Multi-temporal Analysis of Landsat Data to Determine Forest Age Classes for the Mississippi Statewide Forest Inventory- Preliminary Results Curt Collins David Wilkinson David Evans

General Problems Improvements made with traditional large- scale inventory methods (simple random vs. stratified random sampling). –Define sample design strata –Used to allocate plots Further information can be extracted using time-series data to determine resource age

Specific Problem How can resource age information be used? –Model future resource metrics (G-Y modeling) –Possibly provide more strata-definition data (post-stratification) –Identify areas of older stock for economic development purposes (mill feasibility, etc.) –To estimate areas of interest with regard to habitat for ecological concerns How can these ages be found?

Multi-temporal Analysis (a.k.a. Change Detection) Methods Category 1 Algebra Category 2 Transformation Category 3 Classification Category 4 Adv. Models Category 5 GIS Category 6 Other -Image Diff. -Image Regression -Image Ratioing -Veg. Index Diff. -CVA -Background Sub. -PCA -Tasseled Cap -Gramm-Schmidt -Chi-Square -Post Comparison -Spectral-Temporal Analysis -EM Detection -Unsupervised Change Detection -Hybrid Change Detection -Artificial Neural Networks -Li-Strahler Reflectance -Spectral Mixture Model -Biophysical Parameter Method -Spectral Dependence -Knowledge-Based Visual System -Area Production -Change Curves -Generalized linear models -Curve-theorem -Structure-based -Spatial Statistics Adapted from: Lu et al, 2004 International Journal of Remote Sensing -Integrated GIS/Remote Sensing -GIS Approach

Remotely Sensed Data Landsat TM (i.e., ETM+) –Bands 2 (  m), 3 (  m), 4 (  m), & 5 (  m) –Approximately 30 m spatial resolution Landsat MSS –All bands: 1 (  m), 2 (  m), 3 (  m), & 4 (  m) –Approximately 60 m spatial resolution Tassel cap (TC) (also used TM bands 1 & 7) and NDVI transformations also used

Remotely Sensed Data (cont.) Temporal Resolution Selection: Five year intervals were chosen through experience with past projects (four county pilot and Texas-SFA) Data Preprocessing: Georectified to county-level DOQQ mosaicks available from MARIS –Goal was to decrease overlay error to <1 pixel

Study Areas 1.Weyerhaeuser lands in SE Louisiana (St. Tammany & Washington Parishes) –~73,770 ha in actual test area –Predominantly industrial Southern pine timberland 2.John W. Starr School Forest (MSU) (Oktibbeha & Winston Counties) –~905,150 ha in actual test area –Combined research/industrial Southern pine and bottomland hardwood timberlands 1 2

Remotely Sensed Data Chosen Landsat Image Data Used on the Southeast Louisiana Test Area Approximate Year (ID) PathRowSensorSeason Actual Acquisition Date ETM+Leaf-off12/28/ ETM+Leaf-on08/03/ TMLeaf-on09/28/ MSSLeaf-on10/05/ MSSLeaf-on08/31/ * 39MSSLeaf-on09/10/ ** 39MSSLeaf-on10/08/74 * Differs from previous scenes, Landsats 1-3 used a different path-row sequence than newer missions. ** These data are from NALC which uses the newer Landsat (post-Landsat 3) path-row sequence.

Present Landsat Classification ISODATA 100+ Clusters INTERP./ RECODE F/N-F Thematic Recent Leaf-on Recent Leaf-off (Masked) Training Areas are defined with DOQQ data serving as “True” DOQQ-derived N-F, Pine, Hardwood Thematics Final Pine, Mixed, & Hardwood Forest and Non- forest Thematic MAX. LIKE. CLASSIFIER

Multi-temporal Analyses: Post-Classification Comparison (PCC) Two methods attempted: 1.Whole, independent scene classification –Performed on leaf-on datasets, with the exception of the most recent classification –Used, again, 100+ Isodata-derived clusters –Whole Landsat scenes were separately classified for each time period –Forested pixels (recent classification) are “tagged” with an origin based on the most recent non-forest pixel in that locale

Multi-temporal Analyses: Post-Classification Comparison (cont.) 1.Whole, independent scene classification Forest 1991 Forest Change Thematic Non-forest to Forest Non-forest to Forest Forest to Non-forest N-F Classified Non-forest Classified Forest

Multi-temporal Analyses: Post-Classification Comparison (cont.) 2.Masked, independent scene classification –Performed on leaf-on datasets, again, with the exception of the most recent classification –Used, again, 100+ Isodata-derived clusters –Masked Landsat scenes were separately classified for each time period Masked regions incorporate all recent forested areas whose origin was unaccounted for –Forested pixels (recent classification) are “tagged” with an origin based on the most recent non-forest pixel in that locale

Multi-temporal Analyses: Post-Classification Comparison (cont.) 2.Masked, independent scene classification Forest 1991 N-F Forest Change Thematic Classified Non-forest Classified Forest Non-forest to Forest Non-forest to Forest Excluded from Classification

Multi-temporal Analyses: Post-Classification Comparison (cont.) Both of these methods yielded: A forest age thematic representing six approximate age classes: Note: Class 1 ( ) was noted at present as regeneration as it changed from forest to non-forest.

Multi-temporal Analyses: Temporal Image Differencing Involved TC and NDVI transformations of TM and MSS data across the time-series dataset –Required DN to at-satellite reflectance conversion TC and NDVI transformed layers from each of the 6 time-series datasets investigated were differenced –This produced 16 individual “change” layers

Multi-temporal Analyses: Temporal Image Differencing (cont.) Transformed Change Images Used in Image Differencing Approximate Year (ID) Intervals Actual Year Intervals Transformed Layers NDVI, Brightness*, Greenness*, Wetness* NDVI, Brightness*, Greenness* NDVI, Brightness*, Greenness* NDVI, Brightness*, Greenness* NDVI, Brightness*, Greenness* * Derived from tasseled cap transformations.

Multi-temporal Analyses: Temporal Image Differencing (cont.) Transformed difference layers were next masked in two stages 1.Masked out pixels classed as non-forest from both 2003 and 1996 datasets 2.Masked out pixels left from mask 1 which were within standard deviations (heuristics) of the mean (deemed “no change”) The masked datasets were next stacked into TC and NDVI datasets

Multi-temporal Analyses: Temporal Image Differencing (cont.) The stacked datasets were then classified –Utilized maximum likelihood approach in Imagine 8.7 –Six time intervals: –≥4,000 pixels per interval were used in signature definitions

Results: Post-Classification Comparison Overall accuracies from the two PCC operations utilized Weyerhaeuser’s company GIS (LA): GIS regeneration dates (years) were used to class areas into comparable classes as the PCC thematic results The whole scenes exercise yielded: –KHAT = –Overall accuracy =

Results: Post-Classification Comparison (cont.) Accuracies: Whole Scenes Post Classification Comparisons Approximate Year (ID) Intervals Actual Year Intervals Producer’s Accuracy Statistics User’s Accuracy Statistics 2003 (present)

Results: Post-Classification Comparison (cont.) The masked scenes exercise yielded: –KHAT = –Overall accuracy =

Results: Post-Classification Comparison (cont.) Accuracies: Masked Scenes Post Classification Comparisons Approximate Year (ID) Intervals Actual Year Intervals Producer’s Accuracy Statistics User’s Accuracy Statistics 2003 (present)

Results: Temporal Image Differencing Examined through output class areas: Expect somewhat consistent NDVI vs. TC estimated class estimates Expect somewhat constant area estimates through time

Results: Temporal Image Differencing (cont.) Simultaneous Image Differencing Time-Series Area Estimates Approx. Year (ID) Intervals Actual Year Intervals NDVI- Derived Area (ha) Tasseled Cap- Derived Area (ha) Difference (NDVI-TC) ,98230,268-5, ,17123,3923, ,93883,400-51, ,38312,70618, ,47756,310-30, ,556167,95951,597

Discussion/Conclusion Issues noted in this exploration: Lower accuracies due to rectification limits (LA)? Regeneration class: land use vs. land class –Possible GIS incorporation? In the future… More definitive accuracies will be available –Aerial photo validation –MIFI ground data Continued R & D into this problem –One reason we are here---FEEDBACK IS WELCOMED!

Acknowledgements Weyerhaeuser Company Various SITL team members Mississippi Institute for Forest Inventory (MIFI) Contacts: Curt Collins David Wilkinson Dr. David Evans