adoption and
application of these maps going forward. For the year 2020, the three global LULC maps show
strong spatial correspondence (i.e., near-equal area estimates) for water, built area, trees and crop
LULC classes.

Conclusions
LULC mapping at global extents has been revolutionized by the plethora of mediumresolution satellite data available from programs such as Landsat and Sentinel. In our
cross-comparison of global 10 m resolution LULC maps, we found large inaccuracies and
spatial and thematic biases in each product that vary across biomes, continents and human
settlement types. Our overarching recommendation is to critically evaluate each LULC product with reference to the application purpose. We highlight the novelty of DW as a global near real-time LULC product with class probability scores. LULC types, regardless of definition and type system, share with ecosystems the property that their composition, structure and processes often vary in a gradual, continuous manner over space and time.
We suggest that the DW probability scores offer a fundamental shift in land cover mapping from categorical to continuum concepts.

So that’s what we will do, using each one of them after deep evaluation of purposes and accuracy needed.

Sources:
https://earthengine.google.com/
https://dynamicworld.app/
https://www.nature.com/articles/s41597-022-01307-4
https://developers.google.com/earth-engine/datasets/
https://developers.google.com/earth-engine/datasets/tags/landcover
https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1
https://www.mdpi.com/2072-4292/14/16/4101

Tracking fires LIVE: Boiro (A Coruña, Spain)

We can track this up as we speak. Active fire monitoring from the Sentinel Hub EO Browser platform of Copernicus. We can measure burn areas on a regular basis, creating videos, capturing images, creating indexes, histograms, including METEO in the analysis, etc. Everything for better understanding what is going on and maybe, why not, avoiding it in te future. If you would like more information, dont hesitate to contact us.

Geovisualization.net – Quick and short range GIS analysis on the fly!


Verdelhos, Portugal 20220807

Podemos hacer un seguimiento casi en directo. Seguimiento de Incendios activos desde la plataforma Sentinel Hub EO Browser de Copernicus. Podemos medir las zonas quemadas regularmente, creando vídeos, capturando imágenes, creando índices, histogramas, incluir la METEO en el análisis, etc. Todo para entender mejor lo que está pasando y quizás, por qué no, evitarlo en el futuro. Si desea más información, no dude en ponerse en contacto con nosotros.

Geovisualization.net – Análisis SIG rápido y de corto alcance sobre la marcha!
———————————————–

Nous pouvons en faire le suivi en ce moment même. Surveillance active des incendies à partir de la plateforme de navigation EO Sentinel Hub de Copernicus. Nous pouvons mesurer régulièrement les zones brûlées, créer des vidéos, capturer des images, créer des index, des histogrammes, inclure la METEO dans l’analyse, etc. Tout pour mieux comprendre ce qui se passe et peut-être, pourquoi pas, l’éviter à l’avenir. Si vous souhaitez plus d’informations, n’hésitez pas à nous contacter.

Geovisualization.net – Analyse SIG rapide et à court terme à la volée !
———————————————–

Measuring moisture: Normalized Difference Moisture Index (NDMI) Sentinel-2 2022

I am here in front of my desktop and I wonder how to take advantage of Sentinel Hub for tracking up moisture in a random plot anywhere in the world. Has it been irrigated at the right time for the type of crop? How has rainfall been affected over the analysis time? How has the use of certain fertilisers affected it? Has it positively affected production?. Let’s take a closer look.

The NDMI is a normalized difference moisture index, that uses NIR and SWIR bands to display moisture. The SWIR band reflects changes in both the vegetation water content and the spongy mesophyll structure in vegetation canopies, while the NIR reflectance is affected by leaf internal structure and leaf dry matter content but not by water content. The combination of the NIR with the SWIR removes variations induced by leaf internal structure and leaf dry matter content, improving the accuracy in retrieving the vegetation water content. The amount of water available in the internal leaf structure largely controls the spectral reflectance in the SWIR interval of the electromagnetic spectrum. SWIR reflectance is therefore negatively related to leaf water content. In short, NDMI is used to monitor changes in water content of leaves, and was proposed by Gao. NDWI is computed using the near infrared (NIR) and the short wave infrared (SWIR) reflectance’s:

Sentinel-2 NDMI = (B08 – B11) / (B08 + B11)

Note: NDWI index is often used synonymously with the NDMI index, often using NIR-SWIR combination as one of the two options. Gao, referenced above, also called the index NDWI. NDMI seems to be consistently described using NIR-SWIR combination. As the indices with these two combinations work very differently, with NIR-SWIR highlighting differences in water content of leaves, and GREEN-NIR highlighting differences in water content of water bodies, we have decided to separate the indices on our repository as NDMI using NIR-SWIR, and NDWI using GREEN-NIR.

NDMI

The normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).

These are the bands we can use in Sentinel 2 platform:

This is the random plot I have chosen, latitude 41.71715 longitude -5.22810, somewhere in Valladolid (Spain). Does anybody know why did I choose this province? Well, I got my degree in this city, back in 1998. Time flies !.

Choosing a random property anywhere in Valladolid, Spain

Water stress over certain period (5 months from November to April 2022).

Statistical info. When is this property showing water stress throughout time

This is the histogram covering the whole of the area of analysis.

Histogram

And a sequence from June 2021 to June 2022, covering all seasons of the year.

Hope you find it interesting. If you need me to further develope it, a quote or simply say hello, you can contact me anytime,

Alberto
GIS analyst

Sources:
https://gisgeography.com/sentinel-2-bands-combinations/
https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/
https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2007GL031021#:~:text=%5B9%5D%20In%20order%20to%20show,and%20vegetation%20have%20been%20investigated.
https://www.sciencedirect.com/science/article/pii/S221209472100027X
https://apps.sentinel-hub.com/

Ship Monitoring from space: SUEZ

Below you can see the example timelapse of Suez Canal ship traffic, made with a Sentinel-1 custom composite visualization. You can observe the moving ships as time passes.

Sentinel-1 is a pair of European radar imaging (SAR) satellites launched in 2014 and 2016. Its 6 days revisit cycle and ability to observe through clouds makes it perfect for sea and land monitoring, emergency response due to environmental disasters, and economic applications. This dataset represents the global Sentinel-1 GRD archive, from beginning to the present, converted to cloud-optimized GeoTIFF format.

Sentinel-1 AWS-IW-VVVH: SAR urban

The Interferometric Wide (IW) swath mode is the main acquisition mode over land and satisfies the majority of service requirements. It acquires data with a 250 km swath at 5 m by 20 m spatial resolution (single look). IW mode captures three sub-swaths using Terrain Observation with Progressive Scans SAR (TOPSAR). With the TOPSAR technique, in addition to steering the beam in range as in ScanSAR, the beam is also electronically steered from backward to forward in the azimuth direction for each burst, avoiding scalloping and resulting in homogeneous image quality throughout the swath.

TOPSAR Sub-Swath Acquisition

TOPSAR mode replaces the conventional ScanSAR mode, achieving the same coverage and resolution as ScanSAR, but with a nearly uniform Signal-to-Noise Ratio and Distributed Target Ambiguity Ratio.

The azimuth resolution is reduced compared to SM due to the shorter target illumination time of the burst. Using the sweeping azimuth pattern, each target is seen under the same antenna pattern, independently from its azimuth position in the burst image. By shrinking the azimuth antenna pattern, as seen by a target on the ground, scalloping effects on the image can be reduced. Bursts are synchronised from pass to pass to ensure the alignment of interferometric pairs.

IW SLC products contain one image per sub-swath and one per polarisation channel, for a total of three (single polarisation) or six (dual polarisation) images in an IW product.

Each sub-swath image consists of a series of bursts, where each burst has been processed as a separate SLC image. The individually focused complex burst images are included, in azimuth-time order, into a single sub-swath image with black-fill demarcation in between. There is sufficient overlap between adjacent bursts and between sub-swaths to ensure continuous coverage of the ground as provided in GRD products. The images for all bursts in all sub-swaths are resampled to a common pixel spacing grid in range and azimuth while preserving the phase information.

IW SLC Bursts andSub-Swaths

After burst and sub-swath merging, the full product can be created, as is the case for the GRDH products shown in Figure 3. The TOPSAR technique greatly reduces scalloping effects over conventional ScanSAR.

IW GRDH Product

Sentinel-1 uses wide area coverage with improved revisit times and is able to potentially detect smaller ships than Envisat’s ASAR instrument. The mission’s ability to observe in all weather and in day or night time, makes it ideal for precise cueing and location of ship activities at sea, allowing for more efficient and cost-effective use of other security assets, such as patrol aircraft and ships. Data relevant to ship detection are transmitted by the satellite in real-time for reception by local collaborative ground stations supporting European and national services.

Illustration of vessels observed between Gibraltar and Algesiras on September 2017, Copernicus Sentinel Data [2017]
Classical ship wake pattern on SAR imagery (S-1A Stripmap, Copernicus Data [2015]) with a local wind speed of 2 m/s. A/ Kelvin “V” wave dark and bright on each side, B/ Narrow “V” wave, D/ Turbulent wave

Some other interesting stuff you can do:

Ice Monitoring
Oil polution Monitoring
Marine Winds
Etc…

https://en.wikipedia.org/wiki/Suez_Canal
https://www.sentinel-hub.com/explore/eobrowser/
https://custom-scripts.sentinel-hub.com/#sentinel-1
https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-1-sar/acquisition-modes
https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-1-sar/acquisition-modes/interferometric-wide-swath
https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-1-sar/applications/maritime-monitoring
https://www.youtube.com/watch?v=XQe_petggwA&ab_channel=SaankhyaLabs


Recuperación natural de un bosque de Pino Piñonero. Incendio en Pedro Bernado (Ávila)

En este micro vídeo he querido mostrar el proceso de recuperación natural de un bosque de Pino Piñonero (Pinus Pinea) a lo largo de tres años en Pedro Bernardo (Ávila, España). Fecha del incendio Junio 2019. Estimación previa a cuantificación: 40%. Imagenes Sentinel 2 – L2A. Usé una combinación 12-11-4. SWIR-2 (12), SWIR 1 (11), Red (4) entre las fechas de Junio 2019 y Junio 2022 a razón de una imagen al mes, seleccionando solo si ausencia total de nubes y cobertura total sobre la zona de análisis.

Si bien esta especie se ha podido recuperar con mayor prontitud por su resilencia frente al fuego volviendo a brotar con vigor después del incendio, a partir de ese momento se deben poner en marcha otras medidas para favorecer la restauración de las masas forestales mediante la siguientes actuaciones urgentes:

*retirada de pies muertos y eliminación de restos vegetales,
*ejecución de construcciones sencillas, como empalizadas, con materiales del territorio como ramas y troncos para la lucha contra la erosión,
*tratamientos preventivos frente a posibles plagas, ayudas a la regeneración natural e incluso
*acudir a la repoblación forestal, en su caso,
*mejora y construcción de nuevas infraestructuras para la prevención y lucha contra incendios forestales,
*siembras aéreas en áreas de difícil acceso,
*construcción de refugios provisionales para la fauna y otaderos artificiales, etc.

Espero que os resulte interesante,

Alberto CONCEJAL
GIS Analyst

Fuente: http://www.lamagiadelosbosques.com/?page_id=566
https://es.wikipedia.org/wiki/Pinus_pineaÇ
https://apps.sentinel-hub.com/eo-browser/

Sentinel-hub: Highlighting value differences in deserts! (not only!)

This Sentinel-2 aesthetic script can be used to produce beautiful, neon looking results over urban and dry areas. The script is especially useful to highlight value differences in deserts. It’s essentially an RGB composite, with a B12 and B04 difference (which does a good job at displaying certain desert features, like dunes) in the red, B03 in the green, and B02 in the blue channel. Gain and gamma in the script can be modified to fit the location best; for gamma, -0.55 to -0.95 is recommended, while for gain, 2.3 fits most locations. Neon imagery of Beijing, China. Acquired on 2020-01-23.

Neon imagery of Beijing, China. Acquired on 2020-01-23.

https://custom-scripts.sentinel-hub.com/
https://www.sentinel-hub.com/explore/education/custom-scripts-tutorial/

/https://www.youtube.com/watch?v=02Xbbu1PHdg&ab_channel=SentinelHub/
/https://www.youtube.com/watch?v=cgAH2beNYoU&ab_channel=SentinelHub/

Marathon à Nantes 2022: CHECKED!

J’ai toujours cru que faire les choses selon un plan facilitait la réalisation de ce que vous aviez prévu de faire, eh bien, quand il s’agit de courir un Marathon, c’est la clé. Lors de mon troisième Marathon après Valencia en 2017 et Madrid en 2019, j’ai voulu courir a Nantes, ma chérie Nantes, où j’ai vécu certains des moments les plus importants de ma vie…

La première chose, on constate une meilleure tendance dans le nombre de kilomètres de préparation. Attention l’hécatombe du 3ème et 4ème mois à Valencia 2017…

Comme dans presque toutes mes courses, je commence avec un rythme inférieur à la façon dont je finis. Au “semi-marathon de Latina” et au “semi-marathon de Madrid” (il y a cinq et deux semaines respectivement), j’ai fait la même chose, la première moitié lentement, la deuxième moitié a fond !

En plus des trois jours de running par semaine, une séance cardio, un peu de stretching et physiothérapie toutes les deux semaines (merci Sandra !), et surtout le soutien de mes chers amis Hicham, Audrey et leur enfants N et I:-) Vous me manquez déjà ! La prise de gels et de nourriture était super systématique, la prise d’eau tous les 5km, la même chose, et quelques deux arrêts pour faire pipi aussi 🙂 C’est comme ça que j’ai réussi à ne commencer à marcher qu’au km 40 ou presque (au hangar a bananes), et pas plus de quelques metres…

Voilà ces photos du Marathon… Je peux vraiment dire que j’ai super bien profité de cette course du début à la fin. Après 2 ans et demi que j’avais signé, je voulais déjà en finir avec ce sourire et tellement content !!!

Saliendo del Jardin de plantes. KM30
Llegada a Meta!

Et voilà l’hommage à ma petite H: Ça fait 9 ans que je t’ai promis que je courrais ce Marathon à un moment donné… ici tu as ma promesse remplie mon amour.

Alberto

Analyste SIG, père fier et homme d’énergie exultant jusqu’au km 42,195 🙂

Bombas en Mariupol, Ucrania. Una ciudad como la tuya y la mía

Aquello que no podemos apreciar en una imagen de color natural 4-3-2 en Sentinel-2 nada más tenemos que cambiarlo a un falso color 12-11-4 para exponer las explosiones y bombas, en definitiva, la muerte. En este caso lo falso es la verdad (las bombas) y y lo que parece natural, es falso (la ausencia de ellas).

Sentinel 2 Composición Color Natural 4-3-2 2022-03-29

Cada una de estas explosiones es el llanto de una familia por generaciones. La pena por una pérdida irreparable. La humillación inmerecida. La muerte.

Misma imagen Sentinel 2 Composición Falso Color 12-11-4 2022-03-29

Muerte en unos cuantos pixeles

Hace tan solo unos días estas casas estaban en su sitio, en ellas había electricidad y se podía desarrollar la vida pero alguien unilateralmente decidió que no era una buena idea…

Imágenes al azar tomadas de Google Earth con fecha 20220411
Google Earth 20220411

Hace tan solo unos días en esta ciudad de tamaño algo mayor que Valladolid, había vida. la gente iba a trabajar y volvía a casa a hacer la cena, jugar con los niños, hacer planes, vivir. Hace unos pocos menos, empezó la guerra y unos pocos escaparon como pudieron pero otros se quedaron encerrados (incumplimiento de los alto el fuego y falta de respeto a los corredores humanitarios) y se acabó la calefacción, la electricidad, las comunicaciones, la comida y la bebida. Las vidas de sus vecinos y por extensión, nuestras vidas.

Pavel Klimov. Reuters

Hoy no queda más que la esperanza de que esta cerrazón cese de una vez. Cuanto antes. Y que no vuelva a ocurrir nunca. Ah, y que paguen los responsables. Qué iluso soy.

Alberto
Analista SIG e iluso

Fuentes:
https://www.google.com/intl/es/earth/
https://mappinggis.com/2019/05/combinaciones-de-bandas-en-imagenes-de-satelite-landsat-y-sentinel/
https://apps.sentinel-hub.com/eo-browser/
https://es.wikipedia.org/wiki/Sitio_de_Mari%C3%BApol_(2022)
https://es.wikipedia.org/wiki/Cr%C3%ADmenes_de_guerra_en_la_invasi%C3%B3n_rusa_de_Ucrania_de_2022
https://web.archive.org/web/20220309131213/https://www.cnbc.com/2022/03/07/russia-ukraine-war-us-collecting-evidence-of-possible-war-crimes-nbc-reports.html