Relationship between leaf physiologic traits and canopy color indices during the leaf expansion period in an oak forest

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecosphere 6, no. 12 (2015): 1-9, doi:10.1890/ES14-00452.1.Plant phenology has a significant impact on the forest ecosystem carbon balance. Detecting plant phenology by capturing the time-series canopy images through digital camera has become popular in recent years. However, the relationship between color indices derived from camera images and plant physiological characters are elusive during the growing season in temperate ecosystems. We collected continuous images of forest canopy, leaf size, leaf area index (LAI) and leaf chlorophyll measured by a soil plant analysis development (SPAD) analyzer in a northern subtropical oak forest in China. Our results show that (1) the spring peak of color indices, Gcc (Green Chromatic Coordinates) and ExG (Excess Green), was 18 days earlier than the 90% maximum SPAD value; (2) the 90% maximum SPAD value coincided with the change point of Gcc and ExG immediately after their spring peak; and (3) the spring curves of Gcc and ExG before their peaks were highly synchronous with the expansion of leaf size and the development of LAI value. We suggest it needs to be adjusted if camera-derived Gcc or ExG is used as a proxy of chlorophyll or gross primary productivity, and images observation should be complemented with field phenological and physiological information to interpret the physiological meaning of leaf seasonality.This research was funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions in the Discipline of Environmental Science and Engineering at Nanjing Forest University, Changjiang River Delta Urban Forest Ecosystem Research of CFERN (to H. Hu) and Brown University Seed Funds for International Research Projects on the Environment (to J. Tang)

Comparison of phenology estimated from reflectance-based indices and solar-induced chlorophyll fluorescence (SIF) observations in a temperate forest using GPP-based phenology as the standard

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Remote Sensing 10 (2018): 932, doi:10.3390/rs10060932.We assessed the performance of reflectance-based vegetation indices and solar-induced chlorophyll fluorescence (SIF) datasets with various spatial and temporal resolutions in monitoring the Gross Primary Production (GPP)-based phenology in a temperate deciduous forest. The reflectance-based indices include the green chromatic coordinate (GCC), field measured and satellite remotely sensed Normalized Difference Vegetation Index (NDVI); and the SIF datasets include ground-based measurement and satellite-based products. We found that, if negative impacts due to coarse spatial and temporal resolutions are effectively reduced, all these data can serve as good indicators of phenological metrics for spring. However, the autumn phenological metrics derived from all reflectance-based datasets are later than the those derived from ground-based GPP estimates (flux sites). This is because the reflectance-based observations estimate phenology by tracking physiological properties including leaf area index (LAI) and leaf chlorophyll content (Chl), which does not reflect instantaneous changes in phenophase transitions, and thus the estimated fall phenological events may be later than GPP-based phenology. In contrast, we found that SIF has a good potential to track seasonal transition of photosynthetic activities in both spring and fall seasons. The advantage of SIF in estimating the GPP-based phenology lies in its inherent link to photosynthesis activities such that SIF can respond quickly to all factors regulating phenological events. Despite uncertainties in phenological metrics estimated from current spaceborne SIF observations due to their coarse spatial and temporal resolutions, dates in middle spring and autumn—the two most important metrics—can still be reasonably estimated from satellite SIF. Our study reveals that SIF provides a better way to monitor GPP-based phenological metrics.This research was supported by U. S. Department of Energy Office of Biological and Environmental Research Grant DE-SC0006951, National Science Foundation Grants DBI 959333 and AGS-1005663, and the University of Chicago and the MBL Lillie Research Innovation Award to Jianwu Tang and China Scholarship Council No. 201506190095 to Z. Liu. Xiaoliang Lu was also supported by the open project grant (LBKF201701) of Key Laboratory of Land Surface Pattern and Simulation, Chinese Academy of Sciences

Boreaalisen metsän lehtialaindeksin ja sen sitoman fotosynteettisesti aktiivisen säteilyn arviointi

The aim of this dissertation is to assess the accuracy of different ground reference methods used to validate satellite based leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (fPAR) products. LAI and fPAR are strongly linked, although they principally and practically measure different properties: LAI quantifies the areal interphase between soil and atmosphere, whereas fPAR quantifies the energy available for photosynthesis. Until now, the development of remote sensing based methods to estimate LAI and fPAR in a boreal forest has been hindered by the scarcity of ground data, which is required to validate and develop existing algorithms. The aim of the first part of this dissertation was to assess the impacts of different methodological approaches to estimate LAI in boreal forests, and to validate satellite based LAI products. Results showed that the accuracy of ground based LAI estimates is sensitive to both the retrieval methods and sampling scheme used to collect the optical LAI data. The satellite based measurements of LAI demonstrated a large temporal variability in LAI. The second part of the dissertation focused on measuring and modeling fPAR in a boreal forest. A new scheme for measuring and modeling ground reference fPAR based on photon recollision probability was presented in this dissertation. Ground reference fPAR is usually estimated only for the forest canopy layer. This study is among the first ones to validate the new global satellite based fPAR product called GEOV1 using data of both the forest canopy and understory layers from boreal forests. Results showed that satellite based fPAR products may correspond better with the total fPAR, instead of only the forest canopy fPAR as has often been presumed.Tämän väitöskirjan tarkoituksena oli kehittää LAI:n ja fPAR:in maastomittausmenetelmiä ja arvioida nykyisten satelliittipohjaisten LAI- ja fPAR-tuotteiden toimivuutta boreaalisissa metsissä. Lehtialaindeksi (leaf area index, LAI) kuvaa lehtien toispuolista pinta-alaa maapinta-alaa kohden (m2/m2). Akronyymi fPAR on lyhennelmä sanoista fraction of absorbed photosynthetically active radiation (PAR) ja se kuvaa kasvillisuuden kykyä sitoa auringosta saapuvaa säteilyä. fPAR määräytyy LAI:n ja auringon kulman perusteella. LAI:ta ja fPAR:ia voidaan arvioida avaruudesta tehtävällä kaukokartoituksella ja mielenkiinnon kohteena voi olla esimerkiksi globaali ympäristön seuranta. Tällä hetkellä kaukokartoitusmenetelmien kehittymistä hidastaa maastoaineistojen puute, sillä maastoaineistot ovat välttämättömiä mallien tarkkuuden arvioinnissa. Koska LAI on yksi tärkeimpiä fPAR:iin vaikuttavia muuttujia, väitöskirjan ensimmäinen osio keskittyi LAI:n maastomittausmenetelmien tarkkuuden arviointiin. Ensimmäisen osan tarkoituksena oli selvittää, kuinka erilaiset LAI:n arviointitavat ja otanta-asetelmat toimivat boreaalisissa metsissä. Satelliitista mitattujen LAI-arvojen kelpoisuutta arvioitiin vertaamalla niitä maastossa mitattuihin arvoihin. Tulosten mukaan erilaiset LAI:n arviointitavat tuottavat systemaattisesti poikkeavia arvioita ja arvioiden tarkkuus riippuu paitsi käytetystä menetelmästä, myös maastomittausten otanta-asetelmasta. Tutkimuksessa havaittiin, että satelliitista mitattuihin LAI-arvoihin sisältyy paljon ajallista ja paikallista vaihtelua, joka johtuu osin satelliitin mittaaman signaalin saturoitumisesta. Väitöskirjan toinen osa keskittyi fPAR:in mittaamiseen ja mallintamiseen. Tutkimuksen aluksi esiteltiin uusi fPAR-malli, joka soveltuu laajojen alueiden fPAR-arviointiin. Mallin toimivuutta arvioitiin vertaamalla mitattuja ja mallinnettuja fPAR-arvoja toisiinsa. fPAR-mallin todettiin toimivan hyvin. Tämän jälkeen tutkittiin, kuinka hyvin nykyiset satelliittimittauksiin perustuvat fPAR-tuotteet vastaavat maastomittauksiin perustuvaa fPARia. Yleensä satelliittituotteiden toimivuutta arvioitaessa on keskitytty vain metsän latvuskerroksen sitoman säteilymäärän arviointiin, mutta tässä tutkimuksessa huomioitiin myös aluskasvillisuuden sitoma säteily. Tulokset osoittivat, että satelliittimittauksiin perustuva fPAR voi vastata paremmin metsikön latvuksen ja aluskasvillisuuden yhteenlaskettua fPAR:ia kuin pelkän latvuskerroksen fPAR:ia

Assessing the accuracy of the MODIS LAI 1-km product in southeastern United States loblolly pine plantations: Accounting for measurement variance from ground to satellite

Leaf area index (LAI), defined here as one-half of the total leaf area per unit ground surface area (Chen, 1996), has been estimated at a global scale from spectral data processed from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard two NASA EOS-AM spacecraft, Terra (launched in 1999) and Aqua (launched in 2002). The MOD15A2 LAI product is a 1 km global data product composited over an 8-day period and is derived from a three-dimensional radiative transfer model driven by an atmosphere corrected surface reflectance product (MOD09), a land cover product (MOD12) and ancillary information on surface characteristics. The United States Environmental Protection Agency (US EPA) initiated validation research (2002) in the evergreen needle leaf biome, as defined in the MOD12 classification, in a regional study located in the southeastern United States. The validation effort was prompted by the potential use of MODIS LAI inputs into atmospheric deposition and biogenic emission models developed within the US EPA Office of Research and Development. The MODIS LAI validation process involves the creation of a high spatial resolution LAI surface map, which when scaled to the MOD15A2 resolution (1 km) allowed for comparison and analysis with the 1 km MODIS LAI product. Creation of this LAI surface map involved: (1) the collection of in situ LAI measurements via indirect optical measurements, (2) the correlation of land cover specific LAI estimates with spectral values retrieved from high resolution imagery (20 m--30 m), and (3) the aggregation of these 30 m cells to 1 km spatial resolution, matching the resolution of the MODIS product and enabling a comparison of the two LAI values (Morisette et al. 2006). This research assessed the uncertainty associated with the creation of the high-resolution LAI reference map, specifically addressing uncertainty in the indirect in situ optical measurements of LAI and the uncertainty in the land cover classification process. Also addressed was the influence of vegetative understory on satellite-derived vegetation indices from the IKONOS sensor

Assessing spring phenology of a temperate woodland : a multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations

PhD ThesisVegetation phenology is the study of plant natural life cycle stages. Plant phenological events are related to carbon, energy and water cycles within terrestrial ecosystems, operating from local to global scales. As plant phenology events are highly sensitive to climate fluctuations, the timing of these events has been used as an independent indicator of climate change. The monitoring of forest phenology in a cost-effective manner, at a fine spatial scale and over relatively large areas remains a significant challenge. To address this issue, unmanned aerial vehicles (UAVs) appear to be a potential new platform for forest phenology monitoring. The aim of this research is to assess the potential of UAV data to track the temporal dynamics of spring phenology, from the individual tree to woodland scale, and to cross-compare UAV results against ground and satellite observations, in order to better understand characteristics of UAV data and assess potential for use in validation of satellite-derived phenology. A time series of UAV data were acquired in tandem with an intensive ground campaign during the spring season of 2015, over Hanging Leaves Wood, Northumberland, UK. The radiometric quality of the UAV imagery acquired by two consumer-grade cameras was assessed, in terms of the ability to retrieve reflectance and Normalised Difference Vegetation Index (NDVI), and successfully validated against ground (0.84≤R2≥0.96) and Landsat (0.73≤R2≥0.89) measurements, but only NDVI resulted in stable time series. The start (SOS), middle (MOS) and end (EOS) of spring season dates were estimated at an individual tree-level using UAV time series of NDVI and Green Chromatic Coordinate (GCC), with GCC resulting in a clearer and stronger seasonal signal at a tree crown scale. UAV-derived SOS could be predicted more accurately than MOS and EOS, with an accuracy of less than 1 week for deciduous woodland and within 2 weeks for evergreen. The UAV data were used to map phenological events for individual trees across the whole woodland, demonstrating that contrasting canopy phenological events can occur within the extent of a single Landsat pixel. This accounted for the poor relationships found between UAV- and Landsat-derived phenometrics (R2<0.45) in this study. An opportunity is now available to track very fine scale land surface changes over contiguous vegetation communities, information which could improve characterization of vegetation phenology at multiple scales.The Science without Borders program, managed by CAPES-Brazil (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior)

Remote sensing technology applications in forestry and REDD+

Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion

Proceedings of the 6th International Workshop of the EARSeL Special Interest Group on Forest Fires Advances in Remote Sensing and GIS Applications in Forest Fire Management Towards an Operational Use of Remote Sensing in Forest Fire Management

During the last two decades, interest in forest fire research has grown steadily, as more and more local and global impacts of burning are being identified. The definition of fire regimes as well as the identification of factors explaining spatial and temporal variations in these fire characteristics are recently hot fields of research. Changes in these fire regimes have important social and ecological implications. Whether these changes are mainly caused by land use or climate warming, greater efforts are demanded to manage forest fires at different temporal and spatial scales. The European Association of Remote Sensing Laboratories (EARSeL)’s Special Interest Group (SIG) on Forest Fires was created in 1995, following the initiative of several researchers studying Mediterranean fires in Europe. It has promoted five technical meetings and several specialised publications since then, and represents one of the most active groups within the EARSeL. The SIG has tried to foster interaction among scientists and managers who are interested in using remote sensing data and techniques to improve the traditional methods of fire risk estimation and the assessment of fire effect. The aim of the 6th international workshop is to analyze the operational use of remote sensing in forest fire management, bringing together scientists and fire managers to promote the development of methods that may better serve the operational community. This idea clearly links with international programmes of a similar scope, such as the Global Monitoring for Environment and Security (GMES) and the Global Observation of Forest Cover/Land Dynamics (GOFC-GOLD) who, together with the Joint Research Center of the European Union sponsor this event. Finally, I would like to thank the local organisers for the considerable lengths they have gone to in order to put this material together, and take care of all the details that the organization of this event requires.JRC.H.3-Global environement monitorin

Habitat suitablity modelling in the New Forest National Park.

The New Forest National Park is a unique semi-natural landscape which contains many species and habitats which are rare and/or threatened. In order to effectively aid in the conservation of these species, particularly in the face of climate change, there is a requirement to know their habitat requirements and distributions within the New Forest. However, due to limited resources there are gaps in knowledge about this for many of these species. Habitat suitability modelling was carried out to suggest unsurveyed sites of potentially suitable habitat (and consequently higher likelihood of species occurrence) for selected species of high conservation concern (Chamaemelum nobile, Galium constrictum, Gladiolus illyricus, Hipparchia semele, Nemobius sylvestris, Pilularia globulifera, Plebejus argus and Poronia punctata). The performance of several modelling approaches was compared. Of the models based on the use of GIS spatial data, an approach requiring only species presence data (Ecological Niche Factor Analysis (ENFA)) was compared to approaches additionally requiring absence or pseudo-absence data (Generalised Linear Models (GLMs) and Generalised Additive Models (GAMs)). An additional approach that did not require GIS data, Bayesian Belief Network (BBN) modelling, was also used to incorporate finer-scale variables not available in GIS format. This relatively new approach to habitat suitability modelling was also used to predict the potential impact of climate change on the suitability of the habitats for the selected species. The evaluation results showed that the presence-absence GLM and GAM models out-performed the presence-only ENFA method, and that the use of pseudo-absences and automated stepwise variable selection proved effective for developing these models. Species with specialist habitat requirements tended to be modelled more accurately than more generalist species. The BBN models also achieved high evaluation values, and were particularly valuable in being able to provide a quantitative assessment of the potential impact of climate change on the selected species. Habitat suitability modelling at the scale of an individual predicted area of the size of the New Forest has so far been rare, as have predictions of climate change on specific species at this scale. However, the results of this research show that this can be a valuable approach to aid in management and conservation of species and their habitats in protected areas