FUSION User Workshop

Transcription

1 FUSION User Workshop FUSION High Resolution Land Monitoring by Fusion of Optical and Infrared Data Normal temperature applications such as agricultural monitoring Lilit Kocharyan, RapidEye AG Gefördert durch die Bundesrepublik Deutschland Zuwendungsgeber: Raumfahrt-Agentur des Deutschen Zentrums für Luft- und Raumfahrt e.v. mit Mitteln des Bundesministeriums für Wirtschaft und Technologie aufgrund eines Beschlusses des Deutschen Bundestags unter dem Förderkennzeichen 50 EE 0929

2 Content FUSION System Concept RapidEye and FUSION constellations RapidEye Applications Multi-sensor Multi-resolution Technique (MMT) FUSION Applications New Earth Observation products 2

3 FUSION System Concept FUSION system will combine capabilities of the RapidEye system with the ones of the BIRD mission VIS/NIR sensor system: Based on the RapidEye heritage FUSION will carry a payload for the visible spectrum with a Ground Sampling Distance (GSD) of 6.5 m MIR/TIR sensor system: FUSION will be equipped with an infrared payload delivering thermal imagery at 100 m GSD similar to BIRD sensor BIRD system and capabilities will be presented by Dr. Oertel FUSION constellation will consist of four to five LEO satellites RapidEye system and capabilities are presented in the next slides That will enable daily return to any point on Earth Fusion of moderate resolution MIR/TIR with high resolution VIS/NIR data will result in new and enhanced data products 3

4 RapidEye System RapidEye AG is a Geo-information provider of satellite imagery and management solutions RapidEye owns and operates a constellation of five identical Earth Observation satellites Capacity to image more than 4 million km² of Earth daily Return to any point on Earth daily Deliver products with 5 meter pixel spacing Available in five spectral bands Large area, multi-temporal, countrywide coverage 4

5 RapidEye 1-5 Orbit Characteristics RapidEye 1 RapidEye 1 RapidEye 5 RapidEye 5 RapidEye 2 RapidEye 2 RapidEye 2 RapidEye 4 RapidEye 4 RapidEye 3 RapidEye 4 Sun synchronous orbit RapidEye RapidEye 33 Equally spaced in one orbital plane The FUSION system will have similar constellation and orbital characteristics 5

6 RapidEye System Specifications Spectral Bands Satellite Altitude Revisit Time Pixel Size (orthorectified) Blue Green Red Red Edge NIR nm nm nm nm nm 630 km Daily for single areas without consideration of clouds 5m Image Capture Capacity 4 Million km² daily Swath Width / Length 77 km x 1500 km 25 x 25 km² Tile Size Radiometry 12 bit 6

7 RapidEye Standard Image Products Zoom Levels Washington, DC USA Acquired by MATI RapidEye 2 April 26,

8 RapidEye Spectral Bands The RapidEye System can collect imagery in five spectral bands Blue: Green: Red: Red Edge: NIR: µm µm µm µm µm Plants have spectral signatures which can be identified with those 5 bands The combination of these five spectral bands is ideally suited to monitor vegetation conditions and to detect growth anomalies 8

9 FUSION Spectral Bands The FUSION System can collect imagery in seven spectral bands Blue: Green: Red: RE: NIR: MIR: TIR1: Optional: µm µm µm µm µm µm µm TIR2: µm Surface temperatures of land and water can be estimated, which enables observation of normal temperature phenomena such as heat and water budgets, thermal pollution of water bodies etc. IR bands allow measurements at day and night. Influence of air humidity is minimised since absorption bands are avoided. 9

10 RapidEye Core Capabilities Land Cover Analysis & Vegetation Identification Monitoring & Change Detection Damage Assessment Feature & Boundary Extraction Modelling & Production Estimation 10

11 RapidEye Applications Agriculture e.g. ground cover index maps Energy & Infrastructure e.g. pipeline monitoring Forestry Spatial Solutions e.g. stem volume estimation e.g. land cover analysis Security & Emergency Environment e.g. planning support after fires e.g. water quality monitoring 11

12 RapidEye's Competencies in Agriculture Protein content of crop wheat corn Crop Identification rapeseed What is growing and how? where... 12

13 Agriculture RapidEye Agricultural Solutions Agro Intelligence Crop Identification Example: Pilot Project for Sugar Cane Identification in Central America Identification of selected crop types within specific areas 13

14 Agriculture RapidEye Agricultural Solutions Precision Farming Services Up-to-date crop Information for better production management and monitoring of agricultural areas through: Ground cover index maps Chlorophyll content and nitrogen status (relative) Soil brightness maps Illustration: Ground Cover Index maps derived from RapidEye's multi-temporal satellite imagery Examples see next slides Delivery is four to six times during specified time period (growing season) as operational service 14

15 Agriculture RapidEye Agricultural Solutions Precision Farming Services Up-to-date crop Information for better production management and monitoring of agricultural areas through: Ground cover index maps Chlorophyll content and nitrogen status (relative) Soil brightness maps Illustration: Ground Cover Index maps derived from RapidEye's multi-temporal satellite imagery Examples see next slides Delivery is four to six times during specified time period (growing season) as operational service 15

16 Agriculture RapidEye Agricultural Solutions Precision Farming Services Up-to-date crop Information for better production management and monitoring of agricultural areas through: Ground cover index maps Chlorophyll content and nitrogen status (relative) Soil brightness maps Illustration: Ground Cover Index maps derived from RapidEye's multi-temporal satellite imagery Examples see next slides Delivery is four to six times during specified time period (growing season) as operational service 16

17 Agriculture RapidEye Agricultural Solutions Precision Farming Services Up-to-date crop Information for better production management and monitoring of agricultural areas through: Ground cover index maps Chlorophyll content and nitrogen status (relative) Soil brightness maps Illustration: Ground Cover Index maps derived from RapidEye's multi-temporal satellite imagery Examples see next slides Delivery is four to six times during specified time period (growing season) as operational service 17

18 RapidEye Agricultural Solutions Ground Cover Maps For Crop Status Assessment Date: 05/25/06 Crop: Winter Wheat Farmer: W. Mueller Field: Ringsaat Ground CropCover: Type:79.8% STD: 4.9 Corn Ground Cover: 79.8 Ground Cover < Values % > Field: Am Fließ Barley Crop Type: Ground Cover: 60.3% Ground STD: 5.4 Cover: 60.7 Duenenacker CropField: Type: Wheat Ground Cover: 70.6% Ground Cover: 72.4 STD: Ground Cover [%] with the highest green leaf area are shown in blue. Brown areas have the lowest green leaf area. The graph below shows the status of ave- age ground cover of each field over time.ground It Status of the average can be used to quickly determine whether and when aof fieldeach starts to deviate the time. cover fieldform over average trajectory or to make comparisons across years. Used to determine deviations from the average trajectory Winter Wheat : Crop Status on 05/25/06 90 Field:Waldkante Crop Type: Wheat Ground Cover: 65.4% Ground STD: 2.2Cover: 65.3 Status of relative Ground Cover Status of Ground Cover (fraction soil ground covered The map on theof left shows cover, by green i.e., the fraction of soil that is covered by leaves when seen from above) green leaves when seen from above. Areas 70 Duenenacker Ringsaat Am Fließ Waldkante Baseline Mar 30-Mar 9-Apr 19-Apr 29-Apr 9-May 19-May 29-May 18

19 RapidEye Agricultural Solutions Regional Monitoring of Relative Chlorophyll Content and N-Status Relative chlorophyll content is available as pre-operational service Absolute N content requires additional Ground Truth and is currently still not available as preoperational service (Pilot projects only) 19

20 RapidEye Agricultural Solutions Bare Soil Map / Soil Brightness Map RapidEye provides soil brightness or true color map acquired at time of bare soil Displays differences in soil characteristics within a field, e.g. soil color, organic matter and texture Can be used as one of the input layers to create management zones Provides general decision support for locating soil sample spots 20

21 RapidEye Forestry Solutions Forest Classification and Mapping Multi-date RapidEye imagery is used to produce accurate forest maps Example: Multi-temporal Forest Classification in Serbia/Kosovo Early spring Summer Results can be overlaid with additional data, such as coordinate-grids, road layers or elevation data or simply be integrated into a GIS database 21

22 Forest Health Mapping Example: Mapping of Insect damages Classification Ips typographus (Bark Beetle) RapidEye image Dying and dead trees can be mapped with accuracies between 75% and 90%, depending on forest structure. healthy Fusion - User Workshop. damaged 22

23 RapidEye Forestry Solutions Forest Inventory Support Species Separation Legend for Tree Species in % Tree Species Spruce & Fir Species Separation is a key ingredient in managing forest inventories RapidEye has done pilot projects to assess large and remote areas with very little ground truth information is available Standing Stock Spruce and Fir Volume Estimation RapidEye has done pilot projects to provide estimations about where the largest volume of wood can be harvested, so Forest Managers can provide logging concessions m³ / ha m³ / ha m³ / ha m³ / ha >400 m³ / ha 23

24 RapidEye Forestry Solutions Semi-automatic storm damage mapping service Classification of storm damage areas, to determine the extent of losses Storm damage assessment is based on a change detection approach using RapidEye images before and after the storm event RapidEye can detect storm damages with sufficient accuracy for areas from 0.2/0.3 ha, or greater 90 Comparison Average Accuracy and Standard Deviation of Accuracy per Area Class % Accuracy 50 OverallAcc% STDEV (OverallAcc%) Scale 1: Meter Meter 0 to <= to <= to <= to <= to <= to <= to <= to <=0.4 Area Classes (hectare) Determination of the Minimum Mapping Unit 24

25 RapidEye Spatial Solutions Generation of Large Area Mosaics RapidEye can generate seamless mosaics based on large area image acquisition Geometric ortho-correction and final color adjustment Delivery within adjustable (map) units, e.g. 100 x 100 km² The geometric accuracy depends on the geo-rectification quality and therefore mainly on the GCP accuracy Example: Mosaic of Germany Overall Size: 65 Gb Less than 2 % remaining clouds Semi-automatic processing Offered as a service in addition to the standard data product delivery 25

26 RapidEye Solutions for Environment Change Detection (Pilot Project) Critical Changes* Uncritical Changes* *As identified by the customer Images taken in intervals can be analysed for environmental factors that are of interest to the customer Areas effected by a forest fire, soil erosion or flooding may be indicated in a change detection map 26

27 RapidEye Security & Emergency Solutions Disaster Management RapidEye can assist agencies after a man-made or natural disaster events Tornadoes/Hurricanes Drought Floods Landslides Hail Fires Earthquakes Chile before the earthquake by RapidEye-5 on Chile after the earthquake by RapidEye-3 on Change detection: Earthquake effected area marked in red 27

28 RapidEye Energy & Infrastructure Solutions Illegal Settlements The presence of settlements inside a certain corridor prevents optimal use of the pipelines The illegal settlers are a danger for the pipeline itself. Tilling or construction activities can lead directly to pipeline damages. 28

29 FUSION User Workshop FUSION High Resolution Land Monitoring by Fusion of Optical and Infrared Data Multi-sensor Multi-resolution Technique Dr. B. Zhukov Space Research Institute (IKI) of Russian Academy of Sciences, Moscow Dr. D. Oertel Deutsches Zentrum f. Luft- und Raumfahrt, Optische Informationssysteme (DLR-OS) & Astro- und Feinwerktechnik Adlershof GmbH (Astrofein), Berlin 29

30 Multi-sensor Multi-resolution Technique (B. Zhukov et al.) Measuring Instrument (HSI) data Classifying Instrument (CI) data 30

31 MMT Illustration Example 1: Fusion of Thematic Mapper Thermal Data (and comparison with an image from DAIS-7915) Sensor Spectral bands, μm Spatial resol. Thematic Mapper (Landsat) VNIR: , , , SWIR: , TIR: m 30 m 120 m Digital Airborne Imaging Spectrometer DAIS-7915 VNIR: 32 bands in the range of m with the bandwidth of m SWIR: 40 bands in the range of m with the bandwidth of m MIR: 3-5 TIR: , , , , , m 6m 6m 6m 31

32 MMT step 1: Classification using Thematic Mapper VNIR / SWIR data One of six high-resolution TM bands used for classification at μm Classification map (30 m resolution) with 16 classes 32

33 Comparison of original Thematic Mapper thermal image with the DAIS7915 reference image Original TM thermal image (120 m) Reference thermal image (30 m) 33

34 Comparison of the MMT-fused Thematic Mapper thermal image with the DAIS 7915 thermal reference image MMT- fused TM thermal image (30 m) Reference thermal image (30 m) 34

35 MMT Illustration Example 2: Fusion of ASTER Images of Ruqigou area, China (8 August 2003) Spectral bands, μm Original CI / MI resolution resolution Classifying Instrument VNIR: , , m 15 m Measuring Instrument ( HSI ) SWIR: , , , , , m 90 m TIR: , , , , m 90 m In order to simplify the illustration, the SWIR pixels are aggregated to 90 m and a common unmixing of the SWIR and TIR bands is performed. In practice, the SWIR and TIR bands should be unmixed separately using their original resolution 35

36 Initial VNIR and SWIR-TIR false color images VNIR image (15 m) R: μm, G: μm, B: μm SWIR-TIR image (90 m) R: μm, G: μm, B: μm 36

37 MMT step 1: VNIR image classification VNIR image (15 m): R: μm, G: μm, B: μm Classification map (15 m resolution) with 32 classes 37

38 False color IR images: Initial (left) and MMT result (ríght) Initial HSI image (90 m resolution) R: μm, G: μm, B: μm Fused HSI image (15 m resolution) R: μm, G: μm, B: μm 38

39 Potential FUSION Applications - Overview In addition to RapidEye's applications, the fusion of MIR/TIR with VIS/NIR data will result in new Earth Observation data and enable new applications New normal temperature FUSION applications will serve the following markets: Agriculture & Irrigation water management Terrestrial ecosystems and environment Food security Inland water resource management Geology Land planning Sea pollution & fishery Disaster monitoring Security and surveillance High temperature events applications will be presented in the next slot Fires of forests and wild-land Volcanology & geothermy Coal mine fires Industrial hotspots 39

40 FUSION Applications in Agriculture New normal temperature FUSION applications: Agriculture and Irrigation water management Irrigation water management for plant growth and water use optimisation: Water stress detection Irrigation infrastructure monitoring Detecting water leakages Water rights compliance monitoring Estimating and mapping crop residues cover for land conservation and erosion reduction programs Vegetation and soil water content measurements for early warning of draughts IR bands also enable Plant stress detection Determination of the hydrous and energetic state of vegetation Estimation of heat and water budgets from field to regional scales 40

41 FUSION Environmental Applications New normal temperature FUSION applications ctd.: Terrestrial Ecosystems & Environment Carbon sinks and sources (fluxes) estimation through extraction of vegetation parameters (biomass, Leaf Area Index - LAI, Net Primary Productivity - NPP) for Global Change and climate change studies Surface temperatures of land and water can be estimated, which enables observation of normal temperature phenomena such as Heat and water budgets Thermal pollution of rivers, lakes, coastal zones and of flooded areas Characterization of terrestrial ecosystems and their seasonal dynamics Coastal erosion monitoring Determination of bare land surface thermal inertia through day and night time images Will improve the discrimination of bare surface natures and characteristics Peat land state assessment and monitoring (wetness, danger of drought, estimation of peat fire attributes and peat fire carbon emission) 41

42 FUSION Applications New normal temperature FUSION applications ctd.: Food security Crop production quantification, yields forecasting Early warning systems to prevent food shortage, improve decision support tools and optimise the food security activities Inland water resource management Water resource mapping and monitoring for the optimisation of irrigation water management, to reduce or to deal with water shortages Snow cover monitoring for flood prevention Snow cover volume calculations for water supply forecasts for the year 42

43 FUSION Outlook The proposed FUSION mission concept will expand the existing Earth Observation Products by: Near-real time Wildfire attributes and a Fire Zooming Services, as presented in detail in the next presentation Day and night time moderate-to-high resolution data products for normal temperature monitoring and thermal anomaly observation - which will not be provided by Sentinel-2 satellites, EnMap and the planned Landsat Data Continuity Mission (LDCM), due to the fact that these systems will not have Mid IR (MIR, at 4 µm) and Thermal IR (TIR, at 10 µm) spectral bands Innovative day time high resolution data products - fused by MMT for normal temperature monitoring and thermal anomaly observation 43

44 Thank you for your attention. Agricultural area near São Paulo in Brazil RapidEye 4 on November 11, 2008 Fusion - User Workshop. 44