Ag-Analytics® - Elevation Index API¶
Local elevation on a field can have a strong correlation with harvest yield. The correlation stems from the drainage dynamics that are present on a given field, with low areas being productive in dry years and high areas being productive in wet years.
The Elevation Index API supports calculation of a set of elevation indices including field relative elevation, maximum slope, ,local Topographic Position Index(TPI), and local Topographic Roughness Index(TRI) for a given field elevation.
Required libraries¶
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import requests
import json
import time
import os
from pandas.io.json import json_normalize
from collections import defaultdict
import pandas as pd
import zipfile, io
from IPython.display import Image
%matplotlib inline
%autosave 0
Request Parameter Details¶
Request URL: https://ag-analytics.azure-api.net/elevation-index
1) raster (string): Local path to the elevation raster the index will be calculated from
2) Shape (Geojson String): Geojson representation of area of interest boundary.
3) index_list (string): The elevation index of interest to retrieve.
i. Relative_Elevation: Derived by calculating the z-score of every point in the field
ii. Slop: The maximum slope of each point on the field is calculated by taking the gradient of the elevation for each point.
iii.TPI: The topographic position index is a method of measuring the elevation of a given point with respect to a surrounding neighborhood of surrounding points. This index indicates the local extrema,or points of maximum and minimum elevation on a sub-field level.
iV.TRI: The topographic Roughness index express the amount of elevation difference between adjacent points on local field
4) Outside_Radius(optional): Outer radius of search area of each point. Used in TPI only.
(Ex:Outside_Radius =11 the outer search radius is 11 * cellsize. When cellsize is 0.001 of the elevation raster, the radius is about 11 * 8 = 88 meter
5) Inside_Radius(optional): Inner radius of search area. Used in TPI only.
6) Search_Radius(optional): Radius of computational box. search_radius = 1 => 3x3 box, 2 => 5x5 box, etc. Used in TRI only
API Function¶
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def ElevationIndex(values, headers, files = None):
try:
url = 'https://ag-analytics.azure-api.net/elevation-index'
if files == None:
response = requests.post(url, json=values, headers=headers).json()
else:
response = requests.post(url, files = files, data = values).json()
#resp = json.loads(response)
print(response)
return response
except Exception as e:
print(e)
raise e
Request with GeoJSON shape.¶
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# Request as GeoJSON.
values = {'Shape': '{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-76.5907145, 42.443918], [-76.5898132, 42.4224745], [-76.5699863, 42.4230447], [-76.5710592, 42.4443296], [-76.5907145, 42.443918]]]},\"properties\":{\"OBJECTID\":4944402,\"CALCACRES\":46.15999985,\"CALCACRES2\":null},\"id\":4944402}',
'index_list':"['Relative_Elevation']",
'Outside_Radius' :'5',
'Inside_Radius':'3',
'Search_Radius' :'2',
'Legend_Range': '10'
}
headers={'Content-Type':'application/json'}
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IndexResponse = ElevationIndex(values, headers)
Request using GeoTIFF DEM Raster¶
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#Path to raster in your local machine
rasterpath = r"<Path to DEM Raster>"
files = [('elevation_raster',(open(rasterpath, 'rb')))]
#Parameters to create index
values = {'index_list':"['Relative_Elevation']",
'Outside_Radius' :'5',
'Inside_Radius':'3',
'Search_Radius' :'2',
'Legend_Range': '10'
}
# Basic Header Pattern.
headers={'Content-Type':'multipart/form-data'}
# Header for using a subscription key.
# headers={'content-type': "application/json",'Ocp-Apim-Subscription-Key': "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"}
Calling API Function and Displaying Response¶
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IndexResponse = ElevationIndex(values, headers, files)
Output Index Summary¶
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df=defaultdict(list)
for value in IndexResponse['features']:
attributes = value[0]['attributes']
df['Index'].append(attributes['Product'])
df['Max'].append(float(attributes['Max']))
df['Mean'].append(float(attributes['Mean']))
df['Min'].append(float(attributes['Min']))
df['pngb64'].append(attributes['pngb64'])
indexdf=pd.DataFrame.from_dict(df)
indexdf
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Displaying Index Image¶
In [29]:
#####################################
# Select one index to display. Must match one of the following:
# ['Slope', 'Relative_Elevation', 'TPI', 'TRI']
Index = 'Relative_Elevation'
######################################
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#Select one index
Index = Index
#Image Legend
df=defaultdict(list)
for product in IndexResponse['features']:
if Index == product[0]['attributes']['Product']:
Legend = product[0]['attributes']['Legend']
for val in Legend:
df['Max'].append(float(val['Max']))
df['Mean'].append(float(val['Mean']))
df['Min'].append(float(val['Min']))
df['Color'].append(val['color'])
legenddf = pd.DataFrame.from_dict(df)
legenddf.style.applymap(lambda x:"background-color: %s"%x, subset=['Color'])
Out[30]:
In [31]:
IndexImage = indexdf.loc[indexdf['Index'] == Index,'pngb64'].iloc[0]
Image(url = IndexImage, width= 500, height =500)
Out[31]:
GET Request¶
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# Specify local path where file will be downloaded.
local_path = r"<Path to local directory>" # E.g., r"C:\Users\John_Doe\Documents\rasters"
# Create GET request payload.
Index = 'Relative_Elevation'
values = {'filenames': [IndexResponse[Index],]}
print(values)
In [11]:
def Elevation_Index_get(values, local_path):
try:
url = 'https://ag-analytics.azure-api.net/elevation-index'
download_path = os.path.join(local_path, values['filenames'][0])
print(values)
response = requests.get(url, params=values)
open(download_path, 'wb').write(response.content)
print(response.url)
return response
except Exception as e:
print(e)
raise e
In [12]:
Elevation_Index_get(values, local_path)
Out[12]:
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