danhammer

# Accepts a results dictionary and writes an short analysis based on
# the stored results.  Example:

# test_res_dict = {
#     'address' : '1460 Golden Gate Avenue, San Francisco, CA',
#     'sold_date' : '2008-04-21',

#     # baseline level of vegetation or some other sort of indicator of
#     # the property.
#     'pre_ndvi'  : 100,

danhammer

def landsatID(alert_date, coords, offset_days=30):
    """get the ID of the Landsat 8 image that is closest to the
    supplied alert date within the supplied GEE-formatted polygon

    """

    d = datetime.datetime.strptime(alert_date, '%Y-%m-%d')
    begin_date = d - datetime.timedelta(days=offset_days)
    poly = ee.Feature.Polygon(coords)

danhammer

// FORMA, Hammer et al. (2014)

// Objective:
//     Alerts of forest disturbance from MODIS imagery


// GEE core assets: 
//     MOD44B_C4_TREE_2000 (Vegetation Continuous Fields, annual 250m)
//     MOD13Q1 (Vegetation indices, 16-day 250m)
//     NOAA/PRECL_05D (Precipitation Reconstruction over Land, monthly 0.5 degree)

danhammer

// FORMA, Hammer et al. (2014)

// Objective:
//     Alerts of forest disturbance from MODIS imagery


// GEE core assets: 
//     MOD44B_C4_TREE_2000 (Vegetation Continuous Fields, annual 250m)
//     MOD13Q1 (Vegetation indices, 16-day 250m)
//     NOAA/PRECL_05D (Precipitation Reconstruction over Land, monthly 0.5 degree)

danhammer

// Normalized Burn Ratio
// Hammer, Kraft, and Steele (Data Lab at WRI)
// GFW-Fires, prototype

// Reference
// Escuin, S., R. Navarro, P. Fernandez. 2008. Fire severity assessment by 
//   using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference 
//   Vegetation Index) derived from LANDSAT TM/ETM images. Int. J. Remote 
//   Sens. 29:1053-1073.

danhammer

// Normalized Burn Ratio
// Hammer, Kraft, and Steele (Data Lab at WRI)
// GFW-Fires, prototype

// Reference
// Escuin, S., R. Navarro, P. Fernandez. 2008. Fire severity assessment by 
//   using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference 
//   Vegetation Index) derived from LANDSAT TM/ETM images. Int. J. Remote 
//   Sens. 29:1053-1073.

danhammer

// Normalized Burn Ratio
// Hammer, Kraft, and Steele (Data Lab at WRI)
// GFW-Fires, prototype

// Reference
// Escuin, S., R. Navarro, P. Fernandez. 2008. Fire severity assessment by 
//   using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference 
//   Vegetation Index) derived from LANDSAT TM/ETM images. Int. J. Remote 
//   Sens. 29:1053-1073.

danhammer

danhammer

import requests
import multiprocessing
from datetime import datetime

ALERT_URL = 'http://forma-cs-validation.elasticbeanstalk.com/api/alerts'


def _screener(x):
    try:
        not_cached = x['cached'] is not True

danhammer

import scipy
from scipy import ndimage
import copy
import matplotlib
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
import matplotlib.pyplot as plt
import numpy as np
from spaceknow.horik import tools