Forestry v01

8/9/2019 Forestry v01

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Intelescope

IntelescopeEmpowering Agro Business

Through High-Resolution Aerial Remote Sensing

for Precision Agriculture


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Core Technology & Target Opportunity

1 .

Recognizing & classifying high resolution remotely acquired image data is the core technology of

Intelescope. Precision agriculture, mining and urban planning are potential applications

Intelescope blends expertise

in Remote Sensing, Image

Recognition & Agronomy to

improve agricultural yield.

Remote sensing and imagerecognition technology have

wide applicability above and

beyond agriculture.

Mining applications use

remotely acquiredhyperspectral images.

RemoteSensingImage

Recognition

PrecisionAgriculture

Military Target

Acquisition

Robotics /Computer

Vision

Urban-Planning

Infrastructure

Traffic Detection

Mining &

NaturalResources


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Rich Data in Geographical Information Systems

1


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Next Phase R&D

1

Briefcase deployable Mapping


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page 5

Agenda

Land Parcel Due Diligence, Production Auditing & Land Use

Forestry

Field Crops

UrbanApplications, Counting Cows & Misc


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Demonstration of Due DiligenceAbilities

Following sequence ofimages demonstrate operational resolution & object recognition capabilities.

Isolation of

parcel


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Cropped Parcel Determine Area ofInterest

Potential land parcel acquisitions

can be scrutiniz

ed through aerial

analysis.

Due diligence process is simplified

for very large parcels in difficult to

access areas

Analysis deliverables:

Precise (25cm) geo-

referenced orthophoto map

Precise gradient information

for each pixel (GSD 15cm X

15cm)

Detection of rocks and debris

inhibiting soil conditions

Acidity & fertilization map

(correlated with soil samples)

Soil thickness & water

retention ability


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Area of interest @ 480cm / pixel


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@ 240cm / pixel


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@ 120cm / pixel


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@ 60cm / pixel


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@ 30cm / pixel


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High resolution multispectral aerialimages facilitate the due diligence process.

High Resolution Object Detection for Land Parcel Due Diligence

15cm/Pixel

3.22 m


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Land Use: Classification & Auditing

Patria Soragna (near Orthophoto, GSD 25cm)

Rectified Geo-Referenced Mosaic of

Aerial Photographs in red, green & NIR

bands.

Interpreted Geo-

Referenced Image


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page 15

Each plot type is classified through geometric & radiometric matching .The base image from which

this collage is composed was taken in NIR, Red and Green bands.

Land Classification w/ Legend

Farmed areas

Full vegetation cover

Farmed areas

partial vegetation cover

Water

Constructions

Woodland / Trees

Pasture

Vines

Tree cultivation

Unused

Border delineation

Legend


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page 16

Land Use Audit: Automatic Detection of Vineyards

This image is part of a project for Italian Agriculture Ministry on whether subsidies were

appropriately used to seed & expand vineyards

Automatic detection of

rows and breaks in

vineyards. (Image is in

RGB + CIR)

Image interpreted andgeo-referenced. Relevant

aspects (such as length

of rows, breaks in rows,

seeded area) are stored

in geographic

information database.

Thuschanges overtime can be

tracked automatically.

Analysis Details


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Agenda

Land Parcel Due Diligence, Production Auditing & Land Use

Forestry

Field Crops

UrbanApplications, Counting Cows & Misc


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Each tree in field is counted (labeled with precise GPS location). The biomass of plantation is

measured. This can be used to obtain TER Carbon Credits.

Automatic Tree Recognition & Counting for Carbon Credits

Tree story

+

Height AnomalyID: 47.521990 W

23.593409 S

Euc3301-21/06/06

Slope: 5%

Soil: Terra Rosa


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Tree story

Forestry: Extracting Data from Aerial Photographs


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In this example gaps in tree planting are automatically classified, and measured

Tree Counting: Object Recognition & Classification


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Location of the Pilot


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Area: 16,000 hectares

Photography Date: 24/07/2008


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Areas ofinterests

Sample of 5

plantations


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Plot 15D010_a

15D010_a

15D010_b

15D011_a

15D011_b15D011_c

Plots names


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PlantingHoles Recognition


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Tree Counting

RMS Error: 5%

Each Tree Gets an XYEach Tree Gets an XYCoordinateCoordinate


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SievingHoles according to Size

5-10 m2

10-15 m2

10-20 m2


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Sieving Holes according to

Length-Width Ratio Index1 - 1.5 LWRI

1.5 - 2 LWRI

2 - 3 LWRI

3 - 4 LWRI

< 4 LWRI

Holes

Not Holes

(Row Gaps)


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Orthophoto

Height cross section

Orthophoto 3D view

Taller trees

0m

1m

2m

3m

4m

10m 20m 30m 40m 50m

0m

1m

2m

3m

4m

10m 20m 30m 40m 50m

oad Smaller trees

eight

Distance


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Tree height variability


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Tree height variability


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Orthophoto

Sample

plot


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Plot zoom in

Individual tree mapping

Total: 23,568 trees


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Individual tree mapping

GeoGeo--location is assigned tolocation is assigned to

each treeeach tree

Stand extraction


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Surface elevation model

High

Low


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Terrain elevation model

High

Low


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Plot diagonal view


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Surface elevation model

High

Low


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Terrain elevation model

High

Low


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Tree height extraction

Surface

Terrain

Tree Height


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Tree height model

5 m

3 m


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Individual tree height

5 m

3 m


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Tree height layer + Tree count


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Individual tree height 3D

5 m

3 m


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Plot inventory report

TreeID Xcoordinate

Ycoordinate

Tree height(m)

Actual stand(trees/ha)

1 7988368 427345 3.50 1050

2 7988369 427345 3.25 1050

3 7988369 427348 4.00 1000

4 7988372 427348 4.50 950

5 7988372 427351 4.25 950

6 7988374 427351 4.50 950

7 7988374 427354 4.75 800

8 7988376 427354 5.00 800

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page 46

Vegetative intensity in Soybean and Cotton (using the spectral

index). Exposure of fertilization defects, Brazil.


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page 47

CropHeight (2m)

BiomassBiomass

VolumeVolume

RTS / ATR (total recovered sugars) can be accurately measured. This information can be used to

hedge production. Sugar Cane financers can audit their clients with this information.

Measuring the Biomass and Sugar Cane ATR (RTS)


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page 48

Weed Detection Through Spectral Analysis

Specific Localization of Weeds facilitates less use of herbicides saving costs and minimizing

pollution. Eliminating weeds improves agricultural yields.

OrthoPhoto RGB+CIR Weed localization with GPS coordinates


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Object Detection: Counting Cows

Thermal imaging (middle image) can accurately pinpoint cows on a field. Red Xs (right most image)

denote the location of each detected cow

Documents

Forestry v01