Geodata Analysis

Satellite Photography of the Isle of Elba, Italy

bbc6dv7j

 

 

  • NDVI – Normalized Difference Vegetation IndexThis 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
  • Data from ESA Copernicus Sentinel-2 satellite
  • Access: Sentinel Hub
  • Proceeded with QGIS

Sentinel2-NDVI-Elba-Dez-2019

 

 

  • False Color – Vegetation
  • Data from ESA Copernicus Sentinel-2 satellite
  • Access: Sentinel Hub
  • Proceeded with QGIS

Elba-FalseColorVegetation-12122019

 

 

  • Agriculture Layer
  • Data from ESA Copernicus Sentinel-2 satellite
  • Access: Sentinel Hub
  • Proceeded with QGIS

Elba-Agriculture-26122019

 

Satellite Photography of the German North Sea Coast

  • Moisture Index
    The term calculated from the aridity and humidity indices, as Im = 100 × (SD)/PE, where Im is the moisture index, S is the water surplus, D is the water deficit, and PE is the potential evapotranspiration.moistureIndex2b
  • Data from ESA Copernicus Sentinel-2 satellite
  • Access: Sentinel Hub
  • Proceeded with QGIS

NorthSea_MoisureIndex_Dez2019

 

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

copernicus-sentinel-ndvi

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.