2. Department of Forestry and Natural Resources, Purdue University, 715 West State Street, West Lafayette, IN 47907, USA
Accurate information about forest composition, structure, volume, growth, and extent is essential for sustainable forest management and can be extracted directly or indirectly from remotely sensed imagery (Shao 2012a). Over the past few decades, increasing attention has been focused on improving remote sensing applications in forestry. This is evidenced by the increase in publications in ISI-indexed journals: the results of an ISI-indexed publication search using ''forest'' and ''remote sensing'' as key words reveal one article published in the 1960s; four in the 1970s; 24 in the 1980s; 536 in the 1990s; 2519 in the 2000s; and 2930 from 2010 to 2014. The sharpest jump in the number of publications between the 1980s and 1990s reflects the rapid advances in civilian satellite remote sensing achieved during that period (Boyd and Danson 2005; Shao 2012b). The Landsat program is world's longest remote sensing program for observing Earth resources, and its imagery is most often used in forestry (Alberts 2012).
Along with the development of sensor and computation technologies, remote sensing applications in forestry have evolved from conventional aerial photography-based forest inventories (Lyons 1966) to satellite imagery-based forest resource monitoring (Asner et al. 2005; Tang et al. 2010; Pope et al. 2015), from multispectral data-based forest cover mapping (Zhu and Evans 1994; Shao et al. 1996) to hyperspectral data-based biophysical forest estimations (Martin and Aber 1997; Treitz and Howarth 1999), and from passive remote sensing-based forest leaf area index measurements (Turner et al. 1999; Thakur et al. 2014) to active remote sensing-based forest structure characterizations (Dubayah and Drake 2000; Lefsky et al. 2002). Through the integration of multiple data sources, it is possible to improve estimations of forest volume and biomass (Lu 2006; Koch 2010).
Although remote sensing is widely used in forestry, technical challenges still exist. One of the most critical barriers to remote sensing applications in forestry is the lack of timely data collection over target areas. For example, when one wants to assess pest outbreaks (Wulder et al. 2006) or wildfire spread (Arroyo et al. 2008) in a forested landscape, appropriate satellite imagery might be unavailable and aerial photography from crewed/manned aircrafts might be unaffordable. Moreover, stand-level information is critical for sustainable forestry (Zhang and Jim 2013) but cannot be extracted from medium-or coarseresolution remote sensing approaches. Flexible and inexpensive remote sensing systems can help supplement existing remote sensing capabilities and explore new applications. Drones as remote sensing platforms have the potential to increase the efficiency of data acquisition, but their applications are still at an experimental stage (Ambrosia et al. 2011a; Wing et al. 2013; Shahbazi et al. 2014). Here, we briefly review the fundamental concepts and initial applications of drone-based remote sensing in forestry research and hope to guide forestry professionals and researchers to better apply this new geospatial technology.Drones as platforms
The Merriam-Webster Dictionary defines a drone as ''an unmanned aircraft or ship guided by remote control or onboard computers''. Drones are also referred to as unmanned aerial vehicles (UAV), unmanned aerial systems (UAS), unmanned aircrafts (UA), or remotely piloted aircrafts (RPA). Sometimes the term UAV is purposely changed to UAS to reflect the complex systems that are involved in drone operations. The term RPA is typically used by the military (Ambrosia et al. 2011a). Initial forms of drones were developed in the early 20th century (Zaloga 2008). Since the 1950s, drones' major mission has been aerial reconnaissance. As of 2005, the development of drones for military purposes still dominated the drone industry (Colomina and Molina 2014). Over the past few decades, drones of various shapes, sizes, and capabilities have been developed so rapidly that their potentials for civilian applications are overwhelming (Colomina and Molina 2014).
There are different classifications of drones. For example, Watts et al. (2012) classified drones into seven categories: MAV (Micro or Miniature) or NAV (Nano Air Vehicles), VTOL (Vertical Take-Off & Landing), LASE (Low Altitude, Short Endurance), LASE Close, LALE (Low Altitude, Long Endurance), MALE (Medium Altitude, Long Endurance), and HALE (High Altitude, Long Endurance). Anderson and Gaston (2013) classified drones into four size classes: large, medium, small and mini, and micro and nano. Furthermore, others have classified drones into five usage types: target and decoy, reconnaissance, combat, research and development, and civil and commercial (www.theuav.com).
There are two major types of drones based on takeoff and landing techniques: horizontal takeoff and landing and vertical takeoff and landing. Horizontal takeoff and landing are typical characteristics of fixed-wing drones (airplanes), whereas rotorcrafts or rotary-wing drones (helicopters and autogyros), ships, and balloons perform vertical takeoff and landing. For the purpose of remote sensing applications, stability and flight range are critical considerations in drone development. Fixed-and rotary-wing drones perform differently in terms of stability and range. When field coverage is large, fixed-wing drones may be preferable, whereas rotary-wing drones may be more suitable for achieving high spatial resolution measurements. A notable advantage of fixed-wing drones is that minimal experience is required to operate them. However, as more rotor blades are added, the risk of system crashes is reduced for rotarywing drones (Anderson and Gaston 2013). Large fixedwing drones need a runway to take off and land, whereas smaller ones can be launched manually or by using ground stands or vehicles.
Power source directly affects flight endurance (Dudek et al. 2013), and is thus, one of the most important elements of drone equipment. Although internal combustion engines are common for state-of-the-art drones, electrical motors are better choices for smaller drones. Because electrical motors are more economical and vibrate less, they are more suitable for remote sensing applications. Various batteries and fuel cells are available to power electrical motors on drones. In the future, solar energy will be able to help increase flight endurance from hours to days and even years.
Maximum payload weight determines how heavy a suitable sensor on a drone should be. Heavier sensors generally require larger drones. The weight of drone payload ranges from dozens of grams to hundreds of kilograms.
Drones fly autonomously or through remote control/piloting. Autonomous flights are preprogrammed each time with computers and are suitable for systematic landscape mapping. Remotely piloted drones are effective for providing photographs and videos at a local level.Drone remote sensing
The earliest aerial photographs were taken in the 1860s using balloons as platforms. During World Wars Ⅱ and Ⅰ, airplanes were used as aerial photographic platforms. The first generation of Earth-orbiting satellites was launched into space around 1960, and they were exclusively made for reconnaissance. Previously, all the data collected from the Earth's atmosphere or space was in analog form (i.e., film). Since the 1970s, digital sensors have been employed, satellites have been launched for civil applications, and the term remote sensing has become formal. In addition, airborne remote sensing and spaceborne/satellite remote sensing have gradually become well-known terms. Drones had been used for reconnaissance for decades before they were employed for civilian purposes, but their recent application to remote sensing was exceptionally fast. Currently, drones are found in the fields of meteorology, precision agriculture, wildlife research, forestry, land management, infrastructure inspection, traffic monitoring, epidemic emergencies, natural disaster management, and wilderness search and rescue (Shahbazi et al. 2014). We recommend calling this large-scale remote sensing initiative drone remote sensing to distinguish it from crewed aircraft remote sensing and make it comparable to satellite remote sensing (Fig. 1).
Drones can carry a variety of sensing instruments, including visible light, near infrared (NIR), shortwave infrared (SWIR), thermal infrared (TIR), Radar, and Lidar sensors. Drone-borne optical sensors, including visible, NIR, and SWIR, also record data as multispectral or hyperspectral bands (Berni et al. 2009; Saari et al. 2011; Shao 2015). Owing to advances in sensor technology, increasingly smaller, lighter, and cheaper sensors have become available for drone remote sensing applications. Anderson and Gaston (2013) and Colomina and Molina (2014) provide detailed introductions to various drone remote sensing systems.Application examples
The benefits of drone remote sensing include low material and operational costs, flexible control of spatial and temporal resolution, high-intensity data collection, and absence of risk to crews. The following pioneer studies demonstrate the unique advantages of drone remote sensing for forestry research and practices.Surveying forests
Koh and Wich (2012) experimented with using drone remote sensing to survey and map tropical forests in Indonesia because of the high costs of high-resolution satellite remote sensing data, frequent cloud cover, and difficult/expensive ground surveys. The experiment involved an inexpensive ( < $100) and lightweight (~650 g) fixed-wing drone powered by a 2200 mAh battery and a still/video camera. This drone could fly for ~25 min per mission and cover a total distance of ~15 km. The researchers assembled the drone images to develop land use/cover maps at a spatial resolution of 5.1 cm, used the video footage to detect human activities (e.g., burning and logging), and combined the photographic and video information to survey wildlife species and identify flora. They suggested that using drone remote sensing could lead to significant savings in terms of time, manpower, and financial resources for local conservation workers and researchers in the developing tropics. Such applications especially make sense for community-based forest monitoring and forestry programs, such as REDD (Reducing Emissions from Deforestation and forest Degradation), in developing countries (Paneque-Galvez et al. 2014). Many forest surveyors have started to enhance forest measurements with drone remote sensing (Fig. 2).
Forest canopy gaps reflect disturbance and affect forest diversity and productivity, but small forest gaps cannot be measured accurately with satellite remote sensing (Frolking et al. 2009). Getzin et al. (2012) obtained 7-cm-resolution, natural-color images for beech-dominated deciduous and deciduous-coniferous mixed forests in Germany, as such resolution permits the accurate identification of gap objects as small as 1 m2. With a wing span of 2 m and a weight of 6 kg, the drone could fly up to 250 m in altitude for up to 60 min. The flight lines were preprogrammed, and all images were orthorectified based on the internal drone orientation, GPS position, and a digital terrain model. The researchers found strong correlations between the biodiversity measurements and forest gap metrics obtained from drone remote sensing. This study suggests that drone remote sensing is capable of acquiring very high-resolution images suitable for characterizing forest gaps as reliable indicators of biodiversity. The key to the successful high-resolution image acquisition in this study was that the drone could fly at low altitudes. An important imaging product is the digital surface model, which is also useful for measuring forest canopy height, as described below.Measuring forest canopy height
Forest canopy height is a critical parameter of forest quantification, and it is traditionally estimated with analog photogrammetric methods and ground surveys. Lidar technologies have become a new means for estimating canopy height (Lefsky et al. 2002), and traditional photogrammetry has almost been abandoned in forestry. This is particularly the case as drone-borne Lidar technologies are becoming more affordable (Tulldahl and Larsson 2014; Wallace et al. 2014). In addition, high-resolution, lowoblique drone-borne optical imagery promotes forest canopy height measurements through the integration of digital photogrammetry and structure-from-motion techniques (Siebert and Teizer 2014).
Lisein et al. (2013) demonstrated a similar approach for a forest in Belgium. They used a small fixed-wing drone with a wingspan of 1 m, a weight of 2 kg, a cruise speed of 80 km/h, a flight height of 100–750 m, and maximum flight duration of 40 min to obtain NIR images with a spatial resolution of ~7.6 cm. The measurements of forest canopy height derived from the inexpensive photogrammetric method were in strong agreement with Lidar data obtained from an expensive crewed aircraft.
Zarco-Tejada et al. (2014) quantified the tree height in 158 ha of forestland in Spain using a low-cost camera mounted on a fixed-wing drone with a 2-m wingspan and a flight speed of 63 km/h. A low-cost camera was modified to obtain color infrared images at a spatial resolution of 5 cm. The tree height values obtained from the images were accurate when they were compared with groundmeasured values.
A study by Dandois and Ellis (2013) was conducted with a similar photogrammetric approach in the U.S. They acquired overlapping aerial photographs with a consumergrade camera mounted on a small rotary-wing drone that flew at low altitude ( < 130 m). The study site covered three 625-ha deciduous forests in the state of Maryland. They were satisfied with the ability of this approach to observe 3D canopy phenology at high temporal resolutions. Such inexpensive but effective technologies for the multispectral 3D scanning of vegetation provide foresters with another good reason to apply drone remote sensing to their research.
The three experiments described above suggest that this inexpensive optical approach has a similar accuracy to more complex and costly Lidar systems.Tracking forest wildfires
The use of remote sensing to support real-time fire-control tactics is in its infancy (Wing et al. 2014). The MODIS satellite imagery has a high temporal resolution (1–2 days revisiting time) and is commonly applied in forest wildfire monitoring and management. However, the low spatial resolution of MODIS is insufficient for this task at local scales. Crewed aircraft deployments for the real-time monitoring of the spread of forest wildfires are potentially unsafe to crews.
Between 2006 and 2010, NASA and the US Forest Service demonstrated the employment of a large, longduration (24 h), fixed-wing drone for assisting forest wildfire management (Ambrosia et al. 2011b; Hinkley and Zajkowski 2011). The drone weighed nearly five tons and could carry instruments weighing up to one ton. The researchers mounted a multispectral scanner to autonomously collect image data with 16 bands, ranging from visible to TIR. The TIR-band information provided enhanced wildfire images. The drone remote sensing missions provided near real-time (5–10 min) intelligence to support forest wildfire management.
The ability of rotary-wing drones to detect forest wildfires was also tested in Portugal and Spain (Martinez-de Dios et al. 2011; Merino et al. 2012). A series of experiments indicated that rotary-wing drones could effectively collect real-time data of forest wildfires. Specifically, the simultaneous use of multiple drones, either autonomous or remotely controlled, allowed larger areas to be measured and obtained complementary views of wildfires. Mediumand high-altitude drones are more suitable for flying over wildfire areas.Supporting intensive forest management
Intensive forest management is an effective approach to promoting forest productivity and meeting the increasing demand for timber (Arano and Munn 2006; Bai et al. 2015). The management of fast-growing forest plantations is similar to the practice of precision agriculture and can be promoted with drone remote sensing (Wang et al. 2014). A typical exercise is applying fertilizer at the right time and in the right place. Felderhof and Gillieson (2011) acquired NIR imagery with drone remote sensing to map tree canopy health in a macadamia plantation. They found a significant correlation between spectral radiometry and leaf nitrogen levels determined by field sampling. This approach exploited drones to help cut the cost of intensive forest management and increase economic returns.
Forest density control is a traditional forestry practice that is critical for maximizing forest productivity. With drone remote sensing, it is possible to obtain wall-to-wall forest density information at a landscape level (Fig. 2b).
The pruning of forest plantations is important for increasing timber quality and value. Wallace et al. (2014) conducted an experiment to obtain three-return Lidar data of a 4-year-old Eucalyptus stand located in Tasmania, Australia. The scanner platform was a rotary-wing drone, which could fly at a low altitude (40 m) and at low speed. This way they obtained high-density point clouds (145 and 220 pulses/m2). The Lidar data were collected before and after pruning. Individual tree crowns were automatically segmented from the data and both crown volume and base height were determined using the geometry of the point cloud. The results showed significant differences in canopy properties between unpruned and pruned stems, indicating that the drone-based Lidar system could effectively distinguish pruning treatments. At the individual tree-level, there were moderate correlations between Lidar-derived and field-measured crown base heights. As laser scanners continuously become lighter, they can be mounted on smaller drones (Tulldahl and Larsson 2014), making their forestry applications more efficient and affordable.Concluding remarks
The five types of applications discussed above demonstrate the advantages drone remote sensing has over established remote-sensing methods. Continuous developments of new drones and sensors are making remote sensing applications more appealing. Some of the applications that can be accomplished inexpensively with drone remote sensing are difficult to conduct using crewed aircraft remote sensing or satellite remote sensing. Without crews, drones can be extremely small, and their flight endurance can be increased as a result of greater fuel economy (Wing et al. 2014). Some drones are so small that they can fly near forest canopies; some drones are big enough to fly at medium or high altitudes and can fly repeatedly to record the extent of an ongoing wildfire without jeopardizing crews' safety.
In the field of modern forestry, rational forest management requires detailed forest information in digital format (Tang et al. 2009). Forestry has a history of employing aerial photography to obtain forest information. During the past a half century, satellite imagery has become an important data source for forestry research. The present form of drone remote sensing applications in forestry is still at an initial stage, but it is reasonable to anticipate that the role of drone remote sensing will surpass crewed aircraft remote sensing in the near future. The flexibility and low cost of drone remote sensing is beneficial to transforming conventional forestry data into digital formats, enabling forestry practices to become more precise and efficient (Zhao et al. 2005).
Nevertheless, there are challenges for conducting regulated, safe, and comprehensive applications of drone remote sensing. Specifically, platforms, sensors, operations, and environments all have constraints (Anderson and Gaston 2013). Wing et al. (2013) explained that smaller drones are more susceptible to weather and human-related accidents, and therefore, may incur potentially large expenses due to damage from hitting the ground. The high diversity of platforms and sensors enables broad applications of drone remote sensing, however, inconsistencies in these technologies present barriers to standardized and upscaled applications of drone remote sensing. Unfortunately, there are no multidisciplinary collaborations to promote the standardization of drone remote sensing development (Shahbazi et al. 2014). It is important to systematically and continuously conduct comparative studies to determine the appropriate drone remote sensing technologies for various forest conditions and/or forestry purposes.
Alberts K (2012) Landsat data characteristics and holdings. A presentation of USGS Landsat Ground System Lead (http://www.slideserve.com/keahi/landsat-data-characteristics-and-holdings)
Ambrosia V, Hutt M, Lulla K (2011a) Special issue: unmanned airborne systems (UAS) for remote sensing applications. Geocarto Int 26(2): 69-70. DOI:10.1080/10106049.2011.553507
Ambrosia V, Wegener S, Zajkowski T, Sullivan D, Buechel S, Enomoto F, Lobitz B, Johan S, Brass J, Hinkley E (2011b) The Ikhana unmanned airborne system (UAS) western states fire imaging missions: from concept to reality (2006-2010). Geocarto Int 26(2): 85-101. DOI:10.1080/10106049.2010.539302
Anderson K, Gaston KJ (2013) Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front Ecol Environ 11(3): 138-146. DOI:10.1890/120150
Arano KG, Munn IA (2006) Evaluating forest management intensity: a comparison among major forest landowner types. Forest Policy Econ 9(3): 237-248. DOI:10.1016/j.forpol.2005.07.011
Arroyo LA, Pascual C, Manzanera JA (2008) Fire models and methods to map fuel types: the role of remote sensing. For Ecol Manag 256(6): 1239-1252. DOI:10.1016/j.foreco.2008.06.048
Asner G, Knapp D, Broadbent P, Keller M, Silva J (2005) Selective logging in the Brazilian Amazon. Science 310(5747): 480-482. DOI:10.1126/science.1118051
Bai GX, Wang YY, Dai LM, Liu SR, Tang LN, Shao GF (2015) Market-oriented forestry in China promotes forestland productivity. New Forest 46(1): 1-6. DOI:10.1007/s11056-014-9450-8
Berni JAJ, Zarco-Tejada PJ, Suarez L, Fereres E (2009) Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans Geosci Remote Sens 47(3): 722-738. DOI:10.1109/TGRS.2008.2010457
Boyd DS, Danson FM (2005) Satellite remote sensing of forest resources: three decades of research development. Prog Phys Geogr 29(1): 1-26. DOI:10.1191/0309133305pp432ra
Colomina I, Molina P (2014) Unmanned aerial systems for photogrammetry and remote sensing: a review. ISPRS J Photogramm Remote Sens 92: 79-97. DOI:10.1016/j.isprsjprs.2014.02.013
Dandois JP, Ellis EC (2013) High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision. Remote Sens Environ 136: 259-276. DOI:10.1016/j.rse.2013.04.005
Dubayah RO, Drake JB (2000) Lidar remote sensing for forestry. J Forest 98(6): 44-46.
Dudek M, Tomczyk P, Wygonik P, Korkosz M, Bogusz P, Lis B (2013) Hybrid fuel cell—battery system as a main power unit for small unmanned aerial vehicles (UAV). Int J Electrochem Sci 8(6): 8442-8463.
Felderhof L, Gillieson D (2011) Near-infrared imagery from unmanned aerial systems and satellites can be used to specify fertilizer application rates in tree crops. Can J Remote Sens 37(4): 376-386.
Frolking S, Palace MW, Clark DB, Chambers JQ, Hugart HH, Hurtt GC (2009) Forest disturbance and recovery: a general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure. J Geophys Res 114: G00E02. DOI:10.1029/2008JG000911
Getzin S, Wiegand K, Schöning I (2012) Assessing biodiversity in forests using very high-resolution images and unmanned aerial vehicles. Methods Ecol Evol 3(2): 397-404. DOI:10.1111/j.2041-210X.2011.00158.x
Hinkley E, Zajkowski T (2011) USDA forest service-NASA: unmanned aerial systems demonstrations—pushing the leading edge in fire mapping. Geocarto Int 26(2): 103-111. DOI:10.1080/10106049.2011.555823
Koch B (2010) Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment. ISPRS J Photogramm Remote Sens 65(6): 581-590. DOI:10.1016/j.isprsjprs.2010.09.001
Koh LP, Wich SA (2012) Dawn of drone ecology: low-cost autonomous aerial vehicles for conservation. Trop Conserv Sci 5: 121-132. DOI:10.1177/194008291200500202
Lefsky MA, Cohen WB, Parker GG, Harding DJ (2002) Lidar remote sensing for ecosystem studies. Bioscience 52(1): 19-30. DOI:10.1641/0006-3568(2002)052[0019:LRSFES]2.0.CO;2
Lisein J, Pierrot-Deseilligny M, Bonnet S, Lejeune P (2013) A photogrammetric workflow for the creation of a forest canopy height model from small unmanned aerial system imagery. Forests 4(4): 922-944. DOI:10.3390/f4040922
Lu DS (2006) The potential and challenge of remote sensing-based biomass estimation. Int J Remote Sens 27(7): 1297-1328. DOI:10.1080/01431160500486732
Lyons EH (1966) Fixed air-base 70 mm photography, a new tool for forest sampling. For Chron 42(4): 420-431. DOI:10.5558/tfc42420-4
Martin ME, Aber JD (1997) High spectral resolution remote sensing of forest canopy lignin, nitrogen, and ecosystem processes. Ecol Appl 7(2): 431-443. DOI:10.1890/1051-0761(1997)007[0431:HSRRSO]2.0.CO;2
Martinez-de Dios JR, Merino L, Caballero F, Ollero A (2011) Automatic forest-fire measuring using ground stations and unmanned aerial systems. Sensors 11(6): 6328-6353.
Merino L, Caballero F, Martínez-de-Dios JR, Iván M, Aníbal O (2012) An unmanned aircraft system for automatic forest fire monitoring and measurement. J Intell Rob Syst 65(1-4): 553-548.
Paneque-Galvez J, McCall MK, Napoletano BM, Wich SA, Koh LP (2014) Small drones for community-based forest monitoring: an assessment of their feasibility and potential in tropical areas. Forests 5(6): 1481-1507. DOI:10.3390/f5061481
Pope I, Bowen D, Harbor J, Shao GF, Zanotti L, Burniske G (2015) Deforestation of montane cloud forest in the central highlands of Guatemala: contributing factors and implications for sustainability in Q'eqchi' communities. Int J Sustain Dev World Ecol 22: 201-212. DOI:10.1080/13504509.2014.998738
Saari H, Antila T, Holmlund C, Mäkynen J, Ojala K, Toivanen H, Pellikka I, Tuominen S, Pesonen L, Heikkilä J (2011) Unmanned aerial vehicle (UAV) operated spectral camera system for forest and agriculture applications. In: Proceedings of the SPIE 8174: id 81740H.
Shahbazi M, Théau J, Ménard P (2014) Recent applications of unmanned aerial imagery in natural resource management. GISci Remote Sens. DOI:10.1080/15481603.2014.926650
Shao GF (2012a) Remote sensing. In: El-Shaarawi A-H, Piegorsch W (eds) Encyclopedia of environmetrics, 2nd edn. Wiley, Chichester, pp 2187-2193
Shao GF (2012b) Satellite data. In: El-Shaarawi A-H, Piegorsch W (eds) Encyclopedia of environmetrics, 2nd edn. Wiley, Chichester, pp 2390-2395
Shao GF (2015) Optical remote sensing. In: Richardson D (ed) The international encyclopedia of geography: people, the earth, environment, and technology, 2nd edn. Wiley, Chichester, pp 2390-2395
Shao GF, Zhao G, Zhao SD, Shugart HH, Wang SX, Schaller J (1996) Forest cover types derived from landsat TM imagery for Changbai Mountain Area of China. Can J For Res 26(2): 206-216. DOI:10.1139/x26-024
Siebert S, Teizer J (2014) Mobile 3D mapping for surveying earthwork projects using an unmanned aerial vehicle (UAV) system. Autom Constr 41: 1-14. DOI:10.1016/j.autcon.2014.01.004
Tang LN, Shao GF, Dai LM (2009) Roles of digital technology in China's sustainable forestry development. Int J Sustain Dev World Ecol 16(2): 94-101. DOI:10.1080/13504500902794000
Tang LN, Shao GF, Piao ZJ, Dai LM, Jenkins MA, Wang SX, Wu G, Wu JG, Zhao JZ (2010) Forest degradation deepens around and within protected areas in East Asia. Biol Conserv 143(5): 1295-1298. DOI:10.1016/j.biocon.2010.01.024
Thakur T, Swamy SL, Nain AS (2014) Composition, structure and diversity characterization of dry tropical forest of Chhattisgarh using satellite data. J For Res 25(4): 819-825. DOI:10.1007/s11676-014-0486-6
Treitz PM, Howarth PJ (1999) Hyperspectral remote sensing for estimating biophysical parameters of forest ecosystems. Prog Phys Geogr 23(3): 359-390. DOI:10.1177/030913339902300303
Tulldahl HM, Larsson H. 2014. Lidar on small UAV for 3D mapping. In: Proceedings of SPIE sponsored conference on electro-optical remote sensing, photonic technologies, and applications Ⅷ; and military applications in hyperspectral imaging and high spatial resolution sensing Ⅱ. Amsterdam, Netherlands, 22-23
Sep 2014 Turner DP, Cohen WB, Kennedy RE, Karin S (1999) Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites. Remote Sens Environ 70(1):52-68
Wallace L, Watson C, Lucieer A (2014) Detecting pruning of individual stems using airborne laser scanning data captured from an unmanned aerial vehicle. Int J Appl Earth Obs Geoinf 30: 76-85. DOI:10.1016/j.jag.2014.01.010
Wang YY, Bai GX, Shao GF, Cao YK (2014) An analysis of potential investment returns and their determinants of poplar plantations in state-owned forest enterprises of China. New Forest 45(2): 251-264. DOI:10.1007/s11056-014-9406-z
Watts AC, Ambrosia VG, Hinkley EA (2012) Unmanned aircraft systems in remote sensing and scientific research: classification and considerations of use. Remote Sens 4(6): 1670-1692.
Wing MG, Burnett J, Sessions J, Brungardt J, Cordell V, Dobler D, Wilson D (2013) Eyes in the sky: remote sensing technology development using small unmanned aircraft systems. J Forest 111(5): 341-347. DOI:10.5849/jof.12-117
Wing MG, Burnett JD, Sessions J (2014) Remote sensing and unmanned aerial system technology for monitoring and quantifying forest fire impacts. Int J Remote Sens Appl 4(1): 18-35. DOI:10.14355/ijrsa.2014.0401.02
Wulder MA, Dymond CC, White JC, Leckie DG, Carroll AL (2006) Surveying mountain pine beetle damage of forests: a review of remote sensing opportunities. For Ecol Manag 221(1-3): 27-41. DOI:10.1016/j.foreco.2005.09.021
Zaloga SJ (2008) Unmanned aerial vehicles: Robotic air warfare. New York: Osprey Publishing: 1917-2007.
Zarco-Tejada PJ, Diaz-Varela R, Angileri V, Loudjani P (2014) Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. Eur J Agron 55: 89-99. DOI:10.1016/j.eja.2014.01.004
Zhang H, Jim CY (2013) Species adoption for sustainable forestry in Hong Kong's degraded countryside. Int J Sustain Dev World Ecol 20(6): 484-503. DOI:10.1080/13504509.2013.818590
Zhao G, Shao GF, Reynolds K, Wimberly MC, Warner T, Moser JM, Rennolls K, Magnussen S, Kohl M, Andersen HE, Mendoza GA, Dai LM, Huth A, Zhang LJ, Brey J, Sun YJ, Ye RH, Martin BA, Li FR (2005) Digital forestry: a white paper. J Forest 103(1): 47-50
Zhu ZL, Evans DL (1994) U.S. forest types and predicted percent forest cover from AVHRR data. Photogramm Eng Remote Sens 60(5): 525-531.