Geodata Analysis

Satellite Photography of Montenegro

  • Satellite photography with Sentinel2
  • Agriculture layer with contours
  • Processed with sentinel-hub and QGIS

copernicus-sentinel2-Montenegro-April2020

Satellite Photography of Egypt, Abu Simbel

  • Satellite photography with Sentinel2
  • False Color Spectrum (Vegetation)
  • Processed with sentinel-hub and QGIS
  • Red colour: agricultural area, black colour: Nile

Sentinel-2L1C_FALSE_COLOR_Vegetaition_AbuSimbelEgypt.jpeg

Satellite Photography of Genoa, Italy (2019)

  • Satellite photography with Sentinel2
  • Processed with sentinel-hub and QGIS

genua_geology

  • NDVI – Normalized Difference Vegetation Index

copernicus_genua_ndvi

Satellite Photography of the Island of Elba, Italy

  • #Italy Isand of #Elba – An animation of the Normalized Difference Vegetation Index NDVI in 2019 – #satellite #photography with #Sentinel2
    @CopernicusEU

    Processed with sentinel-hub and #QGIS

  • NDVI – Normalized Difference Vegetation Index

bbc6dv7j

This most known and used vegetation index is a simple, but effective VI for quantifying green vegetation. It normalizes green leaf scattering in the Near Infra-red wavelength and chlorophyll absorption in the red wavelength.Values description: The value range of an NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1 to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).

NDVI

Island of Elba, Italy

False Color – Vegetation

Elba-FalseColorVegetation-12122019

Agriculture Layer

Elba-Agriculture-26122019

Moisture Soil Index (Information about the index: see below)

elba-moisture-index2

Moisture Soil Index and Contour

elba_MoistureIndex_Contour

Satellite Photography of Livorno, Italy (2020)

Moisture Soil Index

  • Data from ESA Copernicus Sentinel-2 satellite
  • Access: Sentinel Hub
  • Processed with QGIS
  • Information about the index: see below

Sentinel-2L1C_MOISTURE_INDEX_2020-01-02_2020-03-04_1136899.57_5393620.6_1147469.41_5397755.58_21_mostRecent

Satellite Photography of Corse, Elba and Piombino,
Italy (2020)

Moisture Soil Index

  • Data from ESA Copernicus Sentinel-2 satellite
  • Access: Sentinel Hub
  • Processed with QGIS

Moistureindex_Contour_Corse_Elba_Toskana_Piombiono

Satellite Photography of Venice, Italy (2020)

Moisture Soil Index

Sentinel2MoistureIndexVenice2020

Satellite Photography Island of Crete, Greece (2020)

Moisture Soil Index and Contours

  • Data from ESA Copernicus Sentinel-2 satellite and British Oceanographic Data Centre (BODC)
  • Access: Sentinel Hub
  • Processed with QGIS

GreeceCretaMoistureIndex2020

Satellite Photography of Island of Malta

Moisture Soil Index and Contours

Moistureindex_Contour_Malta_2020

Satellite Photography of Island of Krk (Croatia)

False Colour (Vegetation) and and Contours

FalseColorVegetationSentinel2

 

Agriculture Layer and Contours

Krk_Sentinel_2_AgricultureLayer_Contours

Satellite Photography of Rijeka, Croatia

Moisture Soil Index and Contours

Rijeka-Croatia_moistureindex_contour

Satellite Photography of the German North Sea Coast

Moisture Soil Index

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.

MoistureIndex

  • Data from ESA Copernicus Sentinel-2 satellite
  • Access: Sentinel Hub
  • Proceeded with QGIS
  • September 2019

NorthSea_MoisureIndex_Dez2019

 

NDVI – Normalized Difference Vegetation Index

Nordsee_NDVI-20122019

NDVI

Agriculture Layer

  • February 2020

copernicus-sentinel-ndvi

Isle of Spiekeroog (February 2020) – Moisture Soil Index

  • Data from ESA Copernicus Sentinel-2 satellite
  • Access: Sentinel Hub
  • Proceeded with QGIS
  • January 2020

Sentinel-2L1C_MOISTURE_INDEX_2019-09-13_2019-10-30_53.749826_7.655814_53.78866_7.823769_20_leastCC

 

False Color Vegetation

  • January 2020

Sentinel-2L1C_FALSE_COLOR_2019-12-12_2020-02-05_852241.37_7122917.93_870937.98_7130232.15_22_mostRecent.png

 

Isle of Wangeroog – False Color (Vegetation with contours)

WangeroogeFalseColorVegetationSentinel2

Isle of Wangerooge Moisture Soil Index with contour

Sentinel2MoistureIndexWangeroogContour

 

Island of Sylt (March 2020) – Agriculture Layer

Sylt_Agriculture

Island of Sylt (March 2020) – NDVI – Normalized Difference Vegetation Index

Sylt_NDVI

Island of Sylt (March 2020) Moisture Soil Index

moistureindex_sylt

Island of Sylt (March 2020) – Agriculture Layer

Sylt-FalseColorVegetation

 

 

World map of tweets about #DataScience (2019/2020)

 

HTDataScience2019-2020

Germany – Contours and Rivers

(Made with geoinformation system QGIS 3.5)

DeutschlandGewaesserHoehen

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German North See Coast – Contours

 

NorthSea

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Isle of Elba / Italy – Contours

elba3

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Distribution of Tweets with Hashtag #climatechanges

(December 2019)

HTclimatechangeDez2019

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Contours of the Mediteranean Sea (land and sea)

MittelmeerGesamtNeu2

 

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Germany – Real disposable income

(yellow – high income until red – low income)

PLZ_Kaufkraft_2

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Kaufkraft_2

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Geocoding of the French Atlanic Coast – South of Bordeaux

 

20190905-bisca-pilat2

 

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NDVI

Der Index beruht auf der Tatsache, dass gesunde Vegetation im roten Bereich des sichtbaren Spektralbereichs (Wellenlänge von etwa 600 bis 700 nm) relativ wenig und im darauf folgenden nahen Infrarot-Bereich (Wellenlänge von etwa 700 bis 1300 nm) relativ viel Strahlung reflektiert. Die Reflexion im nahen Infrarot Bereich ist auf die Zellstruktur der Blätter zurückzuführen und wird hauptsächlich durch die Mesophyll-Zellen bestimmt. Je vitaler (grüner) die Pflanze, desto größer ist der Anstieg des Reflexionsgrades in diesem Spektralbereich. Andere Oberflächenmaterialien, wie Boden, Fels oder auch tote Vegetation, zeigen keinen solchen kennzeichnenden Unterschied des Reflexionsgrades beider Bereiche. Dieser Umstand kann dazu dienen, zum einen vegetationsbedeckte von vegetationsfreien Flächen zu unterscheiden.

Man berechnet den NDVI aus den Reflexionswerten im nahen Infrarotbereich und des roten sichtbaren Bereichs (rot, etwa 620 bis 700 nm) des Lichtspektrums:

 

Bei starken atmosphärischen Störungen (dichte Bewölkung) wird zum Teil mit einer Näherungsformel gerechnet:

  

Dabei werden beide Kanäle um einen Spektralbereich verschoben: das nahe Infrarot zum mittleren Infrarot (etwa 1300 bis 3000 nm) und der rote Bereich zum nahen Infrarot.

Durch die Normierung ergibt sich ein Wertebereich zwischen −1 und +1. Negative Werte bezeichnen Wasserflächen. Ein Wert zwischen 0 und 0.2 entspricht nahezu vegetationsfreien Flächen, während ein Wert nahe 1 auf eine hohe Vegetationsbedeckung mit grünen

Live green plants absorb solar radiation in the photosynthetically active radiation (PAR) spectral region, which they use as a source of energy in the process of photosynthesis. Leaf cells have also evolved to re-emit solar radiation in the near-infrared spectral region (which carries approximately half of the total incoming solar energy), because the photon energy at wavelengths longer than about 700 nanometers is too small to synthesize organic molecules. A strong absorption at these wavelengths would only result in overheating the plant and possibly damaging the tissues. Hence, live green plants appear relatively dark in the PAR and relatively bright in the near-infrared.
By contrast, clouds and snow tend to be rather bright in the red (as well as other visible wavelengths) and quite dark in the near-infrared. The pigment in plant leaves, chlorophyll, strongly absorbs visible light (from 0.4 to 0.7 µm) for use in photosynthesis. The cell structure of the leaves, on the other hand, strongly reflects near-infrared light (from 0.7 to 1.1 µm). The more leaves a plant has, the more these wavelengths of light are affected, respectively. Since early instruments of Earth Observation, such as NASA’s ERTS and NOAA’s AVHRR, acquired data in visible and near-infrared, it was natural to exploit the strong differences in plant reflectance to determine their spatial distribution in these satellite images.

The NDVI is calculated from these individual measurements as follows:

where red and NIR stand for the spectral reflectance measurements acquired in the red (visible) and near-infrared regions, respectively. These spectral reflectances are themselves ratios of the reflected over the incoming radiation in each spectral band individually, hence they take on values between 0.0 and 1.0. By design, the NDVI itself thus varies between -1.0 and +1.0. NDVI is functionally, but not linearly, equivalent to the simple infrared/red ratio (NIR/VIS). The advantage of NDVI over a simple infrared/red ratio is therefore generally limited to any possible linearity of its functional relationship with vegetation properties (e.g. biomass). The simple ratio (unlike NDVI) is always positive, which may have practical advantages, but it also has a mathematically infinite range (0 to infinity), which can be a practical disadvantage as compared to NDVI. Also in this regard, note that the VIS term in the numerator of NDVI only scales the result, thereby creating negative values. NDVI is functionally and linearly equivalent to the ratio NIR / (NIR+VIS), which ranges from 0 to 1 and is thus never negative nor limitless in range. But the most important concept in the understanding of the NDVI algebraic formula is that, despite its name, it is a transformation of a spectral ratio (NIR/VIS), and it has no functional relationship to a spectral difference (NIR-VIS).

In general, if there is much more reflected radiation in near-infrared wavelengths than in visible wavelengths, then the vegetation in that pixel is likely to be dense and may contain some type of forest. Subsequent work has shown that the NDVI is directly related to the photosynthetic capacity and hence energy absorption of plant canopies.
Although the index admits to go from -1 to 1, even in more densely populated urban areas the value of normal NDVI is positive, although closer to zero. Negative values are more likely to be disturbed in the atmosphere and some specific materials.