A drone is an unmanned aerial vehicle (UAV) or uncrewed aerial vehicle that flies without a human pilot onboard. The word DRONE stands for “Dynamic Remotely Operated Navigation Equipment”. It is capable of controlled, sustained level flight and is powered by a jet. Birth of unmanned aerial vehicle happened when Archibald Low developed Ruston Proctor Aerial Target used as a guided weapon during World War I (Newcome, 2004). During the initial periods, UAVs were mainly used for military purposes such as for creating weapons platforms, gathering signals intelligence, and acquiring imagery for both tactical and strategic use.
In today’s world UAVs are using in vast areas, such as military, delivery services, security and law enforcement, search and rescue, films & television industries, disaster management and natural resource management.
The application of drones in natural resource management can be categorized under four principal fields: forestry, precision agriculture and rangeland monitoring, aquatic ecosystem management and natural disaster management. Under forestry, UAVs are used for mapping forests and biodiversity, mapping canopy gaps and measuring forest canopy height and attributes.
Drones can be used for the mapping of forest areas (Koh, L. P., and Wich, S. A. 2012). A small UAV type aircraft is driven over the forest cover for a fixed period of flight time. Images are acquired after the flight and are examined. Shots and videos may also catch different human activities, logging, wildlife or flora species. With the use of drones, we can save time, costs, labor power for these purposes.
Canopy gaps, directly affect regeneration, biodiversity, and productivity of the forests. They act as a center of spots that provide ideal conditions for rapid plant reproduction and growth, maintenance of floristic richness in the understory, and an increase of diversity and structural complexity of the forest habitat (Muscolo et al. 2014). Small gaps cannot be identified accurately from the satellite data, but remote sensing from UAVs can provide an excellent tool for the detection of these small canopy gaps. A study was conducted by Bagaram et al. 2018 on 240 ha of temperate deciduous forest types in Central Italy, containing 50 forest inventory plots each of 529 m2 in size showed that small openings in the canopy cover (75% smaller than 7 m2 ) can be faithfully extracted from UAV red, green, and blue bands (RGB) imagery, using the red band and contrast split segmentation.
Measuring Forest Canopy Height and Attributes
Canopy height is a valuable parameter for assessing the forest covers. Presently LiDAR technology is using for the canopy height measurement(Lefsky et al.2002). Lisein et al. used co-registered light detection and ranging (LiDAR) digital terrain model for the determination of canopy heights. Results were comparable as one obtained from more expensive LiDAR crewed equipment. Drones provide a better determination of canopy heights with low costs.
Drones are also used for wildlife monitoring without causing any disturbances. They are used for quantifying wildlife abundance, habitat and distribution. Drones provide an opportunity for aerial surveys over wider areas for counting the large animals (Jachmann 2001). Traditional methods of surveying consume a lot of time often reach a decade. During that time there may be a chance of extinction of some species (Ferreira & Aarde 2009, Bouché et al. 2012). Thermal cameras and sensors present in drones help them to work in night hours.
Precision agriculture and rangeland monitoring
Land-cover mapping and classification
Land-cover maps provide essential data for a wide range of applications, such as urban planning (Yao, Hao, and Zhang 2016), land suitability analysis (Fernandez, Mourato, and Moreira 2016; Chezgi et al. 2016), natural disaster prediction (Hong et al. 2017), and city modeling (Lu, Hernandez, and Ramsey 2015). Normalized difference vegetation index (NDVI) maps have been used to classify vegetated areas and soil surfaces (McGwire et al. 2016).
Crop health monitoring
Crop health monitoring is one of the most important operations in precision agriculture. It includes pest and disease detection, weed identification, timely spraying of pesticides and weedicides and water stress detection (Stehr, N. J. 2015). The normalized difference vegetation index is used for these purposes (NDVI).
By driving drones over agricultural fields farmers can get an idea about the soil condition at the beginning of crop season. Farmers can plan according to it. Drones can produce 3-D maps of soil analysis which is helpful during crop production. With the help of these maps irrigation management and fertilizer application can be done (Puri et al. 2017).
Planting, irrigation, spraying and other operations.
Drones can be used in each farming operation from planting to harvesting. Agricultural operations involve a lot of mechanization. There is a need for trained and skilled operators. The use of drones for planting can reduce the planting costs by 85%. With speed and precision, each operation can be accomplished (Burema, H., & Filin, A. 2016).
Water is essential for living organisms. The agricultural sector is the largest consumer of freshwater (FAO). With the help of hyperspectral, multispectral, or thermal sensors drones that can identify which parts of a field are dry or need improvements.