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A small multi-spectral Imagery Pod to Provide High Resolution Rangeland Management Data



OBJECTIVE: Develop a small multi-spectral imagery pod, to hang under a drone or attached to a rover, capable of providing data to show high resolution vegetation health and distribution, soil properties, soil density, moisture content, etc. to include the “biological soil crust” to assist in restoration efforts and collect data required to comply with the Endangered Species Act (ESA) and the Sikes Act, that would otherwise require “boots on the ground”. 

DESCRIPTION: Vegetation health is a determinate parameter in the management of wildlife. This effort will address the relative heath of vegetation to include the “biological soil crust” and provide data for management activities (e.g. recovery and revegetation). Plants appear green because chlorophyll in the leaves absorbs much of the incident light in the visible wavelengths, particularly the blue and red, while the green color is reflected. Therefore, light reflected by the leaves depends on the amount and various types of leaf pigments that can be used to predict relative health. For example, a water-stressed leaf is known to have low reflectance in the nonvisible wavelengths from about 750 to 1100 nm. Measuring the difference in reflected light at various wavelengths of the electro-magnetic spectrum also makes it possible to distinguish vegetation from soil, green and senescent vegetation, and vegetation species. There has, however, been limited to no application of this technique to rangeland management or to the microbiotic crusts management. Multi-spectral imagery has been widely used to assess crop condition, cover, and growth. Different crop characteristic can be determined based on the band combinations used and include chlorophyll content, biomass and water stress. Hyperspectral imaging is also being used in the detection and diagnostics of disease, nutrient deficiencies, weeds and pests in crop fields. Soil crusts are formed by living organisms and their by-products, creating a surface crust of soil particles bound together by organic materials. Aboveground crust thickness can reach up to 10 cm. The general appearance of the crusts in terms of color, surface topography, and surficial coverage varies. Soil crusts play an important role in the environment. Because they are concentrated in the top 1 to 4 mm of soil, they primarily effect processes that occur at the land surface or soil-air interface. These include soil stability and erosion, atmospheric nitrogen-fixation, nutrient contributions to plants, soil-plant-water relations, infiltration, seedling germination, and plant growth. Crusts are well adapted to severe growing conditions, but poorly adapted to disturbances. Domestic livestock grazing, and more recently, recreational activities and military activities place a heavy toll on the integrity of the crusts. Disruption of the crusts brings decreased organism diversity, soil nutrients, stability, and organic matter. Fire is a common occurrence in many regions where microbiotic crusts grow. Investigations show that fires can cause severe damage, but that recovery is possible. Low-intensity fires do not remove all of the crust structure, which allows for regrowth without significant soil loss. Shrub presence increases the intensity of the fire, decreasing the likelihood of early vegetative or crust recovery. Full recovery of crust from disturbance is a slow process, particularly for mosses and lichens. There are, however, means to facilitate recovery if the location of viable communities can be determined. The proposed technology should be man portable, attached to a rover or drone. The system cannot rely on grid transmitters or receivers and must be appropriate for use in federally designated critical habitat. This is not a request for a drone or rover development. The sensor pod should be self-contained and of a size suitable for use on a drone or rover. 

PHASE I: Research in this phase should focus on device stability in rough terrain to prevent device tip over, bandwidth constraints for operation of the vehicle and camera resolution, battery life/recharging- current batteries require fairly frequent recharging and the method of delivering electricity to the vehicle – i.e. prove of concept. 

PHASE II: It should be focused on system design, manufacturing, environmental maintenance, and quantification of system performance of a pre-production prototype. From the applied research and conceptual design in Phase I, develop a working, scaled- up prototype system. Evaluate if the system can determine the health of vegetation and soil crusts. 

PHASE III: Military Application: Military bases are required by the Sikes Act to manage the wildlife on their bases. Historically, this data has been collected by “boots on the ground” biologist. Field biologist are expensive and sometime difficult to find to provide the data need to comply with the various federal wildlife related laws (ESA, Sikes Act, etc.). Commercial Application: All federal and state agencies that manage land are required by various laws and regulations to manage the wildlife on their land. As on military land, this data has been historically collected by “boots on the ground” biologist. With reduce budgets and limited manpower these agencies must still comply with the various federal wildlife related laws 


1. Carter GA, Knapp AK (2001) Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. Am J Bot 88:677–684; 2. Du Q, French JV, Skaria M, Yang C, Everitt JH (2004) Citrus pest stress monitoring using airborne hyperspectral imagery. In: Conference Proceedings of the International Geoscience and Remote Sensing Symposia 2004, edited by IEEE (Anchorage, USA), Vol. VI, 3981–3984; 3. Jacobi J, Kühbauch W (2005) Site-specific identification of fungal infection and nitrogen deficiency in wheat crop using remote sensing. In: Proceedings of the 5th European Conference on Precision Agriculture, edited by J.V. Stafford (Wageningen Academic Publishers, Netherlands), 73–80

KEYWORDS: Vegetation Health, Multi-Spectral Imagery, Sensor, Detection, Critical Habitat, Federally Threatened Species 

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