Monitoring canopy-scale autumn leaf phenology at fine-scale using unmanned aerial vehicle (UAV) photography

[1]  A. Deslauriers,et al.  Comparing Time-Lapse PhenoCams with Satellite Observations across the Boreal Forest of Quebec, Canada , 2021, Remote Sensing.

[2]  M. Friedl,et al.  Multiscale assessment of land surface phenology from harmonized Landsat 8 and Sentinel-2, PlanetScope, and PhenoCam imagery , 2021, Remote Sensing of Environment.

[3]  Roel Van Hoolst,et al.  Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe , 2021, Remote Sensing of Environment.

[4]  Lars Eklundh,et al.  Assessing Forest Phenology: A Multi-Scale Comparison of Near-Surface (UAV, Spectral Reflectance Sensor, PhenoCam) and Satellite (MODIS, Sentinel-2) Remote Sensing , 2021, Remote. Sens..

[5]  Jian Li,et al.  Application of conventional UAV-based high-throughput object detection to the early diagnosis of pine wilt disease by deep learning , 2021 .

[6]  E. F. Berra,et al.  Remote sensing of temperate and boreal forest phenology: A review of progress, challenges and opportunities in the intercomparison of in-situ and satellite phenological metrics , 2021 .

[7]  Jonathan Bennie,et al.  Monitoring spring phenology of individual tree crowns using drone‐acquired NDVI data , 2020, Remote Sensing in Ecology and Conservation.

[8]  M. Meroni,et al.  Phenology of short vegetation cycles in a Kenyan rangeland from PlanetScope and Sentinel-2 , 2020, Remote Sensing of Environment.

[9]  Han Lu,et al.  Experimental Evaluation and Consistency Comparison of UAV Multispectral Minisensors , 2020, Remote. Sens..

[10]  Chaoyang Wu,et al.  Using the red chromatic coordinate to characterize the phenology of forest canopy photosynthesis , 2020 .

[11]  P. Ciais,et al.  Spatial variance of spring phenology in temperate deciduous forests is constrained by background climatic conditions , 2019, Nature Communications.

[12]  John Y. Park,et al.  Quantifying Leaf Phenology of Individual Trees and Species in a Tropical Forest Using Unmanned Aerial Vehicle (UAV) Images , 2019, Remote. Sens..

[13]  S. Piao,et al.  A new process-based model for predicting autumn phenology: How is leaf senescence controlled by photoperiod and temperature coupling? , 2019, Agricultural and Forest Meteorology.

[14]  Xiaolin Zhu,et al.  Plant phenology and global climate change: Current progresses and challenges , 2019, Global change biology.

[15]  S. Barr,et al.  Assessing spring phenology of a temperate woodland: A multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations , 2019, Remote Sensing of Environment.

[16]  L. Deng,et al.  UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[17]  Meng Wang,et al.  Dynamics of vegetation autumn phenology and its response to multiple environmental factors from 1982 to 2012 on Qinghai-Tibetan Plateau in China. , 2018, The Science of the total environment.

[18]  A. Skidmore,et al.  Vegetation phenology from Sentinel-2 and field cameras for a Dutch barrier island , 2018, Remote Sensing of Environment.

[19]  Tao Wang,et al.  The Response of Vegetation Phenology and Productivity to Drought in Semi-Arid Regions of Northern China , 2018, Remote. Sens..

[20]  O. Sonnentag,et al.  NDVI derived from near-infrared-enabled digital cameras: Applicability across different plant functional types , 2018 .

[21]  Mark A. Friedl,et al.  Fine-scale perspectives on landscape phenology from unmanned aerial vehicle (UAV) photography , 2018 .

[22]  Sari Metsämäki,et al.  Networked web-cameras monitor congruent seasonal development of birches with phenological field observations , 2017 .

[23]  M. Shen,et al.  Emerging opportunities and challenges in phenology: a review , 2016 .

[24]  Rachel Gaulton,et al.  Use of a digital camera onboard a UAV to monitor spring phenology at individual tree level , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[25]  Andrew D Richardson,et al.  Multiscale modeling of spring phenology across Deciduous Forests in the Eastern United States , 2016, Global change biology.

[26]  Xiaoqiu Chen,et al.  Temperature and geographic attribution of change in the Taraxacum mongolicum growing season from 1990 to 2009 in eastern China’s temperate zone , 2015, International Journal of Biometeorology.

[27]  M. Friedl,et al.  Tracking forest phenology and seasonal physiology using digital repeat photography: a critical assessment. , 2014, Ecological applications : a publication of the Ecological Society of America.

[28]  Mark A. Friedl,et al.  Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery , 2014 .

[29]  Y. Ryu,et al.  Monitoring multi-layer canopy spring phenology of temperate deciduous and evergreen forests using low-cost spectral sensors , 2014 .

[30]  K. Beurs,et al.  Dryland vegetation phenology across an elevation gradient in Arizona, USA, investigated with fused MODIS and Landsat data , 2014 .

[31]  Linhai Jing,et al.  Automated tree crown delineation from imagery based on morphological techniques , 2014 .

[32]  Tommy Dalgaard,et al.  Topographically controlled soil moisture is the primary driver of local vegetation patterns across a lowland region , 2013 .

[33]  Serge Rambal,et al.  Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements , 2013 .

[34]  O. Sonnentag,et al.  Climate change, phenology, and phenological control of vegetation feedbacks to the climate system , 2013 .

[35]  A. Richardson,et al.  Landscape controls on the timing of spring, autumn, and growing season length in mid‐Atlantic forests , 2012 .

[36]  Luis Alonso,et al.  Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content , 2011, Sensors.

[37]  Conghe Song,et al.  Topography-mediated controls on local vegetation phenology estimated from MODIS vegetation index , 2011, Landscape Ecology.

[38]  Takeshi Motohka,et al.  Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology , 2010, Remote. Sens..

[39]  H. Poulos,et al.  Topographic influences on vegetation mosaics and tree diversity in the Chihuahuan Desert Borderlands. , 2010, Ecology.

[40]  T. Akiyama,et al.  Field experiments to test the use of the normalized-difference vegetation index for phenology detection. , 2010 .

[41]  Jan Dempewolf,et al.  The spatial distribution of vegetation types in the Serengeti ecosystem: the influence of rainfall and topographic relief on vegetation patch characteristics , 2009 .

[42]  M. Hill,et al.  Slope, aspect and climate: Spatially explicit and implicit models of topographic microclimate in chalk grassland , 2008 .

[43]  John P. Wilson,et al.  Multi-scale linkages between topographic attributes and vegetation indices in a mountainous landscape , 2007 .

[44]  A. Gitelson,et al.  Three‐band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves , 2006 .

[45]  A. Strahler,et al.  Monitoring vegetation phenology using MODIS , 2003 .

[46]  J. Peñuelas,et al.  Responses to a Warming World , 2001, Science.

[47]  J. Franklin.  Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients , 1995 .

[48]  C. Field,et al.  Relationships Between NDVI, Canopy Structure, and Photosynthesis in Three Californian Vegetation Types , 1995 .

[49]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[50]  Yanjun Su,et al.  Monitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observations , 2021 .

[51]  H. Balslev,et al.  Topographic and spatial controls of palm species distributions in a montane rain forest, southern Ecuador , 2008, Biodiversity and Conservation.