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On Remotely Sensed Responses Monitoring Science and Technology Symposium September 21, 2004 Raymond L. Czaplewski, Ph.D. USDA Forest Service Rocky Mountain.

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Presentation on theme: "On Remotely Sensed Responses Monitoring Science and Technology Symposium September 21, 2004 Raymond L. Czaplewski, Ph.D. USDA Forest Service Rocky Mountain."— Presentation transcript:

1 On Remotely Sensed Responses Monitoring Science and Technology Symposium September 21, 2004 Raymond L. Czaplewski, Ph.D. USDA Forest Service Rocky Mountain Research Station

2 2 Outline Remote sensing products Sampling elements USDA monitoring of natural resources Other monitoring programs

3 3 Outline Remote sensing products

4 4 Forest type map using multi-temporal multi-spectral satellite data

5 5

6 6 Low-altitude aerial photography Measure tree heights Identify tree species Count trees / % tree cover Horizontal distances to objects Detect tree mortality Interpret forest condition class Predict field measurements (dbh) from photogrammetric measurements (tree height)

7 7 Frequent monitoring of changes in land use and land cover Wildfire Insect and disease Forest harvesting Land use Urban interface

8 8 Satellite change detection 2000Urban growth Forest to urban Agriculture to urban 1990

9 9 Rapid assessments after catastrophic events

10 10 Hurricanes

11 11 Regional change detection – one week later MODIS Change Detection

12 12 Sample of high-resolution aerial imagery allows rapid, large-area assessments of hurricane damage severity Tree Blow Down Permanent record, low cost, accurate, no accessibility problems

13 13 Ice storms

14 14 Regional sample of measurements over large regions of area affected by ice storm damage Moderate damage Heavy damage

15 15 Permanent Record in Map Format Southern California wildfires

16 Forest change detection Paired TM images 5 years apart Increasing vegetation Decreasing vegetation

17 17 Outline Remote sensing products Sampling elements

18 18 Issues with remotely sensed data Data volumes Resolution of images relative to size of population elements Registration of image to population elements

19 19 Resources to process remotely sensed data for USA Number of personal computers 0 1 AVHRR 1000-m 5 MODIS 250-m 10 1, Landsat 30-m 100, ,000 IKONOS 1-m 10,000,000 Low-altitude aerial photo 0.2-m Mapping Sampling

20 m Agricultural fields Wetlands Lakes and pondsStreamsForest standsUrban Sampling elements

21 ha forest inventory plot measured by field crew Individual trees Sampling elements

22 m Sampling elements 64-ha sample unit

23 23 Satellite data Resolution of remotely sensed data relative to the scale of the sampling elements Registration error between satellite data and sampling elements

24 m AVHRR 1000-m pixel

25 25 MODIS 250-m pixel 250-m

26 26 Landsat 25-m pixel 25-m

27 m 25-m

28 28 Outline Remote sensing products Sampling elements USDA monitoring of natural resources

29 29 USDA surveys of natural resources Forest Service Land management agency Data for national assessments Data used by State agencies, industry Forest Inventory and Analysis Natural Resources Conservation Service Assist farmers soil and water conservation Data used for 5- year Farm Bill National Resources Inventory Statistics agency Crop & livestock production Agricultural prices Farm labor and wages Data used for National Agricultural Statistics Service

30 30 USDA surveys of natural resources Forest area and type Trees and wood Forest condition and health Forest Inventory and Analysis Land cover and land use Soils Erosion Wetlands Land mgmt. Other natural resources National Resources Inventory Crop & livestock production Agricultural prices Farm labor and wages National Agricultural Statistics Service

31 31 USDA surveys of natural resources Field data Remotely sensed data for statistical efficiency Medium resolution national maps Forest Inventory and Analysis Remotely sensed data Field data for difficult measurements Maps with geospatial techniques National Resources Inventory Questionnaires Some field measures Remotely sensed data for sampling frame Remotely sensed data for statistical efficiency and maps National Agricultural Statistics Service

32 32 $0 $100-million $50-million NRI FIA Annual budget Forest & timber NASS Crops & livestock Soil, water & wetlands

33 33 Estimates of area by land use and land cover classes NRI, soil, water, wetlandsFIA, forests, timber Forest Harvested Non-stocked Forest Detailed stand types Stand size classes Non-stocked Cropland Pasture, rangeland CRP Other rural land Built-up and urban Wetland Non-forest Water

34 34 Coverage by land ownership NRI, soil, water, wetlandsFIA, forests, timber Private ownership Public ownership

35 35 FIA and NRI sampling frames 1-plot per 6000-acres NRIFIA 10-miles average of 1 plot per 8,000-acres Systematic grid More plots in diverse landscapes

36 , , , ,000 FIANRI Sampled populations Federal lands Nonforest plots Forest plots Non-federal lands

37 37 Plot re-measurement frequency 2001 Year Panel Core NRI FIA Rotational Every 1-8 years on average Remeasurement frequency Every 7 years

38 ,000 40,000 60,000 80,000 Nonforest plots Forest plots FIANRI Number of plots remeasured each year Core plots (annual) Rotational plots

39 39 Cost of measuring a plot FIA field data NRI remote sensing

40 40 Differences in estimated area of non-federal forestland FIA and NRI FIA and NRI estimates in agreement <3% difference or <1,000,000 acres non-federal forest NRI estimates more than FIA NRI estimates less than FIA 3-5% 5-10% 3-10% zzzzzzzzzzzzzz 10-15% 10-20% 15-30% 30-55%

41 41 NRI acres of forest Core Rotational Statistical calibration of area estimates NRI acres of forest FIA acres of forest Product Estimator Vol acre Acres X Total Volume = Panel Volume per forested acre Potential to build connections between different monitoring programs

42 42 Outline Remote sensing products Sampling elements USDA monitoring of natural resources Other monitoring programs

43 National Wetlands Inventory

44 44 NWI Sample of high- altitude aerial photography used to estimate wetland area and changes

45 stratumasas psps eses 11,00060% (55)600 (550) 22,00050% (45)1,000 (900) 33,00030% (28)900 (840) 44,00010% (7)400 (280) Sum10,0002,900 (2570) Primary Sample Unit (PSU) Secondary Samples (Segment, Sample) Direct Expansion Single- & Double-stage Sampling Review Conventional Strata

46 46 Food and Agricultural Organization (FAO) of the United Nations Global Forest Resources Assessment Conducted every 5 to 10 years by FAO since 1946 Independent remote sensing survey since 1992 Very simple, but globally consistent among nations.

47 47 Sub-continental multi-national assessment areas

48 48 Realized stratified random sample ???

49 Sampling Unit (Landsat scene) #4409 North East India 1990 Changes 1981 to 1988

50 50 Sampling error in deforestation estimates

51 51 © Space Imaging Europe 2000, Novosat Ltd, National Land Survey of Finland 1 m IKONOS panchromatic & 1: map subset. Lohja, Finland

52 52 Landsat 5, TM, bands 4,3,2

53 53 Simulate pre-stratification with low-resolution satellite data (e.g., 250-m MODIS) and compare sampling with 30-m Landsat v. 1-m Ikonos scenes Stratum boundaries for 10x10-km Small Sampling Units 1N i Cumulative square root of change index from MODIS low- resolution satellite data 1N i Stratum boundaries for 150x150-km Large Sampling Units

54 Forest area at time 0 (km 2 ) All of Europe and CIS Total sample size (n) Boreal Coniferous Forest Ecological Zone Estimates from 1-m data, unbiased Total sample size (n) Estimates from 30-m data, includes misclassification bias Total sample size (n) Change in forest area over 10 years (km 2 ) Total sample size (n)

55 55 Conclusions Remotely sensed data can monitor land cover and land use for large areas Unbiased estimates require linkage to field data Scale and registration major issues Opportunities greatest for multi-sensor remote sensing linked to field data collection Possible synergies building connections between independent monitoring programs

56 Thank you


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