Remote Sensing Tools in Grasslands: Connection to the Larger Landscape?

Transcription

1 Remote Sensing Tools in Grasslands: Connection to the Larger Landscape? Anne Smith Agriculture and Agri-Food Canada Lethbridge Research and Development Centre Transboundary Grasslands Partnership Workshop December 4 th, 2018 Lethbridge, Alberta

2 Landsat 5 TM True Colour Composite July 6,

3 Landsat 5 TM True Colour Composite July 6, 1991 Year Cost Processing time 1991 $ week $0 1 hour 3

4 Remote Sensing MODIS 2014 SPOT Update ~2000 EO ~684 LANDSAT 8 RAPIDEYE RADARSAT CONSETLLATION MISSION SENTINEL QUICKBIRD PLANET LABS FLOCKS/DOVES 4

5 Remote Sensing! the acquisition of data about a physical object without touching it e.g. MODIS, Landsat Sentinel-2, SPOT, RapidEye, Planet Labs Doves.. airborne and drone cameras Passive Active e.g. Radarsat-2, TerraSAR, Sentinel-1 airborne and drone sensors. **RADARSAT Constellation Mission February leadertechnic.com 5

6 Remote Sensing Tools for Grassland Monitoring Three broad categories of remote sensing research in grasslands:! Mapping grassland spatial extent and changes over time,! Estimating productivity! Mapping invasive plant species 6

7 MAPPING SPATIAL EXTENT 7

8 Mapping Grassland Spatial Extent! Agriculture Financial Services Corporation grassland mask! Alberta Native Prairie Baseline (~1993)! Alberta Grassland Vegetation Inventory ( ) 8

9 Mapping Grassland Spatial Extent " Comprehensive biophysical, anthropogenic and land use inventory " Colour infrared digital airborne imagery 0.5 m " Polygon-based, 5.0 ha for upland site types and 1.0 ha for wetland site types. " 335,366 polygons in 1204 townships (110 townships remaining) " Accuracy estimated at >90% " > 8 years to compile ( ) Image courtesy of Barry Adams, Alberta Environment and Sustainable Resource Development 9

10 Mapping Grassland Spatial Extent NEWELL TEST SITE RADARSAT-2 PAULI-RGB July 21, 2009 PAKOWKI TEST SITE RADARSAT-2 PAULI-RGB July 03,

11 Mapping Grassland Spatial Extent Ground data (collated from a variety of databases) Remote sensing image (optical or radar) Validation samples Training samples Windshield surveys Crop insurance Irrigation district Native Prairie Baseline Signatures AGRASID Hydrology Township fabric Classification Land cover map 11

12 Mapping Grassland Spatial Extent! FQ4 fine quad-pol images (HH, VH, HV and VH)! Multiple dates! Yamaguchi decomposition! July/August provide best results (overall classification 81-82%; GVI polygons correctly identified 81-84% ) 12

13 Mapping Grassland Spatial Extent 13

14 Mapping Grassland Spatial Extent 14

15 Mapping Grassland Spatial Extent To investigate the potential to use satellite remote sensing products to update the grassland vegetation inventory: Wholesale change (native grassland to cropping) Partial change (oil, gas, transportation infrastructure) Provide a tool for directing groundbased surveys Landsat TM image acquired in July 2011, R:band 4, G:band 3, B: band 2 15

16 Mapping Grassland Spatial Extent 5185 Upland-Grassland polygons covering 14 site types Method development database: 74 native grassland polygons 152 cropped polygons Method validation data: 34 native grassland polygons in both 2006 and native grassland polygons converted to cropping systems between 2006 and

17 Mapping Grassland Spatial Extent! Landsat imagery (6 bands BGR, NIR, 2MIR) functional vegetation indices (Greenness/photosynthesis; vegetation/landscape water content; senescent vegetation)! Hybrid change detection Update changed areas only 2006, 2011, 2013 GVI polygons overlaid on July 6, 2006 Landsat TM5 False Colour Composite Image 17

18 Mapping Grassland Spatial Extent v Field validate Edit polygon July August Native grasslands converted to croplands Native grasslands Results- July and August change detection map 18

19 Mapping Grassland Spatial Extent July July Native grasslands converted to croplands Native grasslands Results-Change detection and

20 Mapping Grassland Spatial Extent 2006 GVI 2007 SPOT Image Database Record Add/edit/delete Updated GVI Slides courtesy of Barry Adams AESRD 20

21 ESTIMATING ANNUAL PRODUCTIVITY OF GRASSLANDS 21

22 Estimating Introduction Annual Productivity of Grasslands! Canada initial testing 1980s!! African, Point 1 North American and Australian savannahs! Point 2 22

23 " Rangeview " MODIS 250 m, daily coverage " Difference from average State of Arizona, 2001 versus 2003, February 17 and May 8 23

24 Estimating Introduction Annual Productivity of Grasslands! Point 1! Point 2 Pasture assessment 24

25 " Pastures from Space/ Pasture Watch " light use efficiency model " integrates MODIS satellite imagery, 250 m, 10 day composites and climate data 25

26 Estimating Annual Productivity of Grasslands! Empirical relationships Landsat (multiple scenes) 16 day repeat cycle 30 m spatial resolution! Simple modelling Moderate Resolution Imaging Spectrometer Daily coverage 250 m spatial resolution! Validation data? 26

27 Estimating Annual Productivity of Grasslands! Rangeland Reference Area Monitoring Program Alberta Environment and Sustainable Resource Development 5-20 years of clipping data Spatially discrete Caveat: Measure potential production Scale 27

28 Estimating Annual Productivity of Grasslands 5000 Y= X , R² = Y = x , R² = Green biomass (kg ha -1 ) Green biomass (kg ha -1 ) LANDSAT DATA NDVI 0 LANDSAT DATA MTVI2 Cages Community Types R 2 (2008) R 2 (2009) R 2 ( ) NDVI MTVI1 MTVI2 NDVI MTVI1 MTVI2 NDVI MTVI1 MTVI2 Foothills rough fescue (FRF) Needle-and thread (NT) Northern wheatgrass (NW)

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31 Estimating Annual Productivity of Grasslands MODIS IMAGERY fapar=(1.21*ndvi)-0.04 APAR (MJ m -2 day -1 ) =fapar*par PAR=(0.5*average daily solar radiation (MJ m -2 day -1 )) APAR u (MJ m -2 day -1 ) = APAR*MI*TI TI=temperature stress index calculated from weather data and optimal values derived from literature (0-1) MI = moisture stress index derived from AGRASID soil information and Nix (1981) (0-1) G T (kg ha -1 day -1 ) = APAR u *LUE*10 where G T is total (above ground and below ground) grass growth 10 is a factor to convert NPP T to kg ha -1. AG A (kg ha -1 day -1 ) = G T * root-shoot ratio where AG A is above ground grass growth AAG(kg/ha annum) = sum jan-dec (AG A *10) where AAG is annual above ground grass growth 31

32 Estimating Annual Productivity of Grasslands APAR Temp. Index Temperature Index Aden (NT) Opt T=15oC Opt T=20oC Opt T=20oC Mean T Mean Air Temeprature (oc) Moist. Index 33

33 Estimating Annual Productivity of Grasslands 34

34 Estimating Annual Productivity of Grasslands Considerations! Multiples spatial resolutions! Size of sampling units (MODIS pixel versus clipping)! Timing of field clipping! Model integrated over time versus clipping is from a single time period! Carryover from different years (influences NDVI itself, masks green vegetation) 35

35 MAPPING NOXIOUS WEEDS 36

36 Mapping Noxious Weeds in Grasslands # Noxious weeds # Reduce productivity and biodiversity # Compete for water resources # Toxic to livestock # Economic impacts (loss of production and cost of control) # Quantitative surveys of invasive species occurrence on the Prairie grasslands are rare due to the cost and accessibility # Remote sensing shows promise 37

37 Mapping Leafy Spurge in Grasslands! Extent of coverage 5 million acres in 15 states and 6 Canadian provinces Manitoba alone 340,000 acres river banks, pastures, and native grasslands! Economic Impacts $130 million annually in North Dakota, South Dakota, Montana, and Wyoming $19 million annually in Manitoba control, loss of forage, loss of livestock! Control biological (insects and sheep) herbicides! Mapping! by people spraying or releasing insects for biocontrol! remote sensing Control by Aphthona sp. 37

38 Mapping Leafy Spurge in Grasslands! Remote sensing Coverage of large and inaccessible areas Lower cost Distinct yellow bracts Unique spectral signature Success in the USA! No wide adopted Considerations! Density, Patch size 38 39

39 Mapping Leafy Spurge in Grasslands AISA imagery Mixed-Tuned Matched Filter! Only requires end-member for target of choice! Provides presence or absence 40

40 AISA Simulated WorldView 2 Simulated Quickbird Sampes points detected (%) Yes 0 No <1 to 10% <10 to 20% <20 to 30% Site 1c Site 1c Site 1c <30 to 40% >40% 0 <1 to 10% <10 to 20% <20 to 30% <30 to 40% >40% 0 <1 to 10% <10 to 20% <20 to 30% AISA Quickbird Worldview-2 Ground cover of yellow flowers (%) <30 to 40% >40%! >30% ground cover of flowering leafy spurge can be detected! <30% detection depends on proximity to higher density patches! potential to use high spatial resolution satellites 40

41 Mapping Leafy Spurge in Grasslands Draganfly Commander ebee DJI Inspire 1 Precision Hawk Multi-rotor, up and down, mins. Fixed-wing, larger areas, 30 mins. 41

42 Mapping Leafy Spurge in Grasslands 43

43 Mapping Leafy Spurge in Grasslands! 9 sampling areas! Transects (3)! Quadrats (19)! Photograph (% total & flowering leafy spurge! Visual density (L, M, H)! Stem count 43

44 Mapping Leafy Spurge in Grasslands Site Site1 Collection date 9 am, July 30, 2016 Flight height (m) Scenes Spatial resolution (cm) Covering area(m 2 )

45 Mapping Leafy Spurge in Grasslands Mapped leafy spurge cover versus ground measured leafy spurge cover Mapped leafy spurge cover versus ground measured flowering leafy spurge cover Mapped leafy spurge cover versus ground measured flowering leafy spurge density 45

46 Mapping Leafy Spurge in Grasslands # UAV Images! Image acquired on June 28, 2015! Flight height m! Spatial resolution: 3.2 cm 47

47 Mapping Leafy Spurge in Grasslands Leafy spurge Leafy spurge mapped using Mixed Tune Matched Filter Classification 47

48 Mapping Leafy Spurge in Grasslands Leafy spurge Original classification map Classification map with tree and shrub mask applied 48

49 Mapping Leafy Spurge in Grasslands Ground samples 1. Transect: Quadrats: 110 (High, Medium, Low density leafy spurge and no leafy spurge) 3. Stems of leafy spurge and flowering leafy spurge were recorded with each quadrat 49

50 Mapping Leafy Spurge in Grasslands Overall classification 70% Producer Accuracy 67% User Accuracy 88% # Reconcile spatial resolution of image <4 cm) with global position system accuracy (>1 m) Leafy spurge 50

51 Remote Sensing in Grasslands! Potential applications of remote sensing Mapping grassland spatial extent and changes over time Estimating productivity Mapping invasive plant species Others???? Remote sensing offers the potential to map broader landscapes 51

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