emileten

from pystac_client import Client

CATALOG_URL = ''  # Replace with your catalog URL
COLLECTION_ID = '' # Replace with your collection ID

# Create a client to connect to the catalog
client = Client.open(CATALOG_URL)

for collection in client.get_all_collections():
    print(f'Collection ID: {collection.id}')

emileten

#!/usr/bin/env python3
from aws_cdk import aws_lambda, Stack, App
import aws_cdk.aws_lambda_python_alpha as aws_lambda_python

class LambdaStack(Stack):
    def __init__(self, app, construct_id, **kwargs) -> None:
        super().__init__(app, construct_id, **kwargs)
        self.construct_id = construct_id
        def python_lambda(name, directory, **kwargs):
            return aws_lambda_python.PythonFunction(

emileten

{
    "id": "ESACCI_Biomass_L4_AGB_V3_100m_2018",
    "links":[],
    "item_assets":{},
    "stac_version": "1.0.0",
    "license": "ESA CCI Data Policy: free and open access",
    "title": "ESA CCI above-ground biomass product level 4, year 2018",
    "type": "Collection",
    "description": "This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017 and 2018. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team.",
    "extent": {

emileten

"""Utilities to bulk load data into pgstac from json/ndjson."""
import logging

from pypgstac.load import Loader as BaseLoader
logger = logging.getLogger(__name__)
from typing import Dict
from pypgstac.load import Methods
from stac_pydantic import Collection
from pypgstac.db import PgstacDB

emileten

{
  "id": "AfriSAR_AGB_Maps_1681",
  "stac_version": "1.0.0",
  "links": [],
  "license": "not-provided",
  "title": "AfriSAR: Aboveground Biomass for Lope, Mabounie, Mondah, and Rabi Sites, Gabon",
  "type": "Collection",
  "description": "This dataset provides gridded estimates of aboveground biomass (AGB) for four sites in Gabon at 0.25 ha (50 m) resolution derived with field measurements and airborne LiDAR data collected from 2010 to 2016. The sites represent a mix of forested, savannah, and some agricultural and disturbed landcover types: Lope site, within Lope National Park; Mabounie, mostly forested site; Mondah Forest, protected area; and the Rabi forest site, part of the Smithsonian Institution of Global Earth Observatories world-wide network of forest plots. Plot-level biophysical measurements of tree diameter and tree height (or estimated by allometry) were performed at 1 ha and 0.25 ha scales on multiple plots at each site and used to derive AGB for each tree and then summed for each plot. Aerial 

emileten

# set ARN_MFA_DEVICE to the ARN of the MFA device associated with your SMCE account.
# and AWS_PROFILE to your AWS credentials profile associated with the SMCE MAAP account (in ~/.aws/credentials)
# first argument (positional) of this function is the MFA code that is displayed by your MFA app at the moment 
# you run the function (e.g. Google Auth) 
mfa_authentication_smce_maap () {

	export AWS_PROFILE="YOUR_SMCE_MAAP_PROFILE"
	export ARN_MFA_DEVICE="ARN_OF_MFA_DEVICE"

	tokens=$(aws sts get-session-token --serial-number $ARN_MFA_DEVICE --token-code $1 --output json)

emileten

emileten

name: explore-sentinel1-rtc
channels:
    - conda-forge
    - defaults
dependencies:
    - python=3.10
    - ipykernel
    - pip
    - pip:
        - stackstac

emileten

emileten