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Global Land Cover Change (Training Data (GEE Data Matching:
GEE display:…
Global Land Cover Change
Training Data
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Upload data to GEE:check:
- Saved as .csv file from R and upload as table in GEE.
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GEE Data Matching:
- GEE display: Mercator - EPSG:3857
- Data we have: WGS84 - EPSG:4326
Pixel Matching:check:
- Each 1km MODIS pixel is perfectly seperately as four 500m subpixels
- GEE Projection mismatching issue is fixed by using the original projection (SIN) rather than the composite projection (WGS84, which is default for all the composite images in GEE)
Issue :question::
- Hold an assumption that all subpixels within a 1km MODIS pixel have same label
Data Filter :question: :1. VIIRS did:
- Filter out the pixels that do not agree with the results from MODIS C5, UMD 2000, IGBP DISCOVER, GLC2000
- Only use the pixels that agree on all the land cover map
- Due to the different classification scheme, only compare Forest, Cropland, Grassland, Savannas, Woody Savannas, and Shrubland.
2. Hansen 1km MODIS:
- find the 100% agreement pixels with Landsat Multispectral Scanner System
Overall Goals:
land cover classifications
Imagining all the images follow the random process; thus, all the spatial and temporal information is associated together.
This is the reason why we use the Spatial-temporal random process.
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Thoughts
- focusing on the pixel more likely to change so as to reduce the propogaton of errors
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Input Features
C6 NBAR Data:Wang, Z., Schaaf, C. B., Sun, Q., Shuai, Y., & Román, M. O. (2018). Capturing rapid land surface dynamics with Collection V006 MODIS BRDF/NBAR/Albedo (MCD43) products. Remote sensing of environment, 207, 50-64.
- everyday input data is retrieved based on a 16-day interval
- A Laplace distribution is used to assign weights to 16-day data
- Snow is counted based on the status of the interest of the day. (heavy scattering effects on snow vs. isotropic diffuse radiation
Smoothed and Gap-Filled using Penalized Smoothing Splines:question:
- Done in MODIS C6
- no need in our method becuase of the short-term average
Extract Interval :check:
- Monthly average
- select the one with max NDVI
1. MODIS C6 Did:
- 5-day intervals, the band info cross the year is used (some are predicted by smoothing spline)
- snow and cloud cover flags are used
2. MODIS from Dr.Gong Did:
- 32-day averages across the year
- slope and latitude are used
3. VIIRS Did:
- Select the maximum NDVI values to composite monthly data
- min/max/mean/ NDVI from 8 greenest months
4. AVHRR 1km from Hansen:
- 10-day interval with max NDVI values
- across 12 months
- use max NDVI to reduce data volumes and cloud contamination, followed by a filter to remote spikes
Quality Control:question:
- Not sure if needed, becuase we only need to make sure the quality of training data
Two-Band Spectral Indices: MODIS Band 1-7
- red
- NIR
- blue
- green
- SWIR1
- SWIR2
- SWIR3
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Classification
Train Sample Export
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Structure Data Classification DNN
- Easy to modify data (remove missing data)
- Not test how to classify image data