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Capture. Analyze. Act. Drone mapping for precision agriculture

FAST AND ACCURATE MAP PROJECTED DATA FOR FARM MANAGEMENT  (New!)

Based on the most important Pixel technique, Aero Hawk Maps 2D is the right software tool that converts your drone-captured imagery into a geo-referenced orthomosaic with all the valuable information that characterizes your farm. Use Aero Hawk Maps 2D  to keep track of the status of your crop on a daily basis.

SAME-DAY DATA FOR SAME-DAY DECISIONS

Time is a critically valuable resource. During the growing season, you need to be capture and evaluate data as quickly and efficiently as possible.   AeroHawk Maps takes your business to the sky so you can capture imagery, view and annotate maps, and then with simple analysis tools you can make the decisions you need to make your agricultural operation soar. 

 

View and share your images online

Use our free online application to view and share your images online, adjust to the color map and setting you want and copy the link to share with others. You will need to use a GeoTiff picture that was generated from the AeroHawk Maps software.

Transfer your images from the AeroHawk camera module

A Windows application for sending images from your M2/M4 to your computer via Wi-Fi or USB.

Mission Planner

Download the latest version of Mission Planner to create your flight plans.

1. Capture

Capture aerial data with Aero Hawk’s drone and camera technologies

2. Analyze

Gain critical insights into your operation for pin-pointed decision-making capability.

3. Act

View, track, organize, create data sets and share in the cloud.

Learn more about using Aero Hawk Maps 2D in your Business.

INDEX ABBREVIATION DEFINITIONS

Plant Health (yellow)      Nitrogen Measurements (green)            Informative (red)

 Blue Triple Band Pass (BBP)

Anthocyanin Reflectance Index. The anthocyanin content in leaves provides a valuable indicator about the physiological status of plants.
Carotenoid Reflectance Index is used for stress tolerance and nutrition.
Edge Normalized Difference Vegetation index is a modification of the traditional broadband NDVI. Applications include precision agriculture, forest monitoring, and vegetation stress detection. This VI differs from the NDVI by using bands along the red edge, instead of the main absorption and reflectance peaks. It capitalizes on the sensitivity of the vegetation red edge to small changes in canopy foliage content, gap fraction, and senescence.
Measurement and Analysis is processing area or Capability of Maturity Model Integration
Vegetation Stress Index.
Green Difference Vegetation index was originally designed with color-infrared photography to predict nitrogen requirements for corn.
Green Normalized Difference Vegetation index is like NDVI except that it measures the green spectrum from 540 to 570 nm instead of the red spectrum. This index is more sensitive to chlorophyll concentration than NDVI.
Green Ratio Vegetation index is sensitive to photosynthetic rates in forest canopies, as green and red reflectances are strongly influenced by changes in leaf pigments.
Normalized Green is the green aspects of the vegetation being concentrated on.with high variability in the canopy structure, or leaf area index.
Structure Insensitive Pigment index maximizes sensitivity to the ratio of bulk carotenoids to chlorophyll while minimizing the impact of the variable canopy structure. It is very useful in areas.

Red Triple Band Pass Filter (RBP)

Anthocyanin Reflectance Index 1 which increment indicates canopy changes in foliage via new growth or death. This index uses reflectance measurements in the visible spectrum to take advantage of the absorption signatures of stress-related pigments. Weakening vegetation contains higher concentrations of anthocyanins, so this index is one measure of stressed vegetation.
Anthocyanin Reflectance Index 2  is a modification to the ARI1 that detects higher concentrations of anthocyanins in vegetation.
This difference vegetation index distinguishes between soil and vegetation, but it does not account for the difference between reflectance and radiance caused by atmospheric effects or shadows.
Normalized Near Infrared is used to find NDVI and ENDVI.
Normalized Red does not take in the difference that NDVI does.
Renormalized Difference Vegetation index uses the difference between near-infrared and red wavelengths, along with the NDVI, to highlight healthy vegetation. It is insensitive to the effects of soil and sun viewing geometry.
This transformed difference vegetation index shows the same sensitivity as the soil adjusted vegetation index (SAVI) to the optical properties of bare soil subjacent to the cover. It does not saturate like NDVI and SAVI and it shows an excellent linearity as a function of the rate of vegetation cover.
Chlorophyll Vegetation Index shows the chlorophyll in detail.
Green Difference Vegetation index was originally designed with color-infrared photography to predict nitrogen requirements for corn.
Green Normalized Difference Vegetation index is like NDVI except that it measures the green spectrum from 540 to 570 nm instead of the red spectrum. This index is more sensitive to chlorophyll concentration than NDVI.
Green Ratio Vegetation index is sensitive to photosynthetic rates in forest canopies, as green and red reflectances are strongly influenced by changes in leaf pigments.
Modified Chlorophyll Absorption Ratio Index Improved is considered a better predictor of green leaf area index (LAI). It incorporates a soil adjustment factor while preserving sensitivity to LAI and resistance to chlorophyll influence.
Modified Triangular Vegetation index makes TVI suitable for LAI estimations by replacing the 750 nm wavelength with 800 nm, whose reflectance is influenced by changes in leaf and canopy structures.
Nitrogen Absorption Index NAI – Nitrogen Absorption Index
NAI 2 – Nitrogen Absorption Index 2
Normalized Green is the green aspects of the vegetation being concentrated on.
Simple Ratio is a ratio of (1) the wavelength with highest reflectance for vegetation and (2) the wavelength of the deepest chlorophyll absorption. The simple equation is easy to understand and is effective over a wide range of conditions. As with the NDVI, it can saturate in dense vegetation when LAI becomes very high.
Transformed Chlorophyll Absorption Reflectance index is one of several CARI indices that indicates the relative abundance of chlorophyll. It is affected by the underlying soil reflectance, particularly in vegetation with a low LAI.
Triangular Vegetation index is calculated as the area of a hypothetical triangle in spectral space that connects (1) green peak reflectance, (2) minimum chlorophyll absorption, and (3) the NIR shoulder. When chlorophyll absorption causes a decrease of red reflectance, and leaf tissue abundance causes an increase in NIR reflectance, the total area of the triangle increases. It is good for estimating green LAI, but its sensitivity to chlorophyll increases with an increase in canopy density.
Global Environmental Monitoring index is a non-linear vegetation index is used for global environmental monitoring from satellite imagery and attempts to correct for atmospheric effects. It is like NDVI but is less sensitive to atmospheric effects. It is affected by bare soil; therefore, it is not recommended for use in areas of sparse or moderately dense vegetation.
Non-Linear index assumes that the relationship between many vegetation indices and surface biophysical parameters is non-linear. It linearizes relationships with surface parameters that tend to be non-linear.
Optimized Soil-Adjusted Vegetation Index is based on the Soil Adjusted Vegetation Index (SAVI). It uses a standard value of 0.16 for the canopy background adjustment factor. Rondeaux (1996) determined that this value provides greater soil variation than SAVI for low vegetation cover, while demonstrating increased sensitivity to vegetation cover greater than 50%. This index is best used in areas with relatively sparse vegetation where soil is visible through the canopy.
Soil Adjusted Vegetation index is like NDVI, but it suppresses the effects of soil pixels. It uses a canopy background adjustment factor, L, which is a function of vegetation density and often requires prior knowledge of vegetation amounts. Huete (1988) suggests an optimal value of L=0.5 to account for first-order soil background variations. This index is best used in areas with relatively sparse vegetation where soil is visible through the canopy.
Infrared Percentage Vegetation index is functionally the same as NDVI, but it is computationally faster. Values range from 0 to 1.
Renormalized Difference Vegetation index uses the difference between near-infrared and red wavelengths, along with the NDVI, to highlight healthy vegetation. It is insensitive to the effects of soil and sun viewing geometry.
Modified Simple Ratio was developed an improvement over RDVI by combining the Simple Ratio into the formula. The result is increased sensitivity to vegetation biophysical parameters.
Global Environmental Monitoring index is a non-linear vegetation index is used for global environmental monitoring from satellite imagery and attempts to correct for atmospheric effects. It is like NDVI but is less sensitive to atmospheric effects. It is affected by bare soil; therefore, it is not recommended for use in areas of sparse or moderately dense vegetation.
The normalized difference vegetation index is a simple graphical indicator that can be used to analyze remote sensing measurements, typically but not necessarily from a space platform, and assess whether the target being observed contains live green vegetation or not.
Non-Linear index assumes that the relationship between many vegetation indices and surface biophysical parameters is non-linear. It linearizes relationships with surface parameters that tend to be non-linear.
This transformed difference vegetation index shows the same sensitivity as the soil adjusted vegetation index (SAVI) to the optical properties of bare soil subjacent to the cover. It does not saturate like NDVI and SAVI and it shows an excellent linearity as a function of the rate of vegetation cover.
Global Environmental Monitoring index is a non-linear vegetation index is used for global environmental monitoring from satellite imagery and attempts to correct for atmospheric effects. It is like NDVI but is less sensitive to atmospheric effects. It is affected by bare soil; therefore, it is not recommended for use in areas of sparse or moderately dense vegetation.
Optimized Soil-Adjusted Vegetation Index is based on the Soil Adjusted Vegetation Index (SAVI). It uses a standard value of 0.16 for the canopy background adjustment factor. Rondeaux (1996) determined that this value provides greater soil variation than SAVI for low vegetation cover, while demonstrating increased sensitivity to vegetation cover greater than 50%. This index is best used in areas with relatively sparse vegetation where soil is visible through the canopy.
Non-Linear index assumes that the relationship between many vegetation indices and surface biophysical parameters is non-linear. It linearizes relationships with surface parameters that tend to be non-linear.
Optimized Soil-Adjusted Vegetation Index is based on the Soil Adjusted Vegetation Index (SAVI). It uses a standard value of 0.16 for the canopy background adjustment factor. Rondeaux (1996) determined that this value provides greater soil variation than SAVI for low vegetation cover, while demonstrating increased sensitivity to vegetation cover greater than 50%. This index is best used in areas with relatively sparse vegetation where soil is visible through the canopy.
Soil Adjusted Vegetation index is like NDVI, but it suppresses the effects of soil pixels. It uses a canopy background adjustment factor, L, which is a function of vegetation density and often requires prior knowledge of vegetation amounts. Huete (1988) suggests an optimal value of L=0.5 to account for first-order soil background variations. This index is best used in areas with relatively sparse vegetation where soil is visible through the canopy.

Fast Map Processing

Get info delivered from drone to desktop in a flash

Crop Stress Detection

Improve crop health with real-time decision-making

Collaborative Insight

Cloud-based sharing program