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changeDetectionLib.py
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changeDetectionLib.py
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"""
Apply change detection methods usin GEE
geeViz.changeDetectionLib is the core module for setting up various change detection algorithms within GEE. Notably, it facilitates the use of LandTrendr and CCDC data preparation, application, and output formatting, compression, and decompression.
"""
"""
Copyright 2024 Ian Housman, Leah Campbell, Josh Heyer
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
# Script to help with basic change detection
# Intended to work within the geeViz package
######################################################################
# Adapted from changeDetectionLib.js
from geeViz.getImagesLib import *
import sys, math, ee
from datetime import datetime
# -------------------------------------------------------------------------
# Image and array manipulation
# ------------------------------------------------------------------------
lossYearPalette = "ffffe5,fff7bc,fee391,fec44f,fe9929,ec7014,cc4c02".split(",")
lossMagPalette = "D00,F5DEB3".split(",")
gainYearPalette = "c5ee93,00a398".split(",")
gainMagPalette = "F5DEB3,006400".split(",")
changeDurationPalette = "BD1600,E2F400,0C2780".split(",")
######################################################################
# Helper to multiply image
def multBands(img, distDir, by=1):
out = img.multiply(ee.Image(distDir).multiply(by))
out = ee.Image(out.copyProperties(img, ["system:time_start"]).copyProperties(img))
return out
######################################################################
# Default run params for LandTrendr
default_lt_run_params = {
"maxSegments": 6,
"spikeThreshold": 0.9,
"vertexCountOvershoot": 3,
"preventOneYearRecovery": True,
"recoveryThreshold": 0.25,
"pvalThreshold": 0.05,
"bestModelProportion": 0.75,
"minObservationsNeeded": 6,
}
######################################################################
# Helper to multiply new baselearner format values (LandTrendr & Verdet) by the appropriate amount when importing
# Duration is the only band that does not get multiplied by 0.0001 upon import.
def LT_VT_multBands(img):
fitted = img.select(".*_fitted").multiply(0.0001)
slope = img.select(".*_slope").multiply(0.0001)
diff = img.select(".*_diff").multiply(0.0001)
mag = img.select(".*_mag").multiply(0.0001)
dur = img.select(".*_dur")
out = dur.addBands(fitted).addBands(slope).addBands(diff).addBands(mag)
out = out.copyProperties(img, ["system:time_start"]).copyProperties(img)
return out
def addToImage(img, howMuch):
out = img.add(ee.Image(howMuch))
out = ee.Image(out.copyProperties(img, ["system:time_start"]).copyProperties(img))
return out
# Used when masking out pixels that don't have sufficient data for Landtrendr and Verdet
def nullFinder(img, countMask):
m = img.mask()
# Allow areas with insufficient data to be included, but then set to a dummy value for later masking
m = m.Or(countMask.Not())
img = img.mask(m)
img = img.where(countMask.Not(), -32768)
return img
# Function to convert an image array object to collection
def arrayToTimeSeries(tsArray, yearsArray, possibleYears, bandName):
# Set up dummy image for handling null values
noDateValue = -32768
dummyImage = ee.Image(noDateValue).toArray()
# Iterate across years
def applyMasks(yr):
yr = ee.Number(yr)
# Pull out given year
yrMask = yearsArray.eq(yr)
# Mask array for that given year
masked = tsArray.arrayMask(yrMask)
# Find null pixels
l = masked.arrayLength(0)
# Fill null values and convert to regular image
masked = masked.where(l.eq(0), dummyImage).arrayGet([-1])
# Remask nulls
masked = masked.updateMask(masked.neq(noDateValue)).rename([bandName]).set("system:time_start", ee.Date.fromYMD(yr, 6, 1).millis())
return masked
tsC = possibleYears.map(lambda yr: applyMasks(yr))
return ee.ImageCollection(tsC)
#########################################################################################################
###### GREATEST DISTURBANCE EXTRACTION FUNCTIONS #####
#########################################################################################################
# ----- function to extract greatest disturbance based on spectral delta between vertices
def extractDisturbance(lt, distDir, params, mmu):
# select only the vertices that represents a change
vertexMask = lt.arraySlice(0, 3, 4) # get the vertex - yes(1)/no(0) dimension
vertices = lt.arrayMask(vertexMask) # convert the 0's to masked
# numberOfVertices = vertexMask.arrayReduce(ee.Reducer.sum(),[1]).arrayProject([1]).arrayFlatten([['vertexCount']])
# secondMask = numberOfVertices.gte(3)
# thirdMask = numberOfVertices.gte(4)
# Map.addLayer(numberOfVertices,{min:2,max:4},'number of vertices',false)
# construct segment start and end point years and index values
left = vertices.arraySlice(1, 0, -1) # slice out the vertices as the start of segments
right = vertices.arraySlice(1, 1, None) # slice out the vertices as the end of segments
startYear = left.arraySlice(0, 0, 1) # get year dimension of LT data from the segment start vertices
startVal = left.arraySlice(0, 2, 3) # get spectral index dimension of LT data from the segment start vertices
endYear = right.arraySlice(0, 0, 1) # get year dimension of LT data from the segment end vertices
endVal = right.arraySlice(0, 2, 3) # get spectral index dimension of LT data from the segment end vertices
dur = endYear.subtract(startYear) # subtract the segment start year from the segment end year to calculate the duration of segments
mag = endVal.subtract(startVal) # substract the segment start index value from the segment end index value to calculate the delta of segments
# concatenate segment start year, delta, duration, and starting spectral index value to an array
distImg = ee.Image.cat([startYear.add(1), mag, dur, startVal.multiply(-1)]).toArray(
0
) # make an image of segment attributes - multiply by the distDir parameter to re-orient the spectral index if it was flipped for segmentation - do it here so that the subtraction to calculate segment delta in the above line is consistent - add 1 to the detection year, because the vertex year is not the first year that change is detected, it is the following year
# sort the segments in the disturbance attribute image delta by spectral index change delta
distImgSorted = distImg.arraySort(mag.multiply(-1))
# slice out the first (greatest) delta
tempDistImg1 = distImgSorted.arraySlice(1, 0, 1).unmask(ee.Image(ee.Array([[0], [0], [0], [0]])))
tempDistImg2 = distImgSorted.arraySlice(1, 1, 2).unmask(ee.Image(ee.Array([[0], [0], [0], [0]])))
tempDistImg3 = distImgSorted.arraySlice(1, 2, 3).unmask(ee.Image(ee.Array([[0], [0], [0], [0]])))
# make an image from the array of attributes for the greatest disturbance
finalDistImg1 = tempDistImg1.arrayProject([0]).arrayFlatten([["yod", "mag", "dur", "preval"]])
finalDistImg2 = tempDistImg2.arrayProject([0]).arrayFlatten([["yod", "mag", "dur", "preval"]])
finalDistImg3 = tempDistImg3.arrayProject([0]).arrayFlatten([["yod", "mag", "dur", "preval"]])
# filter out disturbances based on user settings
def filterDisturbances(finalDistImg):
# threshold = ee.Image(finalDistImg.select(['dur'])) # get the disturbance band out to apply duration dynamic disturbance magnitude threshold
# .multiply((params.tree_loss20 - params.tree_loss1) / 19.0) # ...
# .add(params.tree_loss1) # ...interpolate the magnitude threshold over years between a 1-year mag thresh and a 20-year mag thresh
# .lte(finalDistImg.select(['mag'])) # ...is disturbance less then equal to the interpolated, duration dynamic disturbance magnitude threshold
# .and(finalDistImg.select(['mag']).gt(0)) # and is greater than 0
# .and(finalDistImg.select(['preval']).gt(params.pre_val))
longTermDisturbance = finalDistImg.select(["dur"]).gte(15)
longTermThreshold = finalDistImg.select(["mag"]).gte(params["tree_loss20"]).And(longTermDisturbance)
threshold = finalDistImg.select(["mag"]).gte(params["tree_loss1"])
return finalDistImg.updateMask(threshold.Or(longTermThreshold))
finalDistImg1 = filterDisturbances(finalDistImg1)
finalDistImg2 = filterDisturbances(finalDistImg2)
finalDistImg3 = filterDisturbances(finalDistImg3)
def applyMMU(finalDistImg):
# patchify based on disturbances having the same year of detection
# count the number of pixel in a candidate patch
mmuPatches = finalDistImg.select(["yod.*"]).int16().connectedPixelCount(mmu, True).gte(mmu) # are the the number of pixels per candidate patch greater than user-defined minimum mapping unit?
return finalDistImg.updateMask(mmuPatches) # mask the pixels/patches that are less than minimum mapping unit
# patchify the remaining disturbance pixels using a minimum mapping unit
if mmu > 1:
print("Applying mmu:" + str(mmu) + " to LANDTRENDR heuristic outputs")
finalDistImg1 = applyMMU(finalDistImg1)
finalDistImg2 = applyMMU(finalDistImg2)
finalDistImg3 = applyMMU(finalDistImg3)
return finalDistImg1.addBands(finalDistImg2).addBands(finalDistImg3) # return the filtered greatest disturbance attribute image
# -------------------------------------------------------------------------
# LandTrendr Code
# ------------------------------------------------------------------------
# Landtrendr code taken from users/emaprlab/public
###### UNPACKING LT-GEE OUTPUT STRUCTURE FUNCTIONS #####
# ----- FUNCTION TO EXTRACT VERTICES FROM LT RESULTS AND STACK BANDS -----
def getLTvertStack(LTresult, run_params):
emptyArray = [] # make empty array to hold another array whose length will vary depending on maxSegments parameter
vertLabels = [] # make empty array to hold band names whose length will vary depending on maxSegments parameter
# iString # initialize variable to hold vertex number
# loop through the maximum number of vertices in segmentation and fill empty arrays:
for i in range(1, run_params["maxSegments"] + 2):
vertLabels.append("vert_" + str(i)) # define vertex number as string, make a band name for given vertex
emptyArray.append(0) # fill in emptyArray
zeros = ee.Image(
ee.Array(
[
emptyArray, # make an image to fill holes in result 'LandTrendr' array where vertices found is not equal to maxSegments parameter plus 1
emptyArray,
emptyArray,
]
)
)
lbls = [
["yrs_", "src_", "fit_"],
vertLabels,
] # labels for 2 dimensions of the array that will be cast to each other in the final step of creating the vertice output
vmask = LTresult.arraySlice(
0, 3, 4
) # slices out the 4th row of a 4 row x N col (N = number of years in annual stack) matrix, which identifies vertices - contains only 0s and 1s, where 1 is a vertex (referring to spectral-temporal segmentation) year and 0 is not
# Line by line comments taken from call below
# .arrayMask uses the sliced out isVert row as a mask to only include vertice in this data - after this a pixel will only contain as many "bands" are there are vertices for that pixel - min of 2 to max of 7.
# .arraySlice()...from the vertOnly data subset slice out the vert year row, raw spectral row, and fitted spectral row
# .addBands() # ...adds the 3 row x 7 col 'zeros' matrix as a band to the vertOnly array - this is an intermediate step to the goal of filling in the vertOnly data so that there are 7 vertice slots represented in the data - right now there is a mix of lengths from 2 to 7
# .toArray() # ...concatenates the 3 row x 7 col 'zeros' matrix band to the vertOnly data so that there are at least 7 vertice slots represented - in most cases there are now > 7 slots filled but those will be truncated in the next step
# .arraySlice()...before this line runs the array has 3 rows and between 9 and 14 cols depending on how many vertices were found during segmentation for a given pixel. this step truncates the cols at 7 (the max verts allowed) so we are left with a 3 row X 7 col array
# .arrayFlatten()...this takes the 2-d array and makes it 1-d by stacking the unique sets of rows and cols into bands. there will be 7 bands (vertices) for vertYear, followed by 7 bands (vertices) for rawVert, followed by 7 bands (vertices) for fittedVert, according to the 'lbls' list
ltVertStack = LTresult.arrayMask(vmask).arraySlice(0, 0, 3).addBands(zeros).toArray(1).arraySlice(1, 0, run_params["maxSegments"] + 1).arrayFlatten(lbls, "")
return ltVertStack # return the stack
# Adapted version for converting sorted array to image
def getLTStack(LTresult, maxVertices, bandNames):
nBands = len(bandNames)
emptyArray = [] # make empty array to hold another array whose length will vary depending on maxSegments parameter
vertLabels = [] # make empty array to hold band names whose length will vary depending on maxSegments parameter
for i in range(1, maxVertices + 1): # loop through the maximum number of vertices in segmentation and fill empty arrays
vertLabels.append(str(i)) # make a band name for given vertex
emptyArray.append(-32768) # fill in emptyArray
# Set up empty array list
emptyArrayList = []
for i in range(1, nBands + 1):
emptyArrayList.append(emptyArray)
zeros = ee.Image(ee.Array(emptyArrayList)) # make an image to fill holes in result 'LandTrendr' array where vertices found is not equal to maxSegments parameter plus 1
lbls = [
bandNames,
vertLabels,
] # labels for 2 dimensions of the array that will be cast to each other in the final step of creating the vertice output
# slices out the 4th row of a 4 row x N col (N = number of years in annual stack) matrix, which identifies vertices - contains only 0s and 1s, where 1 is a vertex (referring to spectral-temporal segmentation) year and 0 is not
ltVertStack = LTresult.addBands(zeros).toArray(1).arraySlice(1, 0, maxVertices).arrayFlatten(lbls, "")
# Line by line comments taken from call above
# LTresult: # uses the sliced out isVert row as a mask to only include vertice in this data - after this a pixel will only contain as many "bands" are there are vertices for that pixel - min of 2 to max of 7.
# .addBands(zeros): # ...adds the 3 row x 7 col 'zeros' matrix as a band to the vertOnly array - this is an intermediate step to the goal of filling in the vertOnly data so that there are 7 vertice slots represented in the data - right now there is a mix of lengths from 2 to 7
# .toArray(1): # ...concatenates the 3 row x 7 col 'zeros' matrix band to the vertOnly data so that there are at least 7 vertice slots represented - in most cases there are now > 7 slots filled but those will be truncated in the next step
# .arraySlice(1, 0, maxVertices): # ...before this line runs the array has 3 rows and between 9 and 14 cols depending on how many vertices were found during segmentation for a given pixel. this step truncates the cols at 7 (the max verts allowed) so we are left with a 3 row X 7 col array
# .arrayFlatten(lbls, ''): # ...this takes the 2-d array and makes it 1-d by stacking the unique sets of rows and cols into bands. there will be 7 bands (vertices) for vertYear, followed by 7 bands (vertices) for rawVert, followed by 7 bands (vertices) for fittedVert, according to the 'lbls' list
return ltVertStack.updateMask(ltVertStack.neq(-32768))
############################################################################################################
# Function to remove non vertex info from raw image array format from LandTrendr.
# E.g. if an output is [1985,1990,2020],[500,450,320],[500,510,320],[1,0,1], the output will be [1985,2020],[500,320]
# This is the equivalent to the vert stack functions by keeps outputs in image array format
def rawLTToVertices(rawLT, indexName=None, multBy=10000, vertexNoData=-32768):
if indexName != None:
try:
distDir = changeDirDict[indexName]
except:
distDir = -1
else:
distDir = -1
ltArray = rawLT.select(["LandTrendr"])
rmse = rawLT.select(["rmse"]).multiply(multBy)
vertices = ltArray.arraySlice(0, 3, 4)
ltArray = ltArray.arrayMask(vertices)
minObservationsNeededMask = ltArray.arraySlice(0, 1, 2).arraySlice(1, 0, 1).neq(vertexNoData).arrayProject([0]).arrayFlatten([["test"]])
ltArray = ltArray.arrayMask(ee.Image(ee.Array([[1], [0], [1], [0]])))
l = ltArray.arrayLength(1)
multImg = ee.Image(ee.Array([[1], [distDir * multBy]])).arrayRepeat(1, l)
ltArray = ltArray.multiply(multImg)
return ltArray.addBands(rmse).updateMask(minObservationsNeededMask)
############################################################################################################
# Function to wrap landtrendr processing
def landtrendrWrapper(
processedComposites,
startYear,
endYear,
indexName,
distDir,
run_params,
distParams,
mmu,
):
# startYear = 1984#ee.Date(ee.Image(processedComposites.first()).get('system:time_start')).get('year').getInfo()
# endYear = 2017#ee.Date(ee.Image(processedComposites.sort('system:time_start',false).first()).get('system:time_start')).get('year').getInfo()
noDataValue = 32768
if distDir == 1:
noDataValue = -1.0 * noDataValue
# ----- RUN LANDTRENDR -----
ltCollection = processedComposites.select(indexName).map(lambda img: ee.Image(multBands(img, distDir, 1))) # .unmask(noDataValue)
# Map.addLayer(ltCollection,{},'ltCollection',false)
run_params["timeSeries"] = ltCollection # add LT collection to the segmentation run parameter object
lt = ee.Algorithms.TemporalSegmentation.LandTrendr(**run_params) # run LandTrendr spectral temporal segmentation algorithm
#########################################################################################################
###### RUN THE GREATEST DISTURBANCE EXTRACT FUNCTION #####
#########################################################################################################
# run the dist extract function
distImg = extractDisturbance(lt.select("LandTrendr"), distDir, distParams, mmu)
distImgBandNames = distImg.bandNames()
distImgBandNames = distImgBandNames.map(lambda bn: ee.String(indexName).cat("_").cat(bn))
distImg = distImg.rename(distImgBandNames)
# Convert to collection
rawLT = lt.select([0])
ltYear = rawLT.arraySlice(0, 0, 1).arrayProject([1])
ltFitted = rawLT.arraySlice(0, 2, 3).arrayProject([1])
if distDir == -1:
ltFitted = ltFitted.multiply(-1)
fittedCollection = arrayToTimeSeries(ltFitted, ltYear, ee.List.sequence(startYear, endYear), "LT_Fitted_" + indexName)
# Convert to single image
vertStack = getLTvertStack(rawLT, run_params)
return [lt, distImg, fittedCollection, vertStack]
#########################################################################################################
#########################################################################################################
# Function to join raw time series with fitted time series from LANDTRENDR
# Takes the rawTs as an imageCollection, lt is the first band of the output from LANDTRENDR, and the distDir
# is the direction of change for a loss in vegeation for the chosen band/index
def getRawAndFittedLT(rawTs, lt, startYear, endYear, indexName="Band", distDir=-1):
# Pop off years and fitted values
ltYear = lt.arraySlice(0, 0, 1).arrayProject([1])
ltFitted = lt.arraySlice(0, 2, 3).arrayProject([1])
# Flip fitted values if needed
if distDir == -1:
ltFitted = ltFitted.multiply(-1)
# Convert array to an imageCollection
fittedCollection = arrayToTimeSeries(ltFitted, ltYear, ee.List.sequence(startYear, endYear), "LT_Fitted_" + indexName)
# Join raw time series with fitted
joinedTS = joinCollections(rawTs, fittedCollection)
return joinedTS
#########################################################################################################
# 2023 rework to run LT in its most simple form
# Gets rid of handling :
# Insufficient obs counts, no data handling,
# Exporting of band stack format (assumes image array format for output)
# Function to mask out the non vertex and original values
# Simplified version of rawLTToVertices
def simpleRawLTToVertices(rawLT):
# Pull off rmse
rmse = rawLT.select(["rmse"])
# Mask out non vertex values to use less storage space
ltArray = rawLT.select(["LandTrendr"])
vertices = ltArray.arraySlice(0, 3, 4)
ltArray = ltArray.arrayMask(vertices)
# Mask out all but the year and vertex fited values (get rid of the raw and vertex rows)
return ltArray.arrayMask(ee.Image(ee.Array([[1], [0], [1], [0]]))).addBands(rmse)
###########################################################
# Function to multiply the LandTrendr RMSE and vertex array
# Assumes LTMaskNonVertices has already been run
def multLT(rawLT, multBy):
# Pull off rmse
rmse = rawLT.select(["rmse"]).multiply(multBy).abs()
# Ensure only the LandTrendr array output
ltArray = rawLT.select(["LandTrendr"])
# Form an image to multiply by
l = ltArray.arrayLength(1)
multImg = ee.Image(ee.Array([[1], [multBy]])).arrayRepeat(1, l)
return ltArray.multiply(multImg).addBands(rmse)
###########################################################
# Function to simplify LandTrendr output for exporting
def LTExportPrep(rawLT, multBy=10000):
rawLT = simpleRawLTToVertices(rawLT)
rawLT = multLT(rawLT, multBy)
return rawLT
###########################################################
# New function 11/23 to simplify running of LandTrendr
# and prepping outputs for export
def runLANDTRENDR(ts, bandName, run_params=None):
# Get single band time series and set its direction so that a loss in veg/moisture is going up
ts = ts.select([bandName])
try:
distDir = changeDirDict[bandName]
except:
distDir = -1
ts = ts.map(lambda img: multBands(img, 1, distDir))
# Set up run params
if run_params == None:
run_params = default_lt_run_params
run_params["timeSeries"] = ts
# Run LANDTRENDR
rawLT = ee.Algorithms.TemporalSegmentation.LandTrendr(**run_params)
# Get vertex-only fitted values and multiply the fitted values
return LTExportPrep(rawLT, distDir).set("band", bandName).set("run_params", run_params)
###########################################################
# Pulled from simpleLANDTRENDR below to take the lossGain dictionary and prep it for export
def LTLossGainExportPrep(lossGainDict, indexName="Bn", multBy=10000):
lossStack = lossGainDict["lossStack"]
gainStack = lossGainDict["gainStack"]
# Convert to byte/int16 if possible to save space
lossThematic = lossStack.select([".*_yr_.*"]).int16().addBands(lossStack.select([".*_dur_.*"]).byte())
lossContinuous = lossStack.select([".*_mag_.*", ".*_slope_.*"]).multiply(multBy)
if abs(multBy) == 10000:
lossContinuous = lossContinuous.int16()
lossStack = lossThematic.addBands(lossContinuous)
gainThematic = gainStack.select([".*_yr_.*"]).int16().addBands(gainStack.select([".*_dur_.*"]).byte())
gainContinuous = gainStack.select([".*_mag_.*", ".*_slope_.*"]).multiply(multBy)
if abs(multBy) == 10000:
gainContinuous = gainContinuous.int16()
gainStack = gainThematic.addBands(gainContinuous)
outStack = lossStack.addBands(gainStack)
# Add indexName to bandnames
bns = outStack.bandNames()
outBns = bns.map(lambda bn: ee.String(indexName).cat("_LT_").cat(bn))
return outStack.rename(outBns)
###########################################################
# Pulled from simpleLANDTRENDR below to take prepped (must run LTLossGainExportPrep first) lossGain stack and view it
def addLossGainToMap(
lossGainStack,
startYear,
endYear,
lossMagMin=-8000,
lossMagMax=-2000,
gainMagMin=1000,
gainMagMax=8000,
indexName=None,
howManyToPull=None,
):
if indexName == None or howManyToPull == None:
bns = lossGainStack.bandNames().getInfo()
indexName = bns[0].split("_")[0]
howManyToPull = list(set([int(bn.split("_")[-1]) for bn in bns]))
else:
howManyToPull = list(range(1, howManyToPull + 1))
# Set up viz params
vizParamsLossYear = {"min": startYear, "max": endYear, "palette": lossYearPalette}
vizParamsLossMag = {"min": lossMagMin, "max": lossMagMax, "palette": lossMagPalette}
vizParamsGainYear = {"min": startYear, "max": endYear, "palette": gainYearPalette}
vizParamsGainMag = {"min": gainMagMin, "max": gainMagMax, "palette": gainMagPalette}
vizParamsDuration = {
"min": 1,
"legendLabelLeftAfter": "year",
"legendLabelRightAfter": "years",
"max": 5,
"palette": changeDurationPalette,
}
for i in howManyToPull:
lossStackI = lossGainStack.select([".*_loss_.*_" + str(i)])
lossStackYrMaskI = lossStackI.select([".*_loss_yr.*"]).gte(startYear).And(lossStackI.select([".*_loss_yr.*"]).lte(endYear))
lossStackI = lossStackI.updateMask(lossStackYrMaskI)
gainStackI = lossGainStack.select([".*_gain_.*_" + str(i)])
gainStackYrMaskI = gainStackI.select([".*_gain_yr.*"]).gte(startYear).And(gainStackI.select([".*_gain_yr.*"]).lte(endYear))
gainStackI = gainStackI.updateMask(gainStackYrMaskI)
showLossYear = False
if i == 1:
showLossYear = True
iString = f"{i} "
if howManyToPull == [1]:
iString = ""
Map.addLayer(
lossStackI.select([".*_loss_yr.*"]),
vizParamsLossYear,
"LandTrendr " + iString + indexName + " Loss Year",
showLossYear,
)
Map.addLayer(
lossStackI.select([".*_loss_mag.*"]),
vizParamsLossMag,
"LandTrendr " + iString + indexName + " Loss Magnitude",
False,
)
Map.addLayer(
lossStackI.select([".*_loss_dur.*"]),
vizParamsDuration,
"LandTrendr " + iString + indexName + " Loss Duration",
False,
)
Map.addLayer(
gainStackI.select([".*_gain_yr.*"]),
vizParamsGainYear,
"LandTrendr " + iString + indexName + " Gain Year",
False,
)
Map.addLayer(
gainStackI.select([".*_gain_mag.*"]),
vizParamsGainMag,
"LandTrendr " + iString + indexName + " Gain Magnitude",
False,
)
Map.addLayer(
gainStackI.select([".*_gain_dur.*"]),
vizParamsDuration,
"LandTrendr " + iString + indexName + " Gain Duration",
False,
)
#########################################################################################################
# Function for running LT, thresholding the segments for both loss and gain, sort them, and convert them to an image stack
# July 2019 LSC: replaced some parts of workflow with functions in changeDetectionLib
def simpleLANDTRENDR(
ts,
startYear,
endYear,
indexName="NBR",
run_params=None,
lossMagThresh=-0.15,
lossSlopeThresh=-0.1,
gainMagThresh=0.1,
gainSlopeThresh=0.1,
slowLossDurationThresh=3,
chooseWhichLoss="largest",
chooseWhichGain="largest",
addToMap=True,
howManyToPull=2,
multBy=10000,
):
"""
Takes annual time series input data, properly sets it up for LandTrendr, runs LandTrendr, and provides both a compressed vertex-only format output as well as a basic change detection output.
"""
if run_params == None:
run_params = default_lt_run_params
ts = ts.select(indexName)
lt = runLANDTRENDR(ts, indexName, run_params)
try:
distDir = changeDirDict[indexName]
except:
distDir = -1
ltTS = simpleLTFit(
lt,
startYear,
endYear,
indexName=indexName,
arrayMode=True,
maxSegs=run_params["maxSegments"],
)
joinedTS = joinCollections(ts, ltTS.select([".*_LT_fitted"]))
# Flip the output back around if needed to do change detection
ltRawPositiveForChange = multLT(lt, distDir)
# Take the LT output and detect change
lossGainDict = convertToLossGain(
ltRawPositiveForChange,
"arrayLandTrendr",
lossMagThresh,
lossSlopeThresh,
gainMagThresh,
gainSlopeThresh,
slowLossDurationThresh,
chooseWhichLoss,
chooseWhichGain,
howManyToPull,
)
# Prep loss gain dictionary into multi-band image ready for exporting
lossGainStack = LTLossGainExportPrep(lossGainDict, indexName, multBy)
# Add the change outputs to the map if specified to do so
if addToMap:
Map.addLayer(joinedTS, {"opacity": 0}, "Raw and Fitted Time Series", True)
addLossGainToMap(
lossGainStack,
startYear,
endYear,
(lossMagThresh - 0.7) * multBy,
lossMagThresh * multBy,
gainMagThresh * multBy,
(gainMagThresh + 0.7) * multBy,
indexName,
howManyToPull,
)
return [multLT(lt, multBy), lossGainStack]
#########################################################################################################
#########################################################################################################
# Function to prep data following our workflows. Will have to run Landtrendr and convert to stack after.
def prepTimeSeriesForLandTrendr(ts, indexName, run_params):
maxSegments = ee.Number(run_params["maxSegments"])
# Get single band time series and set its direction so that a loss in veg is going up
ts = ts.select([indexName])
distDir = changeDirDict[indexName]
tsT = ts.map(lambda img: multBands(img, 1, distDir))
# Find areas with insufficient data to run LANDTRENDR
countMask = tsT.count().unmask().gte(run_params["minObservationsNeeded"])
# Mask areas identified by countMask
tsT = tsT.map(lambda img: nullFinder(img, countMask))
run_params["timeSeries"] = tsT
countMask = countMask.rename("insufficientDataMask")
prepDict = {"run_params": run_params, "countMask": countMask}
return prepDict
# This function outputs Landtrendr as a vertical stack and adds properties about the run.
def LANDTRENDRVertStack(composites, indexName, run_params, startYear, endYear):
creationDate = datetime.strftime(datetime.now(), "%Y%m%d")
composites = composites.filter(ee.Filter.calendarRange(startYear, endYear, "year"))
# Prep Time Series and put into run parameters
prepDict = prepTimeSeriesForLandTrendr(composites, indexName, run_params)
run_params = prepDict["run_params"]
countMask = prepDict["countMask"]
# Run LANDTRENDR
rawLt = ee.Algorithms.TemporalSegmentation.LandTrendr(**run_params)
# Map.addLayer(rawLt,{},'raw lt {}-{}'.format(startYear,endYear))
# Convert to image stack
lt = rawLt.select([0])
ltStack = ee.Image(getLTvertStack(lt, run_params)).updateMask(countMask)
ltStack = ltStack.select("yrs.*").addBands(ltStack.select("fit.*"))
rmse = rawLt.select([1]).rename("rmse")
ltStack = ltStack.addBands(rmse)
# Undo distdir change done in prepTimeSeriesForLandtrendr()
ltStack = applyDistDir_vertStack(ltStack, changeDirDict[indexName], "landtrendr")
# Set Properties
ltStack = ltStack.set(
{
"startYear": startYear,
"endYear": endYear,
"band": indexName,
"creationDate": creationDate,
"maxSegments": run_params["maxSegments"],
"spikeThreshold": run_params["spikeThreshold"],
"vertexCountOvershoot": run_params["vertexCountOvershoot"],
"recoveryThreshold": run_params["recoveryThreshold"],
"pvalThreshold": run_params["pvalThreshold"],
"bestModelProportion": run_params["bestModelProportion"],
"minObservationsNeeded": run_params["minObservationsNeeded"],
}
)
return ee.Image(ltStack)
#############################################/
# Function for running LANDTRENDR and converting output to annual image collection
# with the fitted value, duration, magnitude, slope, and diff for the segment for each given year
def LANDTRENDRFitMagSlopeDiffCollection(ts, indexName, run_params):
startYear = ee.Date(ts.first().get("system:time_start")).get("year")
endYear = ee.Date(ts.sort("system:time_start", False).first().get("system:time_start")).get("year")
# Run LandTrendr and convert to VertStack format
ltStack = ee.Image(LANDTRENDRVertStack(ts, indexName, run_params, startYear, endYear))
ltStack = ee.Image(LT_VT_vertStack_multBands(ltStack, "landtrendr", 10000))
# Convert to durFitMagSlope format
durFitMagSlope = convertStack_To_DurFitMagSlope(ltStack, "LT")
return durFitMagSlope
# ----------------------------------------------------------------------------------------------------
# Functions for both Verdet and Landtrendr
# ----------------------------------------------------------------------------------------------------
# Helper to multiply new baselearner format values (LandTrendr & Verdet) by the appropriate amount when importing
# Duration is the only band that does not get multiplied by 0.0001 upon import.
# img = landtrendr or verdet image in fitMagDurSlope format
# multBy = 10000 (to prep for export) or 0.0001 (after import)
def LT_VT_multBands(img, multBy):
fitted = img.select(".*_fitted").multiply(multBy)
slope = img.select(".*_slope").multiply(multBy)
diff = img.select(".*_diff").multiply(multBy)
mag = img.select(".*_mag").multiply(multBy)
dur = img.select(".*_dur")
out = dur.addBands(fitted).addBands(slope).addBands(diff).addBands(mag)
out = out.copyProperties(img, ["system:time_start"]).copyProperties(img)
return out
# Function to apply the Direction of a decrease in photosynthetic vegetation to Landtrendr or Verdet vertStack format
# img = vertStack image for one band, e.g. "NBR"
# verdet_or_landtrendr = 'verdet' or 'landtrendr'
# distDir = from getImagesLib.changeDirDict
def applyDistDir_vertStack(stack, distDir, verdet_or_landtrendr):
years = stack.select("yrs.*")
fitted = stack.select("fit.*").multiply(distDir)
out = years.addBands(fitted)
if verdet_or_landtrendr == "landtrendr":
rmse = stack.select("rmse")
out = out.addBands(rmse)
out = out.copyProperties(stack, ["system:time_start"]).copyProperties(stack)
return out
# Helper to multiply vertStack bands by the appropriate amount before exporting (multBy = 10000)
# or after importing (multBy = 0.0001)
# img = vertStack image for one band, e.g. "NBR"
# verdet_or_landtrendr = 'verdet' or 'landtrendr'
# multBy = 10000 or 0.0001
def LT_VT_vertStack_multBands(img, verdet_or_landtrendr, multBy):
years = img.select("yrs.*")
fitted = img.select("fit.*").multiply(multBy)
out = years.addBands(fitted)
if verdet_or_landtrendr == "landtrendr":
rmse = img.select("rmse").multiply(multBy)
out = out.addBands(rmse)
out = out.copyProperties(img, ["system:time_start"]).copyProperties(img)
return out
# Simplified method to convert LANDTRENDR stack to annual collection of
# Duration, fitted, magnitude, slope, and diff
# Improved handling of start year delay found in older method
def simpleLTFit(ltStack, startYear, endYear, indexName="bn", arrayMode=True, maxSegs=6, multBy=1):
indexName = ee.String(indexName)
# Set up output band names
outBns = [
indexName.cat("_LT_dur"),
indexName.cat("_LT_fitted"),
indexName.cat("_LT_mag"),
indexName.cat("_LT_slope"),
indexName.cat("_LT_diff"),
]
# Separate years and fitted values of vertices
if arrayMode:
ltStack = ltStack.select([0])
zeros = ee.Image(ee.Array([0]).repeat(0, maxSegs + 2))
yrBns = ["yrs_{}".format(i) for i in range(1, maxSegs + 2)]
fitBns = ["fit_{}".format(i) for i in range(1, maxSegs + 2)]
yrs = ltStack.arraySlice(0, 0, 1).arrayProject([1]).arrayCat(zeros, 0).arraySlice(0, 0, maxSegs + 1).arrayFlatten([yrBns]).selfMask()
fit = ltStack.arraySlice(0, 1, 2).arrayProject([1]).arrayCat(zeros, 0).arraySlice(0, 0, maxSegs + 1).arrayFlatten([fitBns]).updateMask(yrs.mask())
else:
yrs = ltStack.select("yrs_.*").selfMask()
fit = ltStack.select("fit_.*").updateMask(yrs.mask())
fit = fit.multiply(multBy)
# Find the first and last vertex years
isStartYear = yrs.reduce(ee.Reducer.firstNonNull())
isEndYear = yrs.reduce(ee.Reducer.lastNonNull())
blankMask = yrs.gte(100000)
# Iterate across each year to find the values for that year
def getYearValues(yr):
yr = ee.Number(yr)
# Find the segment the year belongs to
# Handle whether the year is the same as the first vertex year
startYrMask = blankMask
startYrMask = startYrMask.where(isStartYear.eq(yr), yrs.lte(yr))
startYrMask = startYrMask.where(isStartYear.lt(yr), yrs.lt(yr))
# Handle whether the year is the same as the last vertex year
endYrMask = blankMask
endYrMask = endYrMask.where(isStartYear.eq(yr), yrs.gt(yr))
endYrMask = endYrMask.where(isStartYear.lt(yr), yrs.gte(yr))
# Get fitted values for the vertices segment the year is within
fitStart = fit.updateMask(startYrMask).reduce(ee.Reducer.lastNonNull())
fitEnd = fit.updateMask(endYrMask).reduce(ee.Reducer.firstNonNull())
# Get start and end year for the vertices segment the year is within
yearStart = yrs.updateMask(startYrMask).reduce(ee.Reducer.lastNonNull())
yearEnd = yrs.updateMask(endYrMask).reduce(ee.Reducer.firstNonNull())
# Get the difference and duration of the segment
segDiff = fitEnd.subtract(fitStart)
segDur = yearEnd.subtract(yearStart)
# Get the varius annual derivatives
tDiff = ee.Image(yr).subtract(yearStart)
segSlope = segDiff.divide(segDur)
fitDiff = segSlope.multiply(tDiff)
fitted = fitStart.add(fitDiff)
formatted = ee.Image.cat([segDur, fitted, segDiff, segSlope, fitDiff]).rename(outBns).set("system:time_start", ee.Date.fromYMD(yr, 6, 1).millis())
return formatted
out = ee.ImageCollection(ee.List.sequence(startYear, endYear).map(lambda yr: getYearValues(yr)))
return out
# Wrapper function to iterate across multiple LT band/index values
def batchSimpleLTFit(
ltStacks,
startYear,
endYear,
indexNames=None,
bandPropertyName="band",
arrayMode=True,
maxSegs=6,
multBy=1,
mosaicReducer=ee.Reducer.lastNonNull(),
):
# Get band/index names if not provided
if indexNames == None:
indexNames = ltStacks.aggregate_histogram(bandPropertyName).keys().getInfo()
# Iterate across each band/index and get the fitted, mag, slope, etc
lt_fit = None
for bn in indexNames:
ltt = ltStacks.filter(ee.Filter.eq(bandPropertyName, bn))
bns = ltt.first().bandNames()
ltt = ltt.reduce(mosaicReducer).rename(bns)
if lt_fit == None:
lt_fit = simpleLTFit(ltt, startYear, endYear, bn, arrayMode, maxSegs, multBy)
else:
lt_fit = joinCollections(
lt_fit,
simpleLTFit(ltt, startYear, endYear, bn, arrayMode, maxSegs, multBy),
False,
)
return lt_fit
# Function to parse stack from LANDTRENDR or VERDET into image collection
# July 2019 LSC: multiply(distDir) and multiply(10000) now take place outside of this function,
# but must be done BEFORE stack is passed to this function
def fitStackToCollection(stack, maxSegments, startYear, endYear):
# Parse into annual fitted, duration, magnitude, and slope images
# Iterate across each possible segment and find its fitted end value, duration, magnitude, and slope
def segmentLooper(i):
i = ee.Number(i)
# Set up slector for left and right side of segments
stringSelectLeft = ee.String(".*_").cat(i.byte().format())
stringSelectRight = ee.String(".*_").cat((i.add(1)).byte().format())
# Get the left and right bands into separate images
stackLeft = stack.select([stringSelectLeft])
stackRight = stack.select([stringSelectRight])
# Select off the year bands
segYearsLeft = stackLeft.select(["yrs_.*"]).rename(["year_left"])
segYearsRight = stackRight.select(["yrs_.*"]).rename(["year_right"])
# Select off the fitted bands and flip them if they were flipped for use in LT
segFitLeft = stackLeft.select(["fit_.*"]).rename(["fitted"])
segFitRight = stackRight.select(["fit_.*"]).rename(["fitted"])
# Comput duration, magnitude, and then slope
segDur = segYearsRight.subtract(segYearsLeft).rename(["dur"])
segMag = segFitRight.subtract(segFitLeft).rename(["mag"])
segSlope = segMag.divide(segDur).rename(["slope"])
# Iterate across each year to see if the year is within a given segment
# All annualizing is done from the right vertex backward
# The first year of the time series is inserted manually with an if statement
# Ex: If the first segment goes from 1984-1990 and the second from 1990-1997, the duration, magnitude,and slope
# values from the first segment will be given to 1984-1990, while the second segment (and any subsequent segment)
# the duration, magnitude, and slope values will be given from 1991-1997
def annualizer(yr):
yr = ee.Number(yr)
yrImage = ee.Image(yr)
# Find if the year is the first and include the left year if it is
# Otherwise, do not include the left year
yrImage = ee.Algorithms.If(
yr.eq(startYear),
yrImage.updateMask(segYearsLeft.lte(yr).And(segYearsRight.gte(yr))),
yrImage.updateMask(segYearsLeft.lt(yr).And(segYearsRight.gte(yr))),
)
yrImage = ee.Image(yrImage).rename(["yr"]).int16()
# Mask out the duration, magnitude, slope, and fit raster for the given year mask
yrDur = segDur.updateMask(yrImage)
yrMag = segMag.updateMask(yrImage)
yrSlope = segSlope.updateMask(yrImage)
yrFit = segFitRight.subtract(yrSlope.multiply(segYearsRight.subtract(yr))).updateMask(yrImage)
# Get the difference from the
diffFromLeft = yrFit.subtract(segFitLeft).updateMask(yrImage).rename(["diff"])
# relativeDiffFromLeft = diffFromLeft.divide(segMag.abs()).updateMask(yrImage).rename(['rel_yr_diff_left']).multiply(10000)
# diffFromRight =yrFit.subtract(segFitRight).updateMask(yrImage).rename(['yr_diff_right'])
# relativeDiffFromRight = diffFromRight.divide(segMag.abs()).updateMask(yrImage).rename(['rel_yr_diff_right']).multiply(10000)
# Stack it up
out = yrDur.addBands(yrFit).addBands(yrMag).addBands(yrSlope).addBands(diffFromLeft)
out = out.set("system:time_start", ee.Date.fromYMD(yr, 6, 1).millis())
return out
annualCollection = ee.FeatureCollection(ee.List.sequence(startYear, endYear).map(lambda yr: annualizer(yr)))
return annualCollection
yrDurMagSlope = ee.FeatureCollection(ee.List.sequence(1, maxSegments).map(lambda i: segmentLooper(i)))
# Convert to an image collection
yrDurMagSlope = ee.ImageCollection(yrDurMagSlope.flatten())
# Collapse each given year to the single segment with data
def cleaner(yrDurMagSlope, yr):
yrDurMagSlopeT = yrDurMagSlope.filter(ee.Filter.calendarRange(yr, yr, "year")).mosaic()
yrDurMagSlopeT = yrDurMagSlopeT.set("system:time_start", ee.Date.fromYMD(yr, 6, 1).millis())
return yrDurMagSlopeT
yrDurMagSlopeCleaned = ee.ImageCollection.fromImages(ee.List.sequence(startYear, endYear).map(lambda yr: cleaner(yrDurMagSlope, yr)))
return yrDurMagSlopeCleaned
# Convert image collection created using LANDTRENDRVertStack() to the same format as that created by
# LANDTRENDRFitMagSlopeDiffCollection(). Also works for Verdet Stack.
# VTorLT is the string that is put in the band names, 'LT' or 'VT'
def convertStack_To_DurFitMagSlope(stackCollection, VTorLT):
stackList = stackCollection.first().bandNames()
if "rmse" in stackList.getInfo():
stackList.remove("rmse")
stackCollection = stackCollection.select(stackList)
# Prep parameters for fitStackToCollection
maxSegments = stackCollection.first().get("maxSegments")
startYear = stackCollection.first().get("startYear")
endYear = stackCollection.first().get("endYear")
indexList = ee.Dictionary(stackCollection.aggregate_histogram("band")).keys().getInfo()
# Set up output collection to populate