Máy bay không người lái cung cấp dữ liệu không gian và thể tích để mang lại những hiểu biết mới về mô hình vi khí hậu
Tóm tắt
Vi khí hậu (biến động nhiệt độ ở quy mô nhỏ trong phạm vi mét gần bề mặt Trái Đất) có ảnh hưởng lớn đến khả năng tồn tại và hoạt động của các sinh vật trên cạn. Việc hiểu cách mà các điều kiện khí hậu địa phương thay đổi là một thách thức để đo lường với độ phân giải không-thời gian phù hợp. Các mô hình vi khí hậu cung cấp phương tiện để giải quyết giới hạn này, nhưng yêu cầu làm đầu vào, đo lường hoặc ước lượng nhiều biến môi trường mô tả sự biến thiên của thực vật và địa hình.
Mô tả các thành phần chính của các mô hình vi khí hậu và các tham số môi trường liên quan. Khám phá tiềm năng của máy bay không người lái trong việc cung cấp dữ liệu quy mô thích hợp để đo các tham số môi trường như vậy.
Chúng tôi giải thích cách các cảm biến gắn trên máy bay không người lái có thể cung cấp dữ liệu liên quan trong bối cảnh các sản phẩm cảm biến từ xa thay thế. Chúng tôi cung cấp ví dụ về cách các phép đo khí tượng vi mô trực tiếp có thể được thực hiện bằng máy bay không người lái. Chúng tôi chỉ ra cách dữ liệu thu thập được từ máy bay không người lái có thể được tích hợp vào các mô hình truyền năng lượng bức xạ 3 chiều, bằng cách cung cấp một mô hình thực tế về cảnh quan mà từ đó mô hình hóa sự tương tác của năng lượng mặt trời với thực vật.
Chúng tôi nhận thấy rằng đối với một số biến môi trường (tức là địa hình và chiều cao tán), các kỹ thuật thu thập và xử lý dữ liệu đã được thiết lập, cho phép sản xuất dữ liệu phù hợp cho các mô hình vi khí hậu. Đối với các tham số khác như kích thước lá, các kỹ thuật vẫn còn mới nhưng cho thấy triển vọng. Đối với hầu hết các tham số, việc kết hợp các đặc trưng cảnh quan không gian từ dữ liệu máy bay không người lái và dữ liệu bổ sung từ nghiên cứu trong phòng thí nghiệm và thực địa sẽ là một cách hiệu quả để tạo ra các đầu vào ở quy mô không-thời gian liên quan.
Máy bay không người lái cung cấp một cơ hội thú vị để định lượng cấu trúc và độ không đồng nhất của cảnh quan ở độ phân giải nhỏ, từ đó phù hợp với quy mô để cung cấp những hiểu biết mới về vi khí hậu.
Từ khóa
Tài liệu tham khảo
Agisoft (2020) Agisoft Metashape Professional (Version 1.5.5)
Allen LH, Sinclair TR, Lemon ER (1976) Radiation and Microclimate Relationships in Multiple Cropping Systems. In: ASA Special Publication. American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America
Alvarez-Vanhard E, Houet T, Mony C et al (2020) Can UAVs fill the gap between in situ surveys and satellites for habitat mapping? Remote Sens Environ 243:111780. https://doi.org/10.1016/j.rse.2020.111780
Anders N, Valente J, Masselink R, Keesstra S (2019) Comparing filtering techniques for removing vegetation from UAV-based photogrammetric point clouds. Drones 3:61. https://doi.org/10.3390/drones3030061
Anderson K, Westoby MJ, James MR (2019) Low-budget topographic surveying comes of age: structure from motion photogrammetry in geography and the geosciences. Prog Phys Geogr: Earth Environ 43:163–173. https://doi.org/10.1177/0309133319837454
Badura GP, Bachmann CM, Tyler AC et al (2019) A novel approach for deriving LAI of salt marsh vegetation using structure from motion and multiangular spectra. IEEE J Sel Topics Appl Earth Obs Remote Sens 12:599–613. https://doi.org/10.1109/JSTARS.2018.2889476
Bendig J, Yu K, Aasen H et al (2015) Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int J Appl Earth Obs Geoinf 39:79–87. https://doi.org/10.1016/j.jag.2015.02.012
Bennie J, Huntley B, Wiltshire A et al (2008) Slope, aspect and climate: spatially explicit and implicit models of topographic microclimate in chalk grassland. Ecol Model 216:47–59. https://doi.org/10.1016/j.ecolmodel.2008.04.010
Borra-Serrano I, Swaef TD, Muylle H et al (2019) Canopy height measurements and non-destructive biomass estimation of Lolium perenne swards using UAV imagery. Grass Forage Sci 74:356–369. https://doi.org/10.1111/gfs.12439
Brüllhardt M, Rotach P, Schleppi P, Bugmann H (2020) Vertical light transmission profiles in structured mixed deciduous forest canopies assessed by UAV-based hemispherical photography and photogrammetric vegetation height models. Agric For Meteorol 281:107843. https://doi.org/10.1016/j.agrformet.2019.107843
Calders K, Origo N, Disney M et al (2018) Variability and bias in active and passive ground-based measurements of effective plant, wood and leaf area index. Agric For Meteorol 252:231–240. https://doi.org/10.1016/j.agrformet.2018.01.029
Campbell GS (1986) Extinction coefficients for radiation in plant canopies calculated using an ellipsoidal inclination angle distribution. Agric For Meteorol 36:317–321. https://doi.org/10.1016/0168-1923(86)90010-9
Cao H, Liu Y, Yue X, Zhu W (2017) Cloud-assisted UAV data collection for multiple emerging events in distributed WSNs. Sensors 17:1818. https://doi.org/10.3390/s17081818
Cao C, Lee X, Muhlhausen J et al (2018) Measuring landscape albedo using unmanned aerial vehicles. Remote Sens 10:1812. https://doi.org/10.3390/rs10111812
Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens Environ 62:241–252. https://doi.org/10.1016/S0034-4257(97)00104-1
Cassano JJ (2014) Observations of atmospheric boundary layer temperature profiles with a small unmanned aerial vehicle. Antarct Sci 26:205–213. https://doi.org/10.1017/S0954102013000539
Chazdon RL (2003) Tropical forest recovery: legacies of human impact and natural disturbances. Perspect Plant Ecol Evol Syst 6:51–71. https://doi.org/10.1078/1433-8319-00042
Chen C (2015) Determining the leaf emissivity of three crops by infrared thermometry. Sensors 15:11387–11401. https://doi.org/10.3390/s150511387
Choi F, Gouhier T, Lima F et al (2019) Mapping physiology: biophysical mechanisms define scales of climate change impacts. Conserv Physiol. https://doi.org/10.1093/conphys/coz028
Coops NC, Waring RH, Landsberg JJ (1998) Assessing forest productivity in Australia and New Zealand using a physiologically-based model driven with averaged monthly weather data and satellite-derived estimates of canopy photosynthetic capacity. For Ecol Manag 104:113–127. https://doi.org/10.1016/S0378-1127(97)00248-X
Copernicus Climate Change Service (C3S) (2017) ERA5: fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS)
Cowan IR (1972) Mass and heat transfer in laminar boundary layers with particular reference to assimilation and transpiration in leaves. Agric Meteorol 10:311–329. https://doi.org/10.1016/0002-1571(72)90035-0
Dash JP, Watt MS, Paul TSH et al (2019) Early detection of invasive exotic trees using UAV and manned aircraft multispectral and LiDAR data. Remote Sens 11:1812. https://doi.org/10.3390/rs11151812
Duffy JP, Cunliffe AM, DeBell L et al (2018) Location, location, location: considerations when using lightweight drones in challenging environments. Remote Sens Ecol Conserv 4:7–19. https://doi.org/10.1002/rse2.58
Ehbrecht M, Schall P, Ammer C, Seidel D (2017) Quantifying stand structural complexity and its relationship with forest management, tree species diversity and microclimate. Agric For Meteorol 242:1–9. https://doi.org/10.1016/j.agrformet.2017.04.012
Faye E, Rebaudo F, Yánez-Cajo D et al (2016) A toolbox for studying thermal heterogeneity across spatial scales: from unmanned aerial vehicle imagery to landscape metrics. Methods Ecol Evol 7:437–446. https://doi.org/10.1111/2041-210X.12488
Finn A, Rogers K, Meade J et al (2019) Spatio-temporal observations of temperature and wind velocity using drone-based acoustic atmospheric tomography. J Acoust Soc Am 145:1903–1904. https://doi.org/10.1121/1.5101906
Finnigan JJ (1985) Turbulent transport in flexible plant canopies. The forest atmosphere interaction. Reidel, Dordrecht, pp 443–480
Finnigan J (2000) Turbulence in plant canopies. Annu Rev Fluid Mech 32:519–571. https://doi.org/10.1146/annurev.fluid.32.1.519
Forsmoo J, Anderson K, Macleod CJA et al (2019) Structure from motion photogrammetry in ecology: does the choice of software matter? Ecol Evol. https://doi.org/10.1002/ece3.5443
Garzonio R, Di Mauro B, Colombo R, Cogliati S (2017) Surface reflectance and sun-induced fluorescence spectroscopy measurements using a small hyperspectral UAS. Remote Sens 9:472. https://doi.org/10.3390/rs9050472
Gastellu-Etchegorry J-P, Yin T, Lauret N et al (2015) Discrete Anisotropic Radiative Transfer (DART 5) for modeling airborne and satellite spectroradiometer and LIDAR acquisitions of natural and urban landscapes. Remote Sens 7:1667–1701. https://doi.org/10.3390/rs70201667
Getzin S, Nuske RS, Wiegand K (2014) Using Unmanned Aerial Vehicles (UAV) to quantify spatial gap patterns in forests. Remote Sens 6:6988–7004. https://doi.org/10.3390/rs6086988
Goudriaan J (1977) Crop micrometeorology: a simulation study. Phd, Pudoc
Hay JE (1993) Calculating solar radiation for inclined surfaces: practical approaches. Renew Energy 3:373–380. https://doi.org/10.1016/0960-1481(93)90104-O
Hoffmann H, Jensen R, Thomsen A et al (2016) Crop water stress maps for an entire growing season from visible and thermal UAV imagery. Biogeosciences 13:6545–6563. https://doi.org/10.5194/bg-13-6545-2016
Holman FH, Riche AB, Michalski A et al (2016) High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing. Remote Sens 8:1031. https://doi.org/10.3390/rs8121031
Inoue K, Uchijima Z (1979) Experimental study of microstructure of wind turbulence in rice and maize canopies. Bull Natl Inst Agric Sci Ser A Phys Stat, pp 1–88
Jackson T, Shenkin A, Wellpott A et al (2019) Finite element analysis of trees in the wind based on terrestrial laser scanning data. Agric For Meteorol 265:137–144. https://doi.org/10.1016/j.agrformet.2018.11.014
Jacquemoud S, Verhoef W, Baret F et al (2009) PROSPECT + SAIL models: a review of use for vegetation characterization. Remote Sens Environ 113:S56–S66. https://doi.org/10.1016/j.rse.2008.01.026
Jiang Z, Huete AR, Didan K, Miura T (2008) Development of a two-band enhanced vegetation index without a blue band. Remote Sens Environ 112:3833–3845. https://doi.org/10.1016/j.rse.2008.06.006
Kasperbauer MJ (1987) Far-red light reflection from green leaves and effects on phytochrome-mediated assimilate partitioning under field conditions. Plant Physiol 85:350–354. https://doi.org/10.1104/pp.85.2.350
Kearney MR, Porter WP (2017) NicheMapR—an R package for biophysical modelling: the microclimate model. Ecography 40:664–674. https://doi.org/10.1111/ecog.02360
Kearney MR, Matzelle A, Helmuth B (2012) Biomechanics meets the ecological niche: the importance of temporal data resolution. J Exp Biol 215:922–933. https://doi.org/10.1242/jeb.059634
Kelliher FM, Leuning R, Raupach MR, Schulze E-D (1995) Maximum conductances for evaporation from global vegetation types. Agric For Meteorol 73:1–16. https://doi.org/10.1016/0168-1923(94)02178-M
Kim D-W, Yun HS, Jeong S-J et al (2018) Modeling and testing of growth status for chinese cabbage and white radish with UAV-based RGB imagery. Remote Sens 10:563. https://doi.org/10.3390/rs10040563
Klosterman S, Richardson A (2017) Observing spring and fall phenology in a deciduous forest with aerial drone imagery. Sensors 17:2852. https://doi.org/10.3390/s17122852
Kucharik CJ, Norman JM, Gower ST (1999) Characterization of radiation regimes in nonrandom forest canopies: theory, measurements, and a simplified modeling approach. Tree Physiol 19:695–706. https://doi.org/10.1093/treephys/19.11.695
Lapen DR, Martz LW (1993) The measurement of two simple topographic indices of wind sheltering-exposure from raster digital elevation models. Comput Geosci 19:769–779. https://doi.org/10.1016/0098-3004(93)90049-B
Lawrence DA, Balsley BB (2013) High-resolution atmospheric sensing of multiple atmospheric variables using the DataHawk small airborne measurement system. J Atmos Ocean Technol 30:2352–2366. https://doi.org/10.1175/JTECH-D-12-00089.1
Lembrechts JJ, Lenoir J, Roth N et al (2019) Comparing temperature data sources for use in species distribution models: from in situ logging to remote sensing. Glob Ecol Biogeogr. https://doi.org/10.1111/geb.12974
Levy CR, Burakowski E, Richardson AD (2018) Novel measurements of fine-scale albedo: using a commercial quadcopter to measure radiation fluxes. Remote Sens 10:1303. https://doi.org/10.3390/rs10081303
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 4:922–944. https://doi.org/10.3390/f4040922
Liu X, Wang L (2018) Feasibility of using consumer-grade unmanned aerial vehicles to estimate leaf area index in Mangrove forest. Remote Sens Lett 9:1040–1049. https://doi.org/10.1080/2150704X.2018.1504339
MacHattie LB, McCormack RJ (1961) Forest microclimate: a topographic study in Ontario. J Ecol 49:301–323. https://doi.org/10.2307/2257264
Maclean IMD, Mosedale JR, Bennie JJ (2019) Microclima: an R package for modelling meso- and microclimate. Methods Ecol Evol 10:280–290. https://doi.org/10.1111/2041-210X.13093
Maki T (1975) Interrelationships between zero-plane displacement, aerodynamic roughness length and plant canopy height. J Agric Meteorol 31:7–15. https://doi.org/10.2480/agrmet.31.7
McGill PR, Reisenbichler KR, Etchemendy SA et al (2011) Aerial surveys and tagging of free-drifting icebergs using an unmanned aerial vehicle (UAV). Deep Sea Res Part II 58:1318–1326. https://doi.org/10.1016/j.dsr2.2010.11.007
McNeil BE, Pisek J, Lepisk H, Flamenco EA (2016) Measuring leaf angle distribution in broadleaf canopies using UAVs. Agric For Meteorol 218–219:204–208. https://doi.org/10.1016/j.agrformet.2015.12.058
Meesuk V, Vojinovic Z, Mynett AE, Abdullah AF (2015) Urban flood modelling combining top-view LiDAR data with ground-view SfM observations. Adv Water Resour 75:105–117. https://doi.org/10.1016/j.advwatres.2014.11.008
Met Office (2012): Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations Data (1853-current). NCAS British Atmospheric Data Centre, 13/10/2020. http://catalogue.ceda.ac.uk/uuid/220a65615218d5c9cc9e4785a3234bd0
Milling CR, Rachlow JL, Olsoy PJ et al (2018) Habitat structure modifies microclimate: an approach for mapping fine-scale thermal refuge. Methods Ecol Evol 9:1648–1657. https://doi.org/10.1111/2041-210X.13008
Mlambo R, Woodhouse IH, Gerard F, Anderson K (2017) Structure from motion (SfM) photogrammetry with drone data: a low cost method for monitoring greenhouse gas emissions from forests in developing countries. Forests 8:68. https://doi.org/10.3390/f8030068
Moeser D, Roubinek J, Schleppi P et al (2014) Canopy closure, LAI and radiation transfer from airborne LiDAR synthetic images. Agric For Meteorol 197:158–168. https://doi.org/10.1016/j.agrformet.2014.06.008
Moeslund JE, Arge L, Bøcher PK et al (2013) Topographically controlled soil moisture drives plant diversity patterns within grasslands. Biodivers Conserv 22:2151–2166. https://doi.org/10.1007/s10531-013-0442-3
Monin AS, Obukhov AM (1954) Basic laws of turbulent mixing in the surface layer of the atmosphere. Trudy geofiziceskiy institut AN SSSR 24:163–187
Monteith J, Unsworth M (1990) Principles of environmental physics: plants, animals and the atmosphere. Edward Arnold, London
Norman J (1982) Simulation of microclimates. In: Biometeorology in integrated pest management. Academic Press, New York
North PRJ (1996) Three-dimensional forest light interaction model using a Monte Carlo method. IEEE Trans Geosci Remote Sens 34:946–956. https://doi.org/10.1109/36.508411
Palomaki RT, Rose NT, van den Bossche M et al (2017) Wind estimation in the lower atmosphere using multirotor aircraft. J Atmos Ocean Technol 34:1183–1191. https://doi.org/10.1175/JTECH-D-16-0177.1
Pettorelli N, Nagendra H, Williams R et al (2015) A new platform to support research at the interface of remote sensing, ecology and conservation. Remote Sens Ecol Conserv 1:1–3. https://doi.org/10.1002/rse2.1
Pincebourde S, Murdock CC, Vickers M, Sears MW (2016) Fine-scale microclimatic variation can shape the responses of organisms to global change in both natural and urban environments. Integr Comp Biol 56:45–61. https://doi.org/10.1093/icb/icw016
Potter KA, Woods HA, Pincebourde S (2013) Microclimatic challenges in global change biology. Glob Change Biol 19:2932–2939. https://doi.org/10.1111/gcb.12257
Raupach MR (1989) A practical Lagrangian method for relating scalar concentrations to source distributions in vegetation canopies. Q J R Meteorol Soc 115:609–632. https://doi.org/10.1002/qj.49711548710
Raupach MR, Finnigan JJ, Brunei Y (1996) Coherent eddies and turbulence in vegetation canopies: the mixing-layer analogy. Bound-Layer Meteorol 78:351–382. https://doi.org/10.1007/BF00120941
Reichenau TG, Korres W, Montzka C et al (2016) Spatial heterogeneity of leaf area index (LAI) and its temporal course on arable land: combining field measurements, remote sensing and simulation in a comprehensive data analysis approach (CDAA). PLoS ONE 11:e0158451. https://doi.org/10.1371/journal.pone.0158451
Richardson LF (1922) Weather prediction by numerical process. Cambridge University Press, Cambridge
Roosjen PPJ, Brede B, Suomalainen JM et al (2018) Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data—potential of unmanned aerial vehicle imagery. Int J Appl Earth Obs Geoinf 66:14–26. https://doi.org/10.1016/j.jag.2017.10.012
Roth L, Aasen H, Walter A, Liebisch F (2018) Extracting leaf area index using viewing geometry effects—a new perspective on high-resolution unmanned aerial system photography. ISPRS J Photogramm Remote Sens 141:161–175. https://doi.org/10.1016/j.isprsjprs.2018.04.012
Ryan JC, Hubbard A, Box JE et al (2017) Derivation of high spatial resolution albedo from UAV digital imagery: application over the greenland ice sheet. Front Earth Sci. https://doi.org/10.3389/feart.2017.00040
Sankey T, Donager J, McVay J, Sankey JB (2017) UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA. Remote Sens Environ 195:30–43. https://doi.org/10.1016/j.rse.2017.04.007
Seier G, Sulzer W, Lindbichler P et al (2018) Contribution of UAS to the monitoring at the Lärchberg-Galgenwald landslide (Austria). Int J Remote Sens 39:5522–5549. https://doi.org/10.1080/01431161.2018.1454627
Sellers PJ (1985) Canopy reflectance, photosynthesis and transpiration. Int J Remote Sens 6:1335–1372. https://doi.org/10.1080/01431168508948283
Shaw RH, Pereira AR (1982) Aerodynamic roughness of a plant canopy: a numerical experiment. Agric Meteorol 26:51–65. https://doi.org/10.1016/0002-1571(82)90057-7
Smith MW, Carrivick JL, Quincey DJ (2016) Structure from motion photogrammetry in physical geography. Prog Phys Geogr: Earth Environ 40:247–275. https://doi.org/10.1177/0309133315615805
Sørensen L, Jacobsen L, Hansen J (2017) Low cost and flexible UAV deployment of sensors. Sensors 17:154. https://doi.org/10.3390/s17010154
Stokes VJ, Morecroft MD, Morison JIL (2006) Boundary layer conductance for contrasting leaf shapes in a deciduous broadleaved forest canopy. Agric For Meteorol 139:40–54. https://doi.org/10.1016/j.agrformet.2006.05.011
Stow D, Nichol CJ, Wade T et al (2019) Illumination geometry and flying height influence surface reflectance and NDVI derived from multispectral UAS imagery. Drones 3:55. https://doi.org/10.3390/drones3030055
Suggitt AJ, Wilson RJ, Isaac NJB et al (2018) Extinction risk from climate change is reduced by microclimatic buffering. Nat Clim Change 8:713–717. https://doi.org/10.1038/s41558-018-0231-9
Teng P, Ono E, Zhang Y et al (2019) Estimation of ground surface and accuracy assessments of growth parameters for a sweet potato community in ridge cultivation. Remote Sens 11:1487. https://doi.org/10.3390/rs11121487
Tewes A, Schellberg J (2018) Towards remote estimation of radiation use efficiency in maize using UAV-based low-cost camera imagery. Agronomy 8:16. https://doi.org/10.3390/agronomy8020016
Tian J, Wang L, Li X et al (2017) Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest. Int J Appl Earth Obs Geoinf 61:22–31. https://doi.org/10.1016/j.jag.2017.05.002
Tucci G, Parisi E, Castelli G et al (2019) Multi-sensor UAV application for thermal analysis on a dry-stone terraced vineyard in rural tuscany landscape. ISPRS Int J Geo-Inf 8:87. https://doi.org/10.3390/ijgi8020087
van Zyl JJ (2001) The Shuttle Radar Topography Mission (SRTM): a breakthrough in remote sensing of topography. Acta Astronaut 48:559–565. https://doi.org/10.1016/S0094-5765(01)00020-0
Vierling KT, Vierling LA, Gould WA et al (2008) Lidar: shedding new light on habitat characterization and modeling. Front Ecol Environ 6:90–98. https://doi.org/10.1890/070001
Wang Q, Adiku S, Tenhunen J, Granier A (2005) On the relationship of NDVI with leaf area index in a deciduous forest site. Remote Sens Environ 94:244–255. https://doi.org/10.1016/j.rse.2004.10.006
Webster C, Westoby M, Rutter N, Jonas T (2018) Three-dimensional thermal characterization of forest canopies using UAV photogrammetry. Remote Sens Environ 209:835–847. https://doi.org/10.1016/j.rse.2017.09.033
Weiss SB, Weiss AD (1998) Landscape-level phenology of a threatened butterfly: a GIS-based modeling approach. Ecosystems 1:299–309. https://doi.org/10.1007/s100219900023
Winstral A, Marks D, Gurney R (2009) An efficient method for distributing wind speeds over heterogeneous terrain. Hydrol Process 23:2526–2535. https://doi.org/10.1002/hyp.7141
Wong SC, Cowan IR, Farquhar GD (1979) Stomatal conductance correlates with photosynthetic capacity. Nature 282:424–426. https://doi.org/10.1038/282424a0
Yao X, Wang N, Liu Y et al (2017) Estimation of wheat LAI at middle to high levels using unmanned aerial vehicle narrowband multispectral imagery. Remote Sens 9:1304. https://doi.org/10.3390/rs9121304
Zarco-Tejada PJ, Guillén-Climent ML, Hernández-Clemente R et al (2013) Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV). Agric For Meteorol 171–172:281–294. https://doi.org/10.1016/j.agrformet.2012.12.013
Zellweger F, De Frenne P, Lenoir J et al (2019) Advances in microclimate ecology arising from remote sensing. Trends Ecol Evol 34:327–341. https://doi.org/10.1016/j.tree.2018.12.012
Zhao B, Zhang J, Yang C et al (2018) Rapeseed seedling stand counting and seeding performance evaluation at two early growth stages based on unmanned aerial vehicle imagery. Front Plant Sci. https://doi.org/10.3389/fpls.2018.01362