20 timeseries inspector

Uncomment the following line to install geemap if needed.

In [1]:

# !pip install geemap

# !pip install geemap

In [2]:

import ee
import geemap

import ee import geemap

In [3]:

geemap.show_youtube('0CZ7Aj8hCyo')

geemap.show_youtube('0CZ7Aj8hCyo')

Update the geemap package¶

If you run into errors with this notebook, please uncomment the line below to update the geemap package to the latest version from GitHub. Restart the Kernel (Menu -> Kernel -> Restart) to take effect.

In [4]:

# geemap.update_package()

# geemap.update_package()

NAIP: National Agriculture Imagery Program¶

The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental U.S.

NAIP projects are contracted each year based upon available funding and the FSA imagery acquisition cycle. Beginning in 2003, NAIP was acquired on a 5-year cycle. 2008 was a transition year, and a three-year cycle began in 2009.

NAIP imagery is acquired at a one-meter ground sample distance (GSD) with a horizontal accuracy that matches within six meters of photo-identifiable ground control points, which are used during image inspection.

Older images were collected using 3 bands (Red, Green, and Blue: RGB), but newer imagery is usually collected with an additional near-infrared band (RGBN).

More information about NAIP imagery can be found on Earth Engine Data Catalog.

Create annual composite of NAIP imagery¶

Select 4-band (RGBN) NAIP imagery.

In [5]:

Map = geemap.Map()

Map = geemap.Map()

In [6]:

naip_ts = geemap.naip_timeseries(start_year=2009, end_year=2018)

naip_ts = geemap.naip_timeseries(start_year=2009, end_year=2018)

Create a list of layer names to be shown under the dropdown list.

In [7]:

layer_names = ['NAIP ' + str(year) for year in range(2009, 2019)]
print(layer_names)

layer_names = ['NAIP ' + str(year) for year in range(2009, 2019)] print(layer_names)

['NAIP 2009', 'NAIP 2010', 'NAIP 2011', 'NAIP 2012', 'NAIP 2013', 'NAIP 2014', 'NAIP 2015', 'NAIP 2016', 'NAIP 2017', 'NAIP 2018']

Set visualization parameters.

In [8]:

naip_vis = {'bands': ['N', 'R', 'G']}

naip_vis = {'bands': ['N', 'R', 'G']}

Create a split-panel map for visualizing NAIP imagery

In [9]:

Map = geemap.Map()
Map.ts_inspector(
    left_ts=naip_ts,
    right_ts=naip_ts,
    left_names=layer_names,
    right_names=layer_names,
    left_vis=naip_vis,
    right_vis=naip_vis,
)
Map

Map = geemap.Map() Map.ts_inspector( left_ts=naip_ts, right_ts=naip_ts, left_names=layer_names, right_names=layer_names, left_vis=naip_vis, right_vis=naip_vis, ) Map

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Input In [9], in <cell line: 2>()
      1 Map = geemap.Map()
----> 2 Map.ts_inspector(
      3     left_ts=naip_ts,
      4     right_ts=naip_ts,
      5     left_names=layer_names,
      6     right_names=layer_names,
      7     left_vis=naip_vis,
      8     right_vis=naip_vis,
      9 )
     10 Map

AttributeError: 'Map' object has no attribute 'ts_inspector'

Create annual composite of Landsat imagery¶

Use the drawing tools to create an ROI

In [10]:

import ee
import geemap

import ee import geemap

In [11]:

Map = geemap.Map()
Map

Map = geemap.Map() Map

Out[11]:

Make this Notebook Trusted to load map: File -> Trust Notebook

In [12]:

region = Map.draw_last_feature

region = Map.draw_last_feature

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Input In [12], in <cell line: 1>()
----> 1 region = Map.draw_last_feature

AttributeError: 'Map' object has no attribute 'draw_last_feature'

In [13]:

if region is not None:
    roi = region.geometry()
else:
    roi = ee.Geometry.Polygon(
        [
            [
                [-115.897448, 35.640766],
                [-115.897448, 36.603608],
                [-113.784915, 36.603608],
                [-113.784915, 35.640766],
                [-115.897448, 35.640766],
            ]
        ],
        None,
        False,
    )

if region is not None: roi = region.geometry() else: roi = ee.Geometry.Polygon( [ [ [-115.897448, 35.640766], [-115.897448, 36.603608], [-113.784915, 36.603608], [-113.784915, 35.640766], [-115.897448, 35.640766], ] ], None, False, )

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Input In [13], in <cell line: 1>()
----> 1 if region is not None:
      2     roi = region.geometry()
      3 else:

NameError: name 'region' is not defined

In [14]:

print(roi.getInfo())

print(roi.getInfo())

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Input In [14], in <cell line: 1>()
----> 1 print(roi.getInfo())

NameError: name 'roi' is not defined

In [15]:

landsat_ts = geemap.landsat_timeseries(
    roi=roi, start_year=1984, end_year=2019, start_date='01-01', end_date='12-31'
)

landsat_ts = geemap.landsat_timeseries( roi=roi, start_year=1984, end_year=2019, start_date='01-01', end_date='12-31' )

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Input In [15], in <cell line: 1>()
      1 landsat_ts = geemap.landsat_timeseries(
----> 2     roi=roi, start_year=1984, end_year=2019, start_date='01-01', end_date='12-31'
      3 )

NameError: name 'roi' is not defined

In [16]:

layer_names = ['Landsat ' + str(year) for year in range(1984, 2020)]
print(layer_names)

layer_names = ['Landsat ' + str(year) for year in range(1984, 2020)] print(layer_names)

['Landsat 1984', 'Landsat 1985', 'Landsat 1986', 'Landsat 1987', 'Landsat 1988', 'Landsat 1989', 'Landsat 1990', 'Landsat 1991', 'Landsat 1992', 'Landsat 1993', 'Landsat 1994', 'Landsat 1995', 'Landsat 1996', 'Landsat 1997', 'Landsat 1998', 'Landsat 1999', 'Landsat 2000', 'Landsat 2001', 'Landsat 2002', 'Landsat 2003', 'Landsat 2004', 'Landsat 2005', 'Landsat 2006', 'Landsat 2007', 'Landsat 2008', 'Landsat 2009', 'Landsat 2010', 'Landsat 2011', 'Landsat 2012', 'Landsat 2013', 'Landsat 2014', 'Landsat 2015', 'Landsat 2016', 'Landsat 2017', 'Landsat 2018', 'Landsat 2019']

In [17]:

landsat_vis = {
    'min': 0,
    'max': 4000,
    'gamma': [1, 1, 1],
    'bands': ['NIR', 'Red', 'Green'],
}

landsat_vis = { 'min': 0, 'max': 4000, 'gamma': [1, 1, 1], 'bands': ['NIR', 'Red', 'Green'], }

In [18]:

Map = geemap.Map()
Map.ts_inspector(
    left_ts=landsat_ts,
    right_ts=landsat_ts,
    left_names=layer_names,
    right_names=layer_names,
    left_vis=landsat_vis,
    right_vis=landsat_vis,
)
Map.centerObject(roi, zoom=8)
Map

Map = geemap.Map() Map.ts_inspector( left_ts=landsat_ts, right_ts=landsat_ts, left_names=layer_names, right_names=layer_names, left_vis=landsat_vis, right_vis=landsat_vis, ) Map.centerObject(roi, zoom=8) Map

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Input In [18], in <cell line: 2>()
      1 Map = geemap.Map()
----> 2 Map.ts_inspector(
      3     left_ts=landsat_ts,
      4     right_ts=landsat_ts,
      5     left_names=layer_names,
      6     right_names=layer_names,
      7     left_vis=landsat_vis,
      8     right_vis=landsat_vis,
      9 )
     10 Map.centerObject(roi, zoom=8)
     11 Map

AttributeError: 'Map' object has no attribute 'ts_inspector'

Compare Landsat imagery and National Land Cover Database (NLCD)¶

More information about NLCD can be found at the Earth Engine Data Catalog.

In [19]:

import ee
import geemap

import ee import geemap

In [20]:

Map = geemap.Map()
Map

Map = geemap.Map() Map

Out[20]:

Make this Notebook Trusted to load map: File -> Trust Notebook

In [21]:

NLCD = ee.ImageCollection('USGS/NLCD')

NLCD = ee.ImageCollection('USGS/NLCD')

In [22]:

NLCD_layers = NLCD.aggregate_array('system:id').getInfo()
print(NLCD_layers)

NLCD_layers = NLCD.aggregate_array('system:id').getInfo() print(NLCD_layers)

['USGS/NLCD/NLCD1992', 'USGS/NLCD/NLCD2001', 'USGS/NLCD/NLCD2001_AK', 'USGS/NLCD/NLCD2001_HI', 'USGS/NLCD/NLCD2001_PR', 'USGS/NLCD/NLCD2004', 'USGS/NLCD/NLCD2006', 'USGS/NLCD/NLCD2008', 'USGS/NLCD/NLCD2011', 'USGS/NLCD/NLCD2011_AK', 'USGS/NLCD/NLCD2011_HI', 'USGS/NLCD/NLCD2011_PR', 'USGS/NLCD/NLCD2013', 'USGS/NLCD/NLCD2016']

In [23]:

NLCD2001 = ee.Image('USGS/NLCD/NLCD2001').select('landcover')
NLCD2006 = ee.Image('USGS/NLCD/NLCD2006').select('landcover')
NLCD2011 = ee.Image('USGS/NLCD/NLCD2011').select('landcover')
NLCD2016 = ee.Image('USGS/NLCD/NLCD2016').select('landcover')

NLCD2001 = ee.Image('USGS/NLCD/NLCD2001').select('landcover') NLCD2006 = ee.Image('USGS/NLCD/NLCD2006').select('landcover') NLCD2011 = ee.Image('USGS/NLCD/NLCD2011').select('landcover') NLCD2016 = ee.Image('USGS/NLCD/NLCD2016').select('landcover')

In [24]:

NLCD = ee.ImageCollection([NLCD2001, NLCD2006, NLCD2011, NLCD2016])

NLCD = ee.ImageCollection([NLCD2001, NLCD2006, NLCD2011, NLCD2016])

In [25]:

NLCD_layer_names = ['NLCD ' + str(year) for year in range(2001, 2017, 5)]
print(NLCD_layer_names)

NLCD_layer_names = ['NLCD ' + str(year) for year in range(2001, 2017, 5)] print(NLCD_layer_names)

['NLCD 2001', 'NLCD 2006', 'NLCD 2011', 'NLCD 2016']

In [26]:

roi = ee.Geometry.Polygon(
    [
        [
            [-115.897448, 35.640766],
            [-115.897448, 36.603608],
            [-113.784915, 36.603608],
            [-113.784915, 35.640766],
            [-115.897448, 35.640766],
        ]
    ],
    None,
    False,
)

roi = ee.Geometry.Polygon( [ [ [-115.897448, 35.640766], [-115.897448, 36.603608], [-113.784915, 36.603608], [-113.784915, 35.640766], [-115.897448, 35.640766], ] ], None, False, )

In [27]:

landsat_ts = geemap.landsat_timeseries(
    roi=roi, start_year=2001, end_year=2016, start_date='01-01', end_date='12-31'
)

landsat_ts = geemap.landsat_timeseries( roi=roi, start_year=2001, end_year=2016, start_date='01-01', end_date='12-31' )

In [28]:

landsat_layer_names = ['Landsat ' + str(year) for year in range(2001, 2017)]

landsat_layer_names = ['Landsat ' + str(year) for year in range(2001, 2017)]

In [29]:

landsat_vis = {
    'min': 0,
    'max': 4000,
    'gamma': [1, 1, 1],
    'bands': ['NIR', 'Red', 'Green'],
}

nlcd_vis = {'bands': ['landcover']}

landsat_vis = { 'min': 0, 'max': 4000, 'gamma': [1, 1, 1], 'bands': ['NIR', 'Red', 'Green'], } nlcd_vis = {'bands': ['landcover']}

In [30]:

Map = geemap.Map()
Map.ts_inspector(
    left_ts=landsat_ts,
    right_ts=NLCD,
    left_names=landsat_layer_names,
    right_names=NLCD_layer_names,
    left_vis=landsat_vis,
    right_vis=nlcd_vis,
)
Map.centerObject(roi, zoom=8)
Map

Map = geemap.Map() Map.ts_inspector( left_ts=landsat_ts, right_ts=NLCD, left_names=landsat_layer_names, right_names=NLCD_layer_names, left_vis=landsat_vis, right_vis=nlcd_vis, ) Map.centerObject(roi, zoom=8) Map

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Input In [30], in <cell line: 2>()
      1 Map = geemap.Map()
----> 2 Map.ts_inspector(
      3     left_ts=landsat_ts,
      4     right_ts=NLCD,
      5     left_names=landsat_layer_names,
      6     right_names=NLCD_layer_names,
      7     left_vis=landsat_vis,
      8     right_vis=nlcd_vis,
      9 )
     10 Map.centerObject(roi, zoom=8)
     11 Map

AttributeError: 'Map' object has no attribute 'ts_inspector'

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Last update: 2022-03-14