A310a Checkpoint

Import libraries¶

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import ee
import geemap

import ee import geemap

Create an interactive map¶

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Map = geemap.Map(center=[40, -100], zoom=4)

Map = geemap.Map(center=[40, -100], zoom=4)

Add Earth Engine Python script¶

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# Add Earth Engine dataset
image = ee.Image("USGS/SRTMGL1_003")

#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#  Chapter:      Chapter A3.10 Conservation II - Assessing Agricultural
#                Intensification Near Protected Areas
#  Checkpoint:   A310a
#  Authors:      Pradeep Koulgi, MD Madhusudan
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# 1. Parameters to function calls

# 1.1. Annual dry season max NDVI calculation
modis_veg = ee.ImageCollection("MODIS/006/MOD13Q1")
ndviBandName = "NDVI"
ndviValuesScaling = 0.0001
modisVegScale = 250
# meters
maxNDVIBandname = "max_dryseason_ndvi"
yearTimestampBandname = "year"
years = ee.List.sequence(2000, 2021, 1)
drySeasonStart_doy = 1
drySeasonEnd_doy = 90

# 1.2. Boundaries of Protected Areas of interest
paBoundaries = ee.FeatureCollection("projects/gee-book/assets/A3-10/IndiaMainlandPAs")
boundaryBufferWidth = 5000
# meters
bufferingMaxError = 30
# meters
# Choose PAs in only the western states
western_states = [
    "Rajasthan",
    "Gujarat",
    "Madhya Pradesh",
    "Maharashtra",
    "Goa",
    "Karnataka",
    "Kerala",
]
western_pas = paBoundaries.filter(ee.Filter.inList("STATE", western_states))

# 1.3. Regression analysis
regressionReducer = ee.Reducer.sensSlope()
regressionX = yearTimestampBandname
regressionY = maxNDVIBandname

# 1.4. Surface water layer to mask water pixels from assessment
# Selects pixels where water has ever been detected between 1984 and 2021
surfaceWaterExtent = ee.Image("JRC/GSW1_3/GlobalSurfaceWater").select("max_extent")

# 1.5. Average annual precipitation layer
rainfall = ee.Image("WORLDCLIM/V1/BIO").select("bio12")

# 1.6. Visualization parameters
regressionResultVisParams = {
    "min": -3,
    "max": 3,
    "palette": ["ff8202", "ffffff", "356e02"],
}
regressionSummaryChartingOptions = {
    "title": "Yearly change in dry-season vegetation greenness "
    + "in PA buffers in relation to average annual rainfall",
    "hAxis": {"title": "Annual Precipitation"},
    "vAxis": {
        "title": "Median % yearly change in vegetation greenness " + "in 5 km buffer"
    },
    "series": {"0": {"visibleInLegend": False}},
}

# -----------------------------------------------------------------------
# CHECKPOINT
# -----------------------------------------------------------------------

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: Chapter A3.10 Conservation II - Assessing Agricultural # Intensification Near Protected Areas # Checkpoint: A310a # Authors: Pradeep Koulgi, MD Madhusudan # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # 1. Parameters to function calls # 1.1. Annual dry season max NDVI calculation modis_veg = ee.ImageCollection("MODIS/006/MOD13Q1") ndviBandName = "NDVI" ndviValuesScaling = 0.0001 modisVegScale = 250 # meters maxNDVIBandname = "max_dryseason_ndvi" yearTimestampBandname = "year" years = ee.List.sequence(2000, 2021, 1) drySeasonStart_doy = 1 drySeasonEnd_doy = 90 # 1.2. Boundaries of Protected Areas of interest paBoundaries = ee.FeatureCollection("projects/gee-book/assets/A3-10/IndiaMainlandPAs") boundaryBufferWidth = 5000 # meters bufferingMaxError = 30 # meters # Choose PAs in only the western states western_states = [ "Rajasthan", "Gujarat", "Madhya Pradesh", "Maharashtra", "Goa", "Karnataka", "Kerala", ] western_pas = paBoundaries.filter(ee.Filter.inList("STATE", western_states)) # 1.3. Regression analysis regressionReducer = ee.Reducer.sensSlope() regressionX = yearTimestampBandname regressionY = maxNDVIBandname # 1.4. Surface water layer to mask water pixels from assessment # Selects pixels where water has ever been detected between 1984 and 2021 surfaceWaterExtent = ee.Image("JRC/GSW1_3/GlobalSurfaceWater").select("max_extent") # 1.5. Average annual precipitation layer rainfall = ee.Image("WORLDCLIM/V1/BIO").select("bio12") # 1.6. Visualization parameters regressionResultVisParams = { "min": -3, "max": 3, "palette": ["ff8202", "ffffff", "356e02"], } regressionSummaryChartingOptions = { "title": "Yearly change in dry-season vegetation greenness " + "in PA buffers in relation to average annual rainfall", "hAxis": {"title": "Annual Precipitation"}, "vAxis": { "title": "Median % yearly change in vegetation greenness " + "in 5 km buffer" }, "series": {"0": {"visibleInLegend": False}}, } # ----------------------------------------------------------------------- # CHECKPOINT # -----------------------------------------------------------------------

Display the interactive map¶

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Map

Map