danhammer

import os
import requests
import json
import pandas as pd

# Go from JSON to a measurement of UMD deforestation by year.  If you
# have a shapefile, you will have to convert and simplify the
# polygons, preserving topology.  The Earth Engine API does not
# support long, long requests.  You can convert the shapefile using
# OGR within the data subdirectory:

danhammer

import requests
import pandas
from BeautifulSoup import BeautifulSoup

def _grab_data():
    url = 'http://www.bettermap.org/json'
    x = requests.get(url)
    return x.json()['features']

def _process_entry(entry):

danhammer

from bs4 import BeautifulSoup
import json
import pandas as pd

# Process information on Caltrans projects.  Convert this file to
# GeoJSON:

# wget https://dot.ca.gov/hq/construc/cons.kml
# ogr2ogr -f GeoJSON cons.json cons.kml

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.