Esri ECCE Blog: Customized Script Tool in ArcGIS

March 03, 2019

I shared a blog post on the Esri Canada ECCE site detailing a Python script tool I developed for ArcGIS. The tool summarizes tree characteristics within a moving search window, making it easier to analyze spatial patterns in forest inventory data.

Summary of the Tool

The script uses ArcPy and pandas to automate the analysis of tree metrics such as diameter at breast height, height, basal area, and defoliation levels within a user-defined buffer around each tree. Users can filter by tree species and define the search radius. The tool outputs a summary table in Excel format, listing local statistics for each tree in the dataset.

The full blog post is available here:
Handy Script Tool to Summarize Tree Characteristics within a Moving Search Window

Code Sample

Here is the full script used in the tool:

```python import arcpy import pandas as pd

Set the working environment

env = r”C:\EsriECCE\Blog” arcpy.env.workspace = env arcpy.env.overwriteOutput = True

Obtain script parameter values

input_FC = arcpy.GetParameterAsText(0) output_TB = arcpy.GetParameterAsText(1) NeighborSize = arcpy.GetParameterAsText(2) Species = arcpy.GetParameterAsText(3)

Store tree IDs (FID) in a list

tree_id = [] with arcpy.da.SearchCursor(input_FC, [“FID”]) as cursor: for row in cursor: tree_id.append(row[0])

Define the function to compute statistics

def statsfunction(tree_id, species): whereclause1 = “”” “FID” = %d “”” % tree_id whereclause2 = “”” “SpeClass” = ‘%s’ “”” % species

arcpy.MakeFeatureLayer_management(input_FC, "Target", whereclause1)

if species == "":
    arcpy.SelectLayerByLocation_management(
        in_layer=input_FC,
        overlap_type="WITHIN_A_DISTANCE",
        select_features="Target",
        search_distance=NeighborSize,
        selection_type="NEW_SELECTION",
        invert_spatial_relationship="NOT_INVERT",
    )
    arcpy.Statistics_analysis(
        in_table=input_FC,
        out_table="C:/Users/think/Documents/ArcGIS/Default.gdb/Statistics",
        statistics_fields="DBH MEAN;TreeHeight MEAN;BA SUM;CurOcDefo MEAN;CumOcDefo MEAN;OBJECTID COUNT",
    )
else:
    arcpy.MakeFeatureLayer_management(input_FC, "FiltedTrees", whereclause2)
    arcpy.SelectLayerByLocation_management(
        in_layer="FiltedTrees",
        overlap_type="WITHIN_A_DISTANCE",
        select_features="Target",
        search_distance=NeighborSize,
        selection_type="NEW_SELECTION",
        invert_spatial_relationship="NOT_INVERT",
    )
    arcpy.Statistics_analysis(
        in_table="FiltedTrees",
        out_table="C:/Users/think/Documents/ArcGIS/Default.gdb/Statistics",
        statistics_fields="DBH MEAN;TreeHeight MEAN;BA SUM;CurOcDefo MEAN;CumOcDefo MEAN;OBJECTID COUNT",
    )

Stats = [None] * 6
with arcpy.da.SearchCursor("Statistics", ["MEAN_DBH", "MEAN_TreeHeight", "SUM_BA", "MEAN_CurOcDefo", "MEAN_CumOcDefo", "COUNT_OBJECTID"]) as cursor:
    for row in cursor:
        for i in range(6):
            Stats[i] = row[i]
    return Stats

Prepare DataFrame to store results

Tree_list = pd.DataFrame(columns=[“AvgDBH”, “AvgHeight”, “TotalBasalArea”, “AvgCurrentDefol”, “AvgCumulativeDefol”, “Count”], index=tree_id)

Loop through each tree and apply the function

for tid in tree_id: temp_list = [] STATS = statsfunction(tid, Species) temp_list += STATS[0:7] Tree_list.loc[tid] = temp_list

Export to Excel

Tree_list.to_excel(output_TB)

Delete temporary statistics table

arcpy.Delete_management(“Statistics”)