Scaling relations between LIDAR and hyperspectral variables: an exploration of similarities in surface properties at the SJER site.

JAMB cooperative (Jess, Annie, Maoya, Brandon)

Objective: explore the relation between surface structural and reflective properties

Indirect approach:

novel representations of both LIDAR and hyperspectral variables over the entire site
- spectral power functions (SPF)
- cumulative distribution function (CDF)
for each variable, compare these representations
- for similarities in structure
- for ecologically relevant characteristics
Use this inspection to generate hypotheses

(NOTE: a direct comparison of HS and LIDAR variables is possible, but not on a statistical basis)



In [55]:

    
# Import the scripts
from create_cdf import *
from canopy_biomass_model import *



In [49]:

    
# Create directory and file list for *.tif files in data dictionary
directory = 'DI_data/'
file_list = [i for i in os.listdir(directory) if i.endswith('.tif')]

# Array to hold sample used for selected index
var_array = np.zeros((len(file_list),500))

# Read in, sample for cdf function
for i in range(0,len(file_list)):
    filename = directory + file_list[i]
    print(filename)
    
    # Grab name for output .npy file
    out_name = file_list[i][0:-4]
    
    # Run function to get cdf arrays
    var_array[i,:] = sample_files(filename)









    



DI_data/ALBD.tif
DI_data/ARVI.tif
DI_data/Aspect.tif
DI_data/CHM.tif
DI_data/DSM.tif
DI_data/DTM.tif
DI_data/EVI.tif
DI_data/fPAR.tif
DI_data/LAI.tif
DI_data/MSI.tif
DI_data/NDII.tif
DI_data/NDLI.tif
DI_data/NDNI.tif
DI_data/NDVI.tif
DI_data/NDWI.tif
DI_data/NMDI.tif
DI_data/PRI.tif
DI_data/SAVI.tif
DI_data/Slope.tif
DI_data/WBI.tif



In [50]:

    
# Initialize cdf info array
cdf_info_array = np.zeros((2,500,var_array.shape[0]))

# Get CDF plotting vars for each variable
for i in range(0,len(file_list)):
    filename = directory + file_list[i]
    print(filename)
    temp = var_array[i,:]
    
    # Grab name for output .npy file
    out_name = file_list[i][0:-4]
    
    # out is a tuple of four arrays
    out = cdf(temp,out_name)
    
    # fill array with plotting variables from above
    cdf_info_array[0,:,i] = np.append(out[0],np.zeros((500-len(out[0]))))
    cdf_info_array[1,:,i] = np.append(out[1],np.zeros((500-len(out[1]))))
    
    # now fill zeros with NaNs
    cdf_info_array[np.where(cdf_info_array==0)] = np.nan









    



DI_data/ALBD.tif
DI_data/ARVI.tif
DI_data/Aspect.tif
DI_data/CHM.tif
DI_data/DSM.tif
DI_data/DTM.tif
DI_data/EVI.tif
DI_data/fPAR.tif
DI_data/LAI.tif
DI_data/MSI.tif
DI_data/NDII.tif
DI_data/NDLI.tif
DI_data/NDNI.tif
DI_data/NDVI.tif
DI_data/NDWI.tif
DI_data/NMDI.tif
DI_data/PRI.tif
DI_data/SAVI.tif
DI_data/Slope.tif
DI_data/WBI.tif

CDF and PS Plots illustrating Albedo



In [74]:

    
# Run function and plot
fig = plt.figure(figsize=(10,5))
ax = fig.add_subplot(121)
ind = 0 # Index for Albedo
X1 = cdf_info_array[0,:,ind]
F1 = cdf_info_array[1,:,ind]
plt.title('CDF %s'%file_list[ind][0:-4])
ax.set_ylabel(file_list[ind][0:-4])
ax.set_xlabel('fraction of pixels')
ax.set_xlim([0,1])
ax.plot(F1,X1)

ax = fig.add_subplot(122)
ind = 0
X1 = cdf_info_array[0,:,ind] #replace with ps
F1 = cdf_info_array[1,:,ind]
plt.title('FFT %s'%file_list[ind][0:-4])
plt.ylabel(file_list[ind][0:-4])
ax.plot(F1,X1, color='green')
plt.show()
#sub.plt

CDF and PS Plots illustrating Canopy Height



In [76]:

    
# Run function and plot
fig = plt.figure(figsize=(10,5))
ax = fig.add_subplot(121)
ind = 3 # Index for Canopy Height
X1 = cdf_info_array[0,:,ind]
F1 = cdf_info_array[1,:,ind]
plt.title('CDF %s'%file_list[ind][0:-4])
ax.set_ylabel(file_list[ind][0:-4])
ax.set_xlabel('fraction of pixels')
ax.set_xlim([0,1])
ax.plot(F1,X1)

ax = fig.add_subplot(122)
ind = 0
X1 = cdf_info_array[0,:,ind] #replace with ps
F1 = cdf_info_array[1,:,ind]
plt.title('FFT %s'%file_list[ind][0:-4])
ax.set_ylabel(file_list[ind][0:-4])
ax.set_xlabel('fraction')
ax.plot(F1,X1, color='red')
plt.show()

CDF and PS Plots illustrating NDVI (Greeness)



In [78]:

    
# Run function and plot
fig = plt.figure(figsize=(10,5))
ax = fig.add_subplot(121)
ind = 14 # Index for NDVI
X1 = cdf_info_array[0,:,ind]
F1 = cdf_info_array[1,:,ind]
plt.title('CDF %s'%file_list[ind][0:-4])
ax.set_ylabel(file_list[ind][0:-4])
ax.set_xlabel('fraction of pixels')
ax.set_xlim([0,1])
ax.plot(F1,X1)

ax = fig.add_subplot(122)
ind = 0
X1 = cdf_info_array[0,:,ind] #replace with ps
F1 = cdf_info_array[1,:,ind]
plt.title('FFT %s'%file_list[ind][0:-4])
ax.set_ylabel(file_list[ind][0:-4])
ax.set_xlabel('fraction')
ax.plot(F1,X1, color='red')
plt.show()

Issues to consider:

Influences of assumptions – for example, stationarity
Use the same extent for both the LIDAR and HS images
How much subsampling is needed? Does it depend on the variable?
Produce variance estimates by repeating the analyses over a subgrid



In [ ]: