Fia db

In [ ]:

%pip install basal_and_bark

%pip install basal_and_bark

In [1]:

import basal_and_bark
from basal_and_bark import basal_and_bark as basal
import ipyleaflet
from ipyleaflet import WidgetControl
import ipywidgets
from ipywidgets import HTML, widgets

import basal_and_bark from basal_and_bark import basal_and_bark as basal import ipyleaflet from ipyleaflet import WidgetControl import ipywidgets from ipywidgets import HTML, widgets

Set up the Google Earth Engine (GEE) session. If you do not have a Google Earth Engine account, you can create one and authenticate it in the following code block. If you do not wish to make a GEE account, comment out the next block, second to last line of code: print(leaflet_map.addGEEData(county))

There are additional functionalities to this package outside of what can be done using GEE, although it is cooler with GEE.

In [2]:

import ee
ee.Authenticate()

import ee ee.Authenticate()

To authorize access needed by Earth Engine, open the following URL in a web browser and follow the instructions:

https://code.earthengine.google.com/client-auth?scopes=https%3A//www.googleapis.com/auth/earthengine%20https%3A//www.googleapis.com/auth/devstorage.full_control&request_id=VVPU4jj0Hr8CqhCV4WNFBDWeziY6W8-rlY3xsyxUVe4&tc=fiHrBRclKxbGOTVFTKPt6XuN72Qpb8Ovpec3FKXTI2A&cc=qhZe0FnD96WERNgJjj8dkTcIdHGsGoY1pilHcz-Je-0

The authorization workflow will generate a code, which you should paste in the box below.

Successfully saved authorization token.

Next, setup a simple basal_and_bark Map() environment to show functionality of the package in.

In [3]:

leaflet_map = basal.Map(center = [40,-100], zoom = 4, test = "test", scroll_wheel_zoom = True)

leaflet_map = basal.Map(center = [40,-100], zoom = 4, test = "test", scroll_wheel_zoom = True)

       ESTIMATE
0  1.532357e+07
1  5.771219e+05
2  5.745549e+03
3  9.782071e+04
4  9.251352e+06
5  1.213648e+05
6  1.998276e+06
The above table are estimates for 6 different categories of land, as specified by USFS: 'Timberland', 'Reserved', 'Other Forestland', 'Nonforest', 'Non-Census Water', 'Census Water', 'Other'
The table below contains summary statistics of the canopy cover in the Hansen et al Forest Change dataset.

*** Earth Engine *** Share your feedback by taking our Annual Developer Satisfaction Survey: https://google.qualtrics.com/jfe/form/SV_doiqkQG3NJ1t8IS?source=API

       GEEMap Data
max   1.000000e+02
mean  5.797900e+01
min   0.000000e+00
std   4.493400e+01
sum   9.013637e+09

       ESTIMATE
0  1.804803e+07
1  6.093884e+05
2  2.706504e+04
3  4.040822e+04
4  1.223426e+07
5  1.606379e+05
6  3.324307e+06
The above table are estimates for 6 different categories of land, as specified by USFS: 'Timberland', 'Reserved', 'Other Forestland', 'Nonforest', 'Non-Census Water', 'Census Water', 'Other'
The table below contains summary statistics of the canopy cover in the Hansen et al Forest Change dataset.
       GEEMap Data
max   1.000000e+02
mean  5.254678e+01
min   0.000000e+00
std   4.517764e+01
sum   1.002224e+10

In [4]:

leaflet_map.add_layer_control()

leaflet_map.add_layer_control()

In [5]:

leaflet_map

leaflet_map

Map(center=[40, -100], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_t…

The addRefData() function loads in a shp file of the states for data aggregation. This is specific to states currently. Future work will be looking into potential uses of custom scales for examining forestry data in this package.

In [6]:

states = leaflet_map.addRefData()

states = leaflet_map.addRefData()

Setup an output widget that will house a dropdown menu of years, from which the user can select the year they want to get FIA estimates for.

In [7]:

output_widget = widgets.Output(layout={'border': '1px solid black'})
output_widget.clear_output()
ctrl = WidgetControl(widget=output_widget, position='bottomright')
leaflet_map.add_control(ctrl)

dropdown = widgets.Dropdown(
            options = ["2018", "2019", "2020", "2021"], 
            value="2020",
            description='Year',
            )

with output_widget:
            display(dropdown)

output_widget = widgets.Output(layout={'border': '1px solid black'}) output_widget.clear_output() ctrl = WidgetControl(widget=output_widget, position='bottomright') leaflet_map.add_control(ctrl) dropdown = widgets.Dropdown( options = ["2018", "2019", "2020", "2021"], value="2020", description='Year', ) with output_widget: display(dropdown)

This function activates when the user clicks a location on the map - on_interaction(handle_click). A point is added to the map at the location of the click makePointsFromClick(). This location data is then used to determine which state the user clicked within findInt(). Then FIA estimates are returned for the state the user clicked in getAPIData(). Additionally, the Hansen et al forestry data available on GEE is clipped to the state the user clicked in and plotted on the map addGEEData(). Summary statistics of this clipped data is also returned. This gives the user two different representations of the state of forests in the state they clicked in.

In [8]:

def handle_click(**kwargs):

    if kwargs.get('type') == 'click':
        output_widget.clear_output()
        #leaflet_map.add_layer(Marker(location=kwargs.get('coordinates')))

        gdf = leaflet_map.makePointsFromClick(kwargs.get('coordinates'))

        state = leaflet_map.findInt(states, gdf)
        
        with output_widget:
            display(dropdown)
            print(state)
        print(leaflet_map.getAPIdata(state, dropdown.value))#['ESTIMATE'])
        print("The above table are estimates for 6 different categories of land, as specified by USFS: "+ 
              "'Timberland', 'Reserved', 'Other Forestland', 'Nonforest', 'Non-Census Water', 'Census Water', 'Other'")
        print("The table below contains summary statistics of the canopy cover in the Hansen et al Forest Change dataset.")
        print(leaflet_map.addGEEData(state))

leaflet_map.on_interaction(handle_click)

def handle_click(**kwargs): if kwargs.get('type') == 'click': output_widget.clear_output() #leaflet_map.add_layer(Marker(location=kwargs.get('coordinates'))) gdf = leaflet_map.makePointsFromClick(kwargs.get('coordinates')) state = leaflet_map.findInt(states, gdf) with output_widget: display(dropdown) print(state) print(leaflet_map.getAPIdata(state, dropdown.value))#['ESTIMATE']) print("The above table are estimates for 6 different categories of land, as specified by USFS: "+ "'Timberland', 'Reserved', 'Other Forestland', 'Nonforest', 'Non-Census Water', 'Census Water', 'Other'") print("The table below contains summary statistics of the canopy cover in the Hansen et al Forest Change dataset.") print(leaflet_map.addGEEData(state)) leaflet_map.on_interaction(handle_click)