GitHub - dronemapper-io/CropAnalysis: A jupyter notebook with crop analysis algorithms utilizing digital elevation models and multi-spectral imagery (R-G-B-NIR-Rededge-Thermal)

DroneMapper Crop Analysis

A jupyter notebook with crop analysis algorithms utilizing digital elevation models, dtm and multi-spectral imagery (R-G-B-NIR-Rededge-Thermal) from a MicaSense Altum sensor processed with DroneMapper Remote Expert.

Due to limitations on git file sizes, you will need to download the GeoTIFF data for this project from the following url: https://dronemapper.com/software/DroneMapper_CropAnalysis_Data.zip. Once that has been completed, extract the TIF files into the notebook data directory matching the structure below.

Included Data

data/DrnMppr-DEM-AOI.tif - 32bit georeferenced digital elevation model
data/DrnMppr-ORT-AOI.tif - 16bit georeferenced orthomosaic (Red-Green-Blue-NIR-Rededge-Thermal)
data/DrnMppr-DTM-AOI.tif - 32bit georeferenced dtm
data/plant_count.shp - plant count AOI
data/plots_1.shp - plot 1 AOI
data/plots_2.shp - plot 2 AOI

Algorithms

plot volume/biomass
plot canopy height
plot ndvi zonal statistics
plot thermals
plant count

Notes

These basic algorithms are intended to get you started and interested in multi-spectral processing and analysis.

The orthomosaic, digital elevation model, and dtm were clipped to an AOI using GlobalMapper. The shapefile plots were also generated using GlobalMapper grid tool. We highly recommend GlobalMapper for GIS work!

We cloned the MicaSense imageprocessing repository and created the Batch Processing DroneMapper.ipynb notebook which allows you to quickly align and stack a Altum or RedEdge dataset creating the correct TIF files with EXIF/GPS metadata preserved. These stacked TIF files are then directly loaded into DroneMapper Remote Expert for processing.

This notebook assumes the user has basic knowledge of setting up their python environment, importing libraries and working inside jupyter.

Do More!

Implement additional algorithms like NDRE or alternative methods for plant counts. Submit a pull request!

%load_ext autoreload
%autoreload 2

Load Digital Elevation Model and Orthomosaic

import numpy as np
import rasterio
from matplotlib import pyplot as plt
import matplotlib as mpl

import earthpy as et
import earthpy.plot as ep
import earthpy.spatial as es

# ensure libspatialindex-dev is installed via apt-get or yum

%matplotlib inline

dem = rasterio.open('data/DrnMppr-DEM-AOI.tif')
ortho = rasterio.open('data/DrnMppr-ORT-AOI.tif')

dem_arr = dem.read()
ortho_arr = ortho.read()

# mask elevation <= 0
elevation = dem_arr[0]
elevation[elevation <= 0] = np.nan

# rededge mask <= 0
masked_re = np.ma.masked_where(ortho_arr[4] <= 0, ortho_arr[4])

# generate hillshade
hillshade = es.hillshade(elevation, altitude=30, azimuth=210)

fig, ax = plt.subplots(1, 4, figsize=(20, 20))

# plot
ep.plot_rgb(ortho_arr, ax=ax[0], rgb=[0, 1, 2], title="Red Green Blue", stretch=True)
ep.plot_rgb(ortho_arr, ax=ax[1], rgb=[3, 1, 2], title="NIR Green Blue", stretch=True)
ep.plot_bands(masked_re, ax=ax[2], scale=False, cmap="terrain", title="RedEdge")
ep.plot_bands(elevation, ax=ax[3], scale=False, cmap="terrain", title="Digital Elevation Model")
ax[3].imshow(hillshade, cmap="Greys", alpha=0.5)
plt.show()

Load Plot 1 AOI and Generate NDVI

import geopandas as gpd
from rasterio.plot import plotting_extent

np.seterr(divide='ignore', invalid='ignore')

fig, ax = plt.subplots(figsize=(20, 20))
plot_extent = plotting_extent(dem_arr[0], dem.transform)

# generate ndvi
ndvi = es.normalized_diff(ortho_arr[3], ortho_arr[0])
ep.plot_bands(ndvi, 
              ax=ax, 
              cmap="RdYlGn",
              title="NDVI & Plots", 
              scale=False, 
              vmin=-1, 
              vmax=1, 
              extent=plot_extent)

plots = gpd.read_file('data/plots_1.shp')
plots.plot(ax=ax,
           color='None', 
           edgecolor='black', 
           linewidth=1)

# show plot names
plots.apply(lambda x: ax.annotate(s=x.NAME, xy=x.geometry.centroid.coords[0], ha='center'), axis=1);
plt.show()

Generate NDVI Zonal Statistics For Each Plot

import rasterstats as rs
from shapely.geometry import Polygon
from IPython.display import display

# compute zonal statistics on each plot
plot_zs = rs.zonal_stats(plots, 
                         ndvi, 
                         nodata=0, 
                         affine=dem.transform, 
                         geojson_out=True, 
                         copy_properties=True, 
                         stats="count min mean max median std")

# build dataframe and display first 5 records
plot_df = gpd.GeoDataFrame.from_features(plot_zs)
display(plot_df.head())
plot_df.to_csv('output/aoi1_plot_mean_ndvi.csv')

fig, ax = plt.subplots(figsize=(20, 20))

# plot ndvi
ep.plot_bands(ndvi, 
              ax=ax, 
              cmap="RdYlGn",
              title="NDVI & Plot Mean Values", 
              scale=False, 
              vmin=-1, 
              vmax=1, 
              extent=plot_extent)

# overlay the mean ndvi value color for each plot and all pixels inside plot
plot_df.plot('mean',
             ax=ax, 
             cmap='RdYlGn', 
             edgecolor='black', 
             linewidth=1,
             vmin=-1,
             vmax=1)

# show plot mean values
plot_df.apply(lambda x: ax.annotate(s='{:.2}'.format(x['mean']), xy=x.geometry.centroid.coords[0], ha='center'), axis=1);
plt.show()

	geometry	LAYER	MAP_NAME	NAME	min	max	mean	count	std	median
0	POLYGON Z ((289583.708 5130289.226 0.000, 2895...	Coverage/Quad	User Created Features	2	0.188461	0.873092	0.438559	4444	0.078921	0.436500
1	POLYGON Z ((289588.705 5130289.052 0.000, 2895...	Coverage/Quad	User Created Features	3	0.193214	0.887971	0.445282	4440	0.091090	0.425304
2	POLYGON Z ((289593.702 5130288.877 0.000, 2895...	Coverage/Quad	User Created Features	4	0.232222	0.890147	0.552864	4440	0.112440	0.519746
3	POLYGON Z ((289598.699 5130288.703 0.000, 2896...	Coverage/Quad	User Created Features	5	0.090825	0.865083	0.530295	4444	0.110570	0.515392
4	POLYGON Z ((289603.696 5130288.528 0.000, 2896...	Coverage/Quad	User Created Features	6	0.104697	0.922450	0.536660	4442	0.132731	0.495813

You can view the Plot 1 AOI mean plot NDVI values: aoi1_plot_mean_ndvi.csv

Load Plot 2 AOI & Compute DEM Canopy Mean Height For Each Plot

plots = gpd.read_file('data/plots_2.shp')
plt.rcParams.update({'font.size': 8})

# compute zonal statistics on each plot
plot_zs = rs.zonal_stats(plots, 
                         elevation, 
                         nodata=0, 
                         affine=dem.transform, 
                         geojson_out=True, 
                         copy_properties=True, 
                         stats="count min mean max median std")

# build dataframe and display first 5 records
plot_df = gpd.GeoDataFrame.from_features(plot_zs)
display(plot_df.head())
plot_df.to_csv('output/aoi2_plot_mean_height.csv')

fig, ax = plt.subplots(figsize=(20, 20))

# plot dem
ep.plot_bands(elevation, 
              ax=ax, 
              cmap="terrain",
              title="DEM & Plot Canopy Mean Height", 
              scale=False, 
              extent=plot_extent)

# overlay the mean dem value color for each plot and all pixels inside plot
plot_df.plot('mean',
             ax=ax, 
             cmap='terrain', 
             edgecolor='black', 
             linewidth=1)

# show plot mean values
plot_df.apply(lambda x: ax.annotate(s='{0:0.1f}'.format(x['mean']), xy=x.geometry.centroid.coords[0], ha='center'), axis=1);
plt.show()

	geometry	LAYER	MAP_NAME	NAME	min	max	mean	count	std	median
0	POLYGON Z ((289707.875 5130279.812 1182.502, 2...	Coverage/Quad	User Created Features - Coverage/Quad	1	360.129120	363.381683	361.141294	3318	1.091593	360.441467
1	POLYGON Z ((289712.189 5130279.586 1190.569, 2...	Coverage/Quad	User Created Features - Coverage/Quad	2	360.131866	363.382446	361.710297	3316	1.215926	361.927017
2	POLYGON Z ((289716.503 5130279.360 1183.212, 2...	Coverage/Quad	User Created Features - Coverage/Quad	3	360.117279	363.384766	361.138592	3310	1.122890	360.425781
3	POLYGON Z ((289720.817 5130279.134 1182.668, 2...	Coverage/Quad	User Created Features - Coverage/Quad	4	360.110443	363.387207	361.915436	3322	1.258644	362.585251
4	POLYGON Z ((289725.131 5130278.908 1182.782, 2...	Coverage/Quad	User Created Features - Coverage/Quad	5	360.006683	363.377991	361.558501	3320	1.305164	360.546524

You can view the Plot 2 AOI mean plot height values: aoi2_plot_mean_height.csv

Compute Thermal Mean For Each Plot

The thermal band (6) in the processed orthomosaic shows stitching artifacts which could likely be improved using more accurate pre-processing alignment and de-distortion algorithms. You can find more information about these functions in the MicaSense imageprocessing github repository. See notes at the top of this notebook.

# thermal mask <= 0
masked_thermal = np.ma.masked_where(ortho_arr[5] <= 0, ortho_arr[5])

# compute zonal statistics on each plot
plot_zs = rs.zonal_stats(plots, 
                         masked_thermal, 
                         nodata=0, 
                         affine=dem.transform, 
                         geojson_out=True, 
                         copy_properties=True, 
                         stats="count min mean max median std")

# build dataframe and display first 5 records
plot_df = gpd.GeoDataFrame.from_features(plot_zs)
display(plot_df.head())
plot_df.to_csv('output/aoi2_plot_mean_thermal.csv')

fig, ax = plt.subplots(1, 2, figsize=(20, 20))

# plot thermal
ep.plot_bands(masked_thermal,
              ax=ax[0], 
              scale=False, 
              cmap="gist_gray", 
              title="Thermal",
              extent=plot_extent)

plots.plot(ax=ax[0], color='None', edgecolor='white', linewidth=1)

# display thermal plot
plot_df.plot('mean',
             ax=ax[1], 
             cmap='inferno', 
             edgecolor='black',
             linewidth=1,
             legend=True)


# show plot mean values
plot_df.apply(lambda x: ax[1].annotate(s='{0:0.1f}'.format(x['mean']), xy=x.geometry.centroid.coords[0], ha='center'), axis=1);
plt.show()

	geometry	LAYER	MAP_NAME	NAME	min	max	mean	count	std	median
0	POLYGON Z ((289707.875 5130279.812 1182.502, 2...	Coverage/Quad	User Created Features - Coverage/Quad	1	30008.0	30431.0	30196.628692	3318	120.982058	30193.0
1	POLYGON Z ((289712.189 5130279.586 1190.569, 2...	Coverage/Quad	User Created Features - Coverage/Quad	2	30068.0	30560.0	30333.192702	3316	123.856093	30332.0
2	POLYGON Z ((289716.503 5130279.360 1183.212, 2...	Coverage/Quad	User Created Features - Coverage/Quad	3	29792.0	30645.0	30266.030211	3310	170.831207	30275.5
3	POLYGON Z ((289720.817 5130279.134 1182.668, 2...	Coverage/Quad	User Created Features - Coverage/Quad	4	29790.0	30700.0	30386.137267	3322	201.266919	30391.0
4	POLYGON Z ((289725.131 5130278.908 1182.782, 2...	Coverage/Quad	User Created Features - Coverage/Quad	5	29618.0	30691.0	30209.904518	3320	292.299392	30262.0

You can view the Plot 2 AOI mean plot thermal values: aoi2_plot_mean_thermal.csv

Load Plot 1 AOI & Compute Volume/Biomass For Each Plot

# we will use the masked dtm and masked elevation data
plots = gpd.read_file('data/plots_1.shp')

dtm = rasterio.open('data/DrnMppr-DTM-AOI.tif')
dtm_arr = dtm.read()

# mask dtm <= 0
elevation_dtm = dtm_arr[0]
elevation_dtm[elevation_dtm <= 0] = np.nan

# create canopy model
canopy_model = (elevation - elevation_dtm)

# compute zonal statistics on each plot
plot_zs = rs.zonal_stats(plots, 
                         canopy_model, 
                         nodata=0, 
                         affine=dem.transform, 
                         geojson_out=True, 
                         copy_properties=True, 
                         stats="sum count min mean max")

# get pixel size
transform = dtm.transform
pixel_size_x = transform[0]
pixel_size_y = -transform[4]

# calculate volume
def volume(pixel_count, pixel_sum):
    return (pixel_sum / pixel_count * (pixel_size_x * pixel_size_y) * pixel_count)

# build dataframe and display first 5 records
plot_df = gpd.GeoDataFrame.from_features(plot_zs)

# add columns to dataframe
plot_df['volume_m3'] = plot_df.apply(lambda x: volume(x['count'], x['sum']), axis=1)
plot_df['area_m2'] = plot_df.apply(lambda x: x.geometry.area, axis=1)

display(plot_df.head())
plot_df.to_csv('output/aoi1_plot_volume.csv')

fig, ax = plt.subplots(figsize=(20, 20))

# plot canopy model
ep.plot_bands(canopy_model, 
              ax=ax, 
              scale=False, 
              cmap="RdYlGn_r", 
              title="Canopy Model", 
              extent=plot_extent,
              vmin=0,
              vmax=5)

# display volume plot
plot_df.plot('volume_m3',
             ax=ax, 
             cmap='hot', 
             edgecolor='black',
             linewidth=1,
             legend=True)

# show plot names
plot_df.apply(lambda x: ax.annotate(s='{0:0.1f}'.format(x['volume_m3']), xy=x.geometry.centroid.coords[0], ha='center'), axis=1);
plt.show()

	geometry	LAYER	MAP_NAME	NAME	min	max	mean	count	sum	volume_m3	area_m2
0	POLYGON Z ((289583.708 5130289.226 0.000, 2895...	Coverage/Quad	User Created Features	2	-0.153473	3.525543	1.532524	4444	6810.538483	76.650368	50.0
1	POLYGON Z ((289588.705 5130289.052 0.000, 2895...	Coverage/Quad	User Created Features	3	-0.094482	3.575226	1.646664	4440	7311.188690	82.285021	50.0
2	POLYGON Z ((289593.702 5130288.877 0.000, 2895...	Coverage/Quad	User Created Features	4	-0.070648	3.992493	1.884596	4440	8367.608276	94.174676	50.0
3	POLYGON Z ((289598.699 5130288.703 0.000, 2896...	Coverage/Quad	User Created Features	5	0.032928	4.575989	2.969443	4444	13196.202637	148.518915	50.0
4	POLYGON Z ((289603.696 5130288.528 0.000, 2896...	Coverage/Quad	User Created Features	6	0.074188	5.105408	3.155879	4442	14018.412506	157.772617	50.0

You can view the Plot 1 AOI plot volume/biomass values: aoi1_plot_volume.csv

Load Plant Count AOI & Count Plants

import pandas as pd
import cv2

# we will use the masked dtm and masked elevation data
plot = gpd.read_file('data/plant_count.shp')

# mask the dtm and dem to the plot extent
dtm_clip, dtm_transform = rasterio.mask.mask(dtm, plot.geometry, crop=True)
dem_clip, dem_transform = rasterio.mask.mask(dem, plot.geometry, crop=True)
rgb_clip, ort_transform = rasterio.mask.mask(ortho, plot.geometry, crop=True)

# filter elevations <= 0
dtm_clip[dtm_clip <= 0] = np.nan
dem_clip[dem_clip <= 0] = np.nan

# filter plant model ground pixels
plant_model = (dem_clip - dtm_clip)
plant_model[plant_model <= 0.20] = np.nan

# generate binary image
binary_image = 255 * (plant_model[0] > 0)
binary_image_int = cv2.bitwise_not(binary_image.astype(np.uint8))

fig, ax = plt.subplots(1, 2, figsize=(20, 20))

# plot plant model
ep.plot_bands(plant_model[0], 
              ax=ax[0], 
              scale=False, 
              cmap="terrain", 
              title="Plant Model", 
              extent=plot_extent)

# plot binary image
ep.plot_bands(binary_image_int, 
              ax=ax[1], 
              scale=False, 
              cmap="binary", 
              title="Binary Plant Mask", 
              extent=plot_extent)
plt.show()

fig, ax = plt.subplots(figsize=(20,20))

# setup basic blob detector
params = cv2.SimpleBlobDetector_Params()
params.minDistBetweenBlobs = 1
params.filterByColor = False
params.blobColor = 255
params.filterByArea = True
params.minArea = 5; 
params.maxArea = 5000; 
params.filterByCircularity = False
params.filterByConvexity = False
params.filterByInertia = True
params.minInertiaRatio = 0.01
params.maxInertiaRatio = 1
detector = cv2.SimpleBlobDetector_create(params)

# build new rgb image
rgb = np.stack((rgb_clip[0], rgb_clip[1], rgb_clip[2]), -1)
rgb = es.bytescale(rgb, high=255, low=0)

# resize binary_image_int to match rgb
binary_image_int = cv2.resize(binary_image_int, dsize=(rgb.shape[1], rgb.shape[0]), interpolation=cv2.INTER_CUBIC)

# detect
keypoints = detector.detect(binary_image_int)

print('Plant count: {}'.format(len(keypoints)))

# plot plant count
plants = cv2.drawKeypoints(rgb, keypoints, np.array([]), (0, 255, 0), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
plt.imshow(plants)
plt.show()

# show full values in dataframe display
pd.set_option('display.float_format', lambda x: '%.6f' % x)
plant_coordinates = {}

# extract plant geo positions
for i, keypoint in enumerate(keypoints):
    plant_center = ortho.xy(keypoint.pt[0], keypoint.pt[1])
    plant_coordinates[i] = [plant_center[0], plant_center[1]]
    
plant_df = pd.DataFrame.from_dict(plant_coordinates, orient='index', columns=['UTMX', 'UTMY'])
display(plant_df.head())
plant_df.to_csv('output/plant_count.csv')

Plant count: 310

	UTMX	UTMY
0	289622.411171	5130240.899705
1	289621.849705	5130244.325966
2	289622.101569	5130248.858655
3	289621.865086	5130253.863387
4	289621.436280	5130258.158493

You can view the plant counts: plant_count.csv

Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
data		data
doc		doc
output		output
DroneMapper Crop Analysis.ipynb		DroneMapper Crop Analysis.ipynb
README.md		README.md
requirements.txt		requirements.txt

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Repository files navigation

DroneMapper Crop Analysis

Included Data

Algorithms

Notes

Do More!

Load Digital Elevation Model and Orthomosaic

Load Plot 1 AOI and Generate NDVI

Generate NDVI Zonal Statistics For Each Plot

Load Plot 2 AOI & Compute DEM Canopy Mean Height For Each Plot

Compute Thermal Mean For Each Plot

Load Plot 1 AOI & Compute Volume/Biomass For Each Plot

Load Plant Count AOI & Count Plants

Thanks! Keep an eye out for future notebooks and algorithms! DroneMapper.com

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dronemapper-io/CropAnalysis

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Repository files navigation

DroneMapper Crop Analysis

Included Data

Algorithms

Notes

Do More!

Load Digital Elevation Model and Orthomosaic

Load Plot 1 AOI and Generate NDVI

Generate NDVI Zonal Statistics For Each Plot

Load Plot 2 AOI & Compute DEM Canopy Mean Height For Each Plot

Compute Thermal Mean For Each Plot

Load Plot 1 AOI & Compute Volume/Biomass For Each Plot

Load Plant Count AOI & Count Plants

Thanks! Keep an eye out for future notebooks and algorithms! DroneMapper.com

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