Insub-Saharan Africa, rangelands cover 494.2 million hectares. This vast area ischaracterised by harsh conditions with low and erratic rainfall. Historically,these rangelands have diverse plant communities, productive and well adapted tosustain grazing pressure. Nowadays, the ability of the rangelands to performtheir functions is declining, and their biodiversity is threatened. Furthermore,there are several indications that rangeland resources continue to decline inboth productivity and quality (more annual, less palatable and more unpalatablespecies). Thus, it is imperative that these degraded lands are rehabilitatedand better managed.

With theadvances in technology, large areas can be mapped and monitored rapidly.Combining information from low-altitude sensors and geo-spatial data appears tooffer an optimal path for developing a practical system for cost-effective,databased, rangeland monitoring and assessment. If an inventory of rangeresources is established, priority areas with the greatest potential forimprovement can be identified. In collaboration with Oregon State University,the ICARDA Rangeland Ecology & Management Unit (REMU) has been developingquantitative methods to estimate cover using globally positioned digitalimaging coupled with image analysis for several years. The unit has developedsoftware and protocols that are repeatable and technician independent. Theseprotocols use a continuously recording global positioning system (GPS) device,digital camera, and a computer to acquire and manage information. REMU plans todisseminate the technique to all countries in sub- Saharan Africa (Annex 1 is auser guide and provides more information on application of this technique).

One ofthe most important indicators of rangeland condition and health is thepercentage of the soil that is covered and protected from raindrops by plantsand litter. Sequential measurements of cover at seasonal or yearly intervalscan indicate range trend. Unfortunately, plant foliar and litter cover isgenerally not measured. Rather, technicians estimate it using the quadratmethod because quantitative measurement via intercept or point methods istedious and time consuming. Differences in experience and judgment lead tosubstantial differences in cover estimates between technicians and from onesampling period to the next.

Scaling up approaches

Theinnovation is useful to a wide range of users. These are: national agriculturalresearch and development agencies; non-governmental organisations; developmentinstitutions; agricultural universities; and policy makers.

Severaldissemination methods were used to reach the users of the innovations. Thesewere: training (degree training, on-the-job training, group or individualtraining) and conducting research trials using the proposed techniques.

Thefactors essential for successful promotion and wider adoption of the technologyor innovation include: motivation and interest of research partners especiallyyoung scientists; and enabling policies (commitment of the governments insupport of this work). Continuous feedback would be important for fine-tuningthe protocol and for ease of use of the technology.

Challengesencountered in efforts to further disseminate the innovation and to promote itsadoption and scaling up/out include: funds to purchase necessary equipment(hand-held GPS units, digital cameras, specialised/customised software); use ofGPS receivers; availability and access to existing GIS data themes; lack ofenabling policies and effective institutions; and ensuring participation ofyoung scientists.

Recommendationsto address the challenges include: motivation of research partners; commitmentof governments in support of the innovation; strengthening capacity of existinginstitutions; and facilitating establishment of effective institutions.

Promotingparticipation of young women scientists.

Name and address of key scientist:

DrMounir Louhaichi,

ICARDA,Rangeland Ecology & Management Unit;

P. O.Box 5466,

Aleppo,Syria;

Email: M.Louhaichi@cgiar.org;

Tel:+963-21 221 3433;

Fax:+963-21 221 3490

Name and address of key scientistsinvolved in generation of the innovation:

Prof DrDouglas E. Johnson,

OregonState University, Department of Rangeland Ecology & Management,

202Strand Agricultural Hall, Corvallis,

Oregon97331, USA;

Email: Douglas.e.johnson@oregonstate.edu;

Telephone:(541) 737-1624; Fax: (541) 737-0504

ICARDAand its collaborators on this work have been developing quantitative methodsfor estimating cover using globally positioned digital imaging coupled withimage analysis for several years and have developed software and protocols thatare repeatable and technician independent. These protocols use a continuouslyrecording global positioning system (GPS) device, digital camera, and acomputer to acquire and manage information. Specialised software links an imageof the ground to a specific location, rotates and scales the image andprocesses it into meaningful classes such as foliar cover, litter and bareground. Original images, processed images, track logs and photo locations arestored on the computer or a DVD for reference and archiving. Because bothpositioned and processed images are tagged with GPS coordinates, they can beviewed in a geographic information system (GIS).

Materials and methods

Thistechnique requires a staff-mounted digital camera that is positioned to take adigital photograph vertically downward from a fixed height when the platformholding the camera is level (Figure 3.1). It consists of a Bogen Manfrotto 676BMonopod and a Bogen Manfrotto 3025/056 3D Junior Head to which a 5 by 8 inch(13 by 20 cm) platform has been mounted (Booth et al. 2004, Johnson et al.2008). Attached to the platform is a bubble level, magnetic compass,continuously recording GPS unit, and the digital camera. The camera head may bemounted on either a pole of a fixed height or a variable height monopod, aslong as the height does not change during a photo shoot (Figure 3.2).

Image processing using VegMeasuresoftware

VegMeasureis a computerised vegetation measurement program. It is the result of work doneon a series of projects by the Department of Rangeland Ecology and Managementat Oregon State University (USA) that measured and monitored vegetation onrangelands, agronomic fields and riparian areas.

Figure 3.1: Apparatus used to obtain digitalimages of quadrats.

Figure3.2: Acquisition of digital images in the field using a staff-mounted cameraand a continuously recording GPS unit.

Thissoftware package was developed to chart or map vegetation in a sample quadraton the ground, using photographs taken vertically downward. Special emphasiswas given to accurately represent the position and area of plants in thequadrat and document their condition. Previously, hand charting of vegetationin quadrats, a very time consuming and tedious method, was used to collect coverestimates in the field. VegMeasure is a successful attempt at speeding up theprocess. In addition, the protocol presented helps standardise the measurementprocess so that results are more uniform and reflective of the condition ofvegetation growing on a site.

Sinceits beginnings, VegMeasure has also been used to quantify streamside vegetationusing low-altitude aerial photographs, overstorey cover using vertically upwardphotographs from ground level, and distribution of weeds from aerial photographsby capturing distinctive flower colour.

VegMeasurecan be used on permanent quadrats to document change through time, such asspring plant growth or reduction of leaf area by grazing. Charting ofvegetation across several years with VegMeasure has allowed us to quantify theincrease or decrease of specific bunchgrass and forb cover to measure growth.Individual plant persistence and behaviour can also be measured via VegMeasurein vegetative communities (Figure 3.3).

Thesoftware uses information gathered either from a digital camera, video, webcam, or a scan from conventional colour photographs or colon infraredphotographs. These images can be processed either in real-time in the fieldusing a laptop computer or post-processed in the office. Most of the processinguses digital conventional colour images taken in the field using a digitalcamera. Typically, one should use GPS to identify the location of each imagethen process the data later in a laboratory.

Given aset of images, VegMeasure will analyse them according to either a pre-selectedalgorithm or a classification algorithm chosen by the user. The program thenproduces a report indicating the percentage of each class in each image as wellas all images collectively. At its simplest, VegMeasure will classify an imageinto two classes and show the results on screen where the user can evaluate thedata. Also included is a K-means algorithm that divides the image into a seriesof classes. This should facilitate identification of distinctive vegetation inthe image.

VegMeasuresupports a variety of algorithms, which are described in detail later in theuser’s manual. Furthermore, the software supports interfacing to GPS units whenthey are attached via a USB port. When connected, VegMeasure automaticallyrecords the location (in latitude and longitude) of an acquired image.

Figure 3.3: Sample output of processed imagesusing VegMeasure software.