Cumulative Effects and Land Use Monitoring Technology

Transcription

1 Cumulative Effects and Land Use Monitoring Technology prepared for the 2013 Remote Sensing and Monitoring Forum Roger De Abreu Canada Centre for Remote Sensing (CCRS) Purpose of Presentation: Demonstrate how remote sensing supports cumulative effects monitoring The context today for regulatory compliance Establishment of Baselines Detecting, Quantifying and Monitoring Change Understanding Landscape Process / Dynamics Validation and Input to Models 1

2 Baseline for Change CCRS LTSDRs (Latifovic et. al.) Long Term Satellite Data Records (LTSDRS) Very high quality, national scale composites High radiometric and geometric accuracy resulting from CCRS correction methods yields low noise levels and consistency of composites over time and space Enables assessment of subtle long term trends and comparisons across different areas Long Term Satellite Data Records Sensors AVHRR NOAA 1 km Period: Transition: MetOp MODIS NASA 1; 0.5; 0.25 km Period: Transition: NPOESS VIIRS SPOT/VGT VITO 1km Period: Transition: SENTINEL MERIS ESA 1; 0.3 km Period: (ended) Transition: SENTINEL

3 Derived Indicators Radiation Albedo Snow LAI Forest Cover.obtained from the LTSDRs directly or via inputs to models Land cover LC change ET NPP Cumulative Impacts of Summer Range on Bathurst Caribou Productivity CIMP (W. Chen) Identifying, monitoring impacts of changing habitat on caribou is a priority in CIMP Developed AVHRR based indicators of the quantity, availability and quality of Caribou summer range forage Strong relationship between changes in summer range to Caribou productivity Summer range 1:1,500,000 Nunavut 3

4 Bathurst caribou Late-winter calf:cow ratio (%) Remotely sensed summer range cumulative indicator 06/06/2013 Cumulative Impacts of Summer Range on Bathurst Caribou Productivity CIMP (W. Chen) Late-winter CC ratio Summer range indicator Year Land Cover Change Detection w Landsat CIMP (R. Fraser et. al.) Investigating the utility of 30 m historical Landsat imagery (1985- present) for large-area change monitoring within NWT Analyzing surface changes within a ~ 300,000 km2 study region or 25% of NWT land area using 250 scenes. Change detection approach examines long-term reflectance trends using dense stacks of growing-season Landsat images. 4

5 Landsat Trends ( ) Norman Wells Area (35km extent) Regenerating Seismic Lines Old Disturbance and Forest Succession Old Canol Road 10 Land Surface Characterization and Change in the Alberta Oilsands Region (Latifovic et. al.) Generate regional LTSDRs in finer detail Derive land surface information (land cover, phenology, surface temperature etc.) to establish baselines, analyze trends and support modeling for assessment of land surface/water/air processes MODIS Vegetation trends (NDVI) , 250m Fort McMurray Slope.tif Value Greening High trend : 0.0 Low : -0. Browning trend 5

6 Snow Cover Monitoring in the AOSR (R. Fernandes) Alberta Environment and Sustainable Resource Development (AESRD), Environment Canada. Information on snow cover trends for habitat assessment Evaluation of feasibility of real-time snow cover monitoring to improve fire danger risk assessment over the AOSR Monitoring River Ice Dynamics J. van der Sanden et. al. Study areas: Athabasca River at Fort McMurray (freeze-up / break-up) Peace Athabasca Delta (break-up / flooding) Validation and calibration of hydraulic, sediment transport, and contaminant transport models New/ improved approaches to extract ice cover / flood information from (future) satellite SAR images. Ice cover condition, Fort McMurray, April 29, 2013, 01:24 13:29 UTC 6

7 Polarimetric SAR for Oil Sand Peatland Monitoring (R. Touzi et.al.) Polarimetric SAR methods have been developed for improved peatland classification / monitoring Touzi scattering parameter detects subsurface water flow L-Band SAR improves separation of bog from fen strong complement to optical based classifications Google Earth Alberta ALOS_PALSAR_path190_05 June 2007 Polarimetric SAR for Oil Sand Peatland Monitoring (R. Touzi et.al.) Landsat Classification Wetland Inventory s RS2_RGB (R:HH, G:HV, B:VV) ALOS_RGB (R:HH, G:HV, B:VV) 1 7

8 Water Level Estimation (B. Brisco et. al.). Investigating an operational approach to water level estimation and wetland change using SAR data and interferometry Results indicate that wetlands maintain good coherence over growing year Corner reflectors installed to assess the relationship between changes in phase and water levels on emergent vegetation Changes in coherence could be a detector of wetland conditions as well In Situ Extraction Site Surface Deformation V. Singhroy, J. Pearse (AGS) et. al.) Build InSAR deformation maps and correlate with industrial injection heave models. Build capacity of regulators to support operational use of InSAR RADARSAT-2 InSAR measuring surface deformation at rates of cm/year in active in situ sites Strong correlation btw deformation rates and steam injection Blowout 8

9 In Situ Extraction Site Surface Deformation V. Singhroy, J. Pearse (AGS) et. al.) Build InSAR deformation maps and correlate with industrial injection heave models. Build capacity of regulators to support operational use of InSAR RADARSAT-2 InSAR measuring surface deformation at rates of cm/year in active in situ sites Strong correlation btw deformation rates and steam injection Blowout Pearse et. al., 2013) Pearse et. al., 2013) 9

10 Pearse et. al., 2013) Infrastructure and Landscape Change in AOSR Y. Zhang et. al. Study areas: Open Mining Site : Kearl Lake In Situ Site : Christina Lake Expected outputs: Methods and tools for operational monitoring of infrastructure development and land disturbances using high resolution imagery Information products for study sites Recommendations to integrate the outputs in compliance monitoring operations Temporal Development Kearl Lake Open Pit : Kearl Lake Land Disturbance Christina Lake In Situ: Christina Lake 10

11 Infrastructure and Landscape Change in AOSR Y. Zhang et. al. Study areas: Open Mining Site : Kearl Lake In Situ Site : Christina Lake Expected outputs: Methods and tools for operational monitoring of infrastructure development and land disturbances using high resolution imagery Information products for study sites Recommendations to integrate the outputs in compliance monitoring operations Temporal Development Kearl Lake SPOT 5 image time series ( ) Land of Disturbance the Kearl Lake Christina project Lake site Kearl Lake Anthropogenic Changes ( ) SPOT Water 2007 Water 2011 Figure 7 11

12 Christina Lake Anthropogenic Changes ( ) SPOT5-RapidEye Water 2006 New Water 2011 Questions? Project Leads Long Term Satellite Data Records Rasim Latifovic Caribou and Summer Range Wenjun Chen Landscape Change in NWT Rob Fraser Water Level Estimation Brian Brisco Peatland Monitoring Ridha Touzi River Ice Joost van der Sanden In Situ Monitoring Vern Singhroy Infrastructure Change Ying Zhang Snow Cover Monitoring Richard Fernandes 12

13 Role of Remote Sensing Water Level Estimation Peatland Monitoring Snow Cover Monitoring River Ice In Situ Site Monitoring Infrastructure Hyperspectral / LiDAR Long Term Satellite Data Records 13

14 Projects in the AOSR Initiated 2012 Meetings with provincial & federal regulators in February 2012 A number of different collaborators & supporters Alberta Environment (AE) Alberta Environment and Sustainable Resource Development (AESRD) Energy Resource and Conservation Board (ERCB) Alberta Geological Survey (AGS) Environment Canada (EC) Canadian Space Agency (CSA) Projects in the AOSR Snow Cover Monitoring Collaborators: Alberta Environment and Sustainable Resource Development (AESRD) Expected outputs: Information on snow cover trends for habitat assessment Evaluation of feasibility of real-time snow cover monitoring to improve fire danger risk assessment over the AOSR 14

15 AOSR Land Surface Characterization CCRS is using existing LTSDRs and creating finer scale LTSDRs over the AOSR Environmental information needs identified through stakeholder meetings Water quality / quantity Air quality Biodiversity Biochemical cycling (e.g. carbon) Wild fire risk Wildlife monitoring and assessment Projects in the AOSR Hyperspectral & LiDAR Collaborators: University of Victoria University of Alberta University of Lethbridge University of Calgary 500K over 2 years from NRCan Study areas: > 1000 km 2 in the AOSR Expected outputs: Determine potential for forest / peatland monitoring Examine relationships between airborne hyperspectral and spaceborne hyperspectral (coming ~ 2015) 15

16 National Scale Land Surface Characterization Land Cover 20m 4 Coniferous High Density 40 young conifer high density 19 Coniferous Medium Density 94 medium conifer arid rangeland medium conifer on sand 1 Coniferous Low to Medium Density 26 Sparse Coniferous-Shrub Cover 95 sparse conifer arid rangeland 93 Spruce Lichen Bog sparse conifer rock / lichen 3 Mixed conifer 2 Mixed Medium to High Density 6 Mixed deciduous 48 Deciduous Medium to High Density 5 recent fire < 2 years? 11 regenerating fire < 10 years sparse / young conifer forest 7 older fire - open conifer regen sparse conifer / herb - shrub 108 young mixed forest regen. 12 recent disturbance - cutover managed land 21 agriculture (planted cutover) 10 arid rangeland 13 tall shrub 105 Low Lying Shrub High Density 14 herb / shrub (Bryoids may substitue for herb northward) 15 herbaceous wet sedge 16 tussock tundra 17 dry upland graminoid tundra 89 lichen dominanted 90 Wet Bryoid / lichen 86 Low Vegetation/Lichen Barren 27 (5) Ice Wedge Polygons (HB Lowlands) 96 Rock Outcrop 134 Road/Bare/Gravel 200 barren unconsolidated - sand 62 Wetland/Fen 100 water 201 ice AOSR Land Surface Characterization Objectives: Generate regional LTSDRs in finer detail Temporally: Daily Composites at 250 m Spatially: Annual or Biannual Composites at 20 m (beginning in 2015 Sentinel 2) Derive land surface information (land cover, phenology, surface temperature etc.) to establish baselines, analyze trends and support modeling for assessment of land surface/water/air processes 16

17 06/06/2013 AOSR Land Surface Characterization Volatile Organic Compounds (VOCs) Project contribution to biogenic VOC modeling: Leaf Area Leaf Area Index Long Term Satellite Data Records (LTSDR) 6 Biogenic VOC model 3 0 Land Cover Processing to generate high quality data: Geolocation Radiometric calibration and correction Cloud\shadow screening Atmosphere correction Sun-sensor geometry correction Topographic correction (if needed) Compositing Source: Sakulyanontvittaya, T. and G. Yarwood. Improved Biogenic Emission Inventories across the West. Final Report prepared for Western Governors Association, Denver Colorado, March 19, AOSR Land Surface Characterization Systematic Land Surface Characterization Land Cover MODIS 250m *Allows land cover dynamics to be assessed in modeling. 17

18 AOSR Land Surface Characterization Regional LAI Validation Air-Shed Modelling Area Validation Samples 140 In-situ LAI Plots CCRS Leaf Area Index Modellers require error bars to make use of LAI products. LAI product validation over oil sands region following international protocols and survey methods that were both developed by CCRS scientists. RGB Composite of Tasseled Cap Trend Channels Effective for Visualizing Different Types of Physical Changes TC Brightness Trend TC Greenness Trend TC Wetness Trend RGB Composite trend channels B=Brightness Change (red channel) G=Greenness Change (green channel) W=Wetness Change (blue channel) Yellow = B G W (e.g. water veg) Blue = B G W (e.g. slump disturbance) Light Blue = B G W (e.g. veg growth) 18