Journal of Resources and Ecology ›› 2019, Vol. 10 ›› Issue (5): 481-493.DOI: 10.5814/j.issn.1674-764X.2019.05.003
• Plant Ecosystem • Previous Articles Next Articles
Received:
2019-03-19
Accepted:
2019-05-13
Online:
2019-09-30
Published:
2019-10-11
Contact:
ZHOU Yuke
Supported by:
ZHOU Yuke. Greenness Index from Phenocams Performs Well in Linking Climatic Factors and Monitoring Grass Phenology in a Temperate Prairie Ecosystem[J]. Journal of Resources and Ecology, 2019, 10(5): 481-493.
Add to citation manager EndNote|Ris|BibTeX
URL: http://www.jorae.cn/EN/10.5814/j.issn.1674-764X.2019.05.003
Location | Elevation | Camera description | Camera orientation | Vegetation type | North America ecoregion | Dominant species |
---|---|---|---|---|---|---|
47.8993°N, 97.3161°W | 268 m | StarDot NetCam SC | North | Grassland | No. 9 Temperate prairies | Andropogon gerardii, Distichlis spicata, Muhlenbergia richardsonis |
Location | Elevation | Camera description | Camera orientation | Vegetation type | North America ecoregion | Dominant species |
---|---|---|---|---|---|---|
47.8993°N, 97.3161°W | 268 m | StarDot NetCam SC | North | Grassland | No. 9 Temperate prairies | Andropogon gerardii, Distichlis spicata, Muhlenbergia richardsonis |
Data name | Start date | End date | Frequency | Integrated frequency | Spatial scope |
---|---|---|---|---|---|
Gcc | 2015-4-1 | 2018-9-11 | 30 min | Daily | Landscape scale |
Modis NDVI(EVI) | 2015-1-1 | 2018-9-11 | 16 day | Semimonthly | 2.5 km×2.5 km |
VIIRS NDVI(EVI) | 2015-1-1 | ||||
Air Temperature (Oakville) | 2018-3-18 | 2018-9-11 | 15 min | Daily | Point |
Air Temperature (Grand Forks) | 2015-4-1 | 2018-9-11 | Daily | Daily | Point |
Precipitation (Oakville) | 2018-3-18 | 2018-9-11 | 15 min | Daily | Point |
Precipitation (Grand Forks) | 2015-4-1 | 2018-9-11 | Daily | Daily | Point |
GDD (Grand Forks) | 2015-4-1 | 2018-9-11 | Daily | Daily | Point |
Soil Water Content | 2018-3-18 | 2018-9-11 | 15 min | Daily | Point |
Soil Temperature 2" | 2018-3-18 | 2018-9-11 | 15 min | Daily | Point |
Soil Temperature 4" | 2018-3-18 | 2018-9-11 | 15 min | Daily | Point |
Soil Temperature 8" | 2018-3-18 | 2018-9-11 | 15 min | Daily | Point |
Soil Temperature 20" | 2018-3-18 | 2018-9-11 | 15 min | Daily | Point |
Soil Temperature 40" | 2018-3-18 | 2018-9-11 | 15 min | Daily | Point |
Data name | Start date | End date | Frequency | Integrated frequency | Spatial scope |
---|---|---|---|---|---|
Gcc | 2015-4-1 | 2018-9-11 | 30 min | Daily | Landscape scale |
Modis NDVI(EVI) | 2015-1-1 | 2018-9-11 | 16 day | Semimonthly | 2.5 km×2.5 km |
VIIRS NDVI(EVI) | 2015-1-1 | ||||
Air Temperature (Oakville) | 2018-3-18 | 2018-9-11 | 15 min | Daily | Point |
Air Temperature (Grand Forks) | 2015-4-1 | 2018-9-11 | Daily | Daily | Point |
Precipitation (Oakville) | 2018-3-18 | 2018-9-11 | 15 min | Daily | Point |
Precipitation (Grand Forks) | 2015-4-1 | 2018-9-11 | Daily | Daily | Point |
GDD (Grand Forks) | 2015-4-1 | 2018-9-11 | Daily | Daily | Point |
Soil Water Content | 2018-3-18 | 2018-9-11 | 15 min | Daily | Point |
Soil Temperature 2" | 2018-3-18 | 2018-9-11 | 15 min | Daily | Point |
Soil Temperature 4" | 2018-3-18 | 2018-9-11 | 15 min | Daily | Point |
Soil Temperature 8" | 2018-3-18 | 2018-9-11 | 15 min | Daily | Point |
Soil Temperature 20" | 2018-3-18 | 2018-9-11 | 15 min | Daily | Point |
Soil Temperature 40" | 2018-3-18 | 2018-9-11 | 15 min | Daily | Point |
Fig. 3 Sample photography and grass growing dynamic at Oakville station Note: Red polygon represents the ROI (Region of Interesting) used to calculate greenness index.
Fig. 6 Comparison of Gcc with air temperature (2015-2018) (a), and GDD (b); and Gcc index compared with MODIS VI (c), VIIRS VI (d) and Sentinel2 VI (e).
Fig. 7 Projection of the nine environmental variables on the 1st and 2nd principal component axes. Black points refer to the observed records for the variables.
Coefficients | Estimate | Std. error | t value | Pr (>|t|) |
---|---|---|---|---|
Intercept | 3.132e-01 | 7.881e-03 | 39.798 | < 2e-16 *** |
Solar | 2.527e-04 | 5.277e-05 | 4.788 | 4.28e-06 *** |
SoilTemp4 | -8.647e-03 | 3.546e-03 | -2.439 | 0.0160 * |
SoilTemp8 | 8.264e-03 | 4.011e-03 | 2.061 | 0.0412 * |
SoilTemp20 | 3.756e-02 | 2.804e-03 | 13.396 | < 2e-16 *** |
SoilTemp40 | -3.677e-02 | 2.066e-03 | -17.801 | < 2e-16 *** |
Soil Water | 6.107e-02 | 1.167e-02 | 5.232 | 6.10e-07 *** |
Coefficients | Estimate | Std. error | t value | Pr (>|t|) |
---|---|---|---|---|
Intercept | 3.132e-01 | 7.881e-03 | 39.798 | < 2e-16 *** |
Solar | 2.527e-04 | 5.277e-05 | 4.788 | 4.28e-06 *** |
SoilTemp4 | -8.647e-03 | 3.546e-03 | -2.439 | 0.0160 * |
SoilTemp8 | 8.264e-03 | 4.011e-03 | 2.061 | 0.0412 * |
SoilTemp20 | 3.756e-02 | 2.804e-03 | 13.396 | < 2e-16 *** |
SoilTemp40 | -3.677e-02 | 2.066e-03 | -17.801 | < 2e-16 *** |
Soil Water | 6.107e-02 | 1.167e-02 | 5.232 | 6.10e-07 *** |
Year | SOS | EOS | LOS | POP | MGS | RSP | RAU | PEAK | MSP | MAU | |||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2015 | 151 | 284 | 133 | 172 | 0.402 | 0.003 | -0.0014 | 0.426 | 0.398 | 0.350 | |||
2016 | 141 | 283 | 142 | 167 | 0.412 | 0.0036 | -0.0017 | 0.438 | 0.3981 | 0.352 | |||
2017 | 132 | 257 | 125 | 172 | 0.411 | 0.0032 | -0.0013 | 0.427 | 0.376 | 0.374 | |||
2018 | 127 | 210 | 83 | 167 | 0.438 | 0.005 | -0.0013 | 0.451 | 0.386 | 0.420 | |||
Mean | 137 | 258 | 120 | 169 | 0.416 | 0.0037 | -0.0014 | 0.4355 | 0.389 | 0.374 |
Year | SOS | EOS | LOS | POP | MGS | RSP | RAU | PEAK | MSP | MAU | |||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2015 | 151 | 284 | 133 | 172 | 0.402 | 0.003 | -0.0014 | 0.426 | 0.398 | 0.350 | |||
2016 | 141 | 283 | 142 | 167 | 0.412 | 0.0036 | -0.0017 | 0.438 | 0.3981 | 0.352 | |||
2017 | 132 | 257 | 125 | 172 | 0.411 | 0.0032 | -0.0013 | 0.427 | 0.376 | 0.374 | |||
2018 | 127 | 210 | 83 | 167 | 0.438 | 0.005 | -0.0013 | 0.451 | 0.386 | 0.420 | |||
Mean | 137 | 258 | 120 | 169 | 0.416 | 0.0037 | -0.0014 | 0.4355 | 0.389 | 0.374 |
Year | VI | SOS | EOS | LOS | POP | MGS | RSP | RAU | Peak | MSP | MAU |
---|---|---|---|---|---|---|---|---|---|---|---|
2015 | Gcc | 149 | 271 | 122 | 191 | 0.420 | 0.003 | -0.001 | 0.434 | 0.388 | 0.378 |
MODNDVI | 143 | 243 | 100 | 194 | 0.661 | 0.007 | -0.004 | 0.732 | 0.499 | 0.584 | |
MODEVI | 151 | 261 | 110 | 190 | 0.444 | 0.010 | -0.004 | 0.504 | 0.324 | 0.332 | |
VIIRSNDVI | 137 | 250 | 113 | 194 | 0.690 | 0.006 | -0.004 | 0.761 | 0.541 | 0.599 | |
VIIRSEVI | 142 | 278 | 136 | 192 | 0.445 | 0.009 | -0.005 | 0.495 | 0.323 | 0.317 | |
Dev | 0.042 | 0.05 | 0.07 | 0.005 | 0.250 | 0.625 | 0.760 | 0.30 | 0.08 | 0.170 | |
2016 | Gcc | 152 | 340 | 188 | 200 | 0.406 | 0.003 | -0.001 | 0.447 | 0.393 | 0.348 |
MODNDVI | 108 | 338 | 230 | 221 | 0.562 | 0.005 | -0.007 | 0.721 | 0.372 | 0.209 | |
MODEVI | 133 | 312 | 179 | 221 | 0.382 | 0.003 | -0.004 | 0.462 | 0.274 | 0.22 | |
VIIRSNDVI | 102 | 329 | 227 | 214 | 0.628 | 0.005 | -0.007 | 0.79 | 0.419 | 0.284 | |
VIIRSEVI | 146 | 265 | 119 | 205 | 0.451 | 0.004 | -0.004 | 0.51 | 0.350 | 0.354 | |
Dev | 0.25 | 0.09 | 0.005 | 0.07 | 0.20 | 0.29 | 0.82 | 0.28 | 0.11 | 0.30 | |
2017 | Gcc | 176 | 295 | 119 | 213 | 0.423 | 0.003 | -0.001 | 0.438 | 0.393 | 0.391 |
MODNDVI | 128 | 296 | 168 | 214 | 0.620 | 0.005 | -0.007 | 0.745 | 0.444 | 0.368 | |
MODEVI | 142 | 279 | 137 | 210 | 0.408 | 0.004 | -0.005 | 0.481 | 0.301 | 0.27 | |
VIIRSNDVI | 120 | 278 | 158 | 199 | 0.670 | 0.006 | -0.005 | 0.777 | 0.456 | 0.525 | |
VIIRSEVI | 145 | 256 | 111 | 200 | 0.516 | 0.006 | -0.005 | 0.591 | 0.379 | 0.405 | |
Dev | 0.32 | 0.06 | 0.17 | 0.04 | 0.24 | 0.43 | 0.82 | 0.32 | 0.05 | 0.003 |
Year | VI | SOS | EOS | LOS | POP | MGS | RSP | RAU | Peak | MSP | MAU |
---|---|---|---|---|---|---|---|---|---|---|---|
2015 | Gcc | 149 | 271 | 122 | 191 | 0.420 | 0.003 | -0.001 | 0.434 | 0.388 | 0.378 |
MODNDVI | 143 | 243 | 100 | 194 | 0.661 | 0.007 | -0.004 | 0.732 | 0.499 | 0.584 | |
MODEVI | 151 | 261 | 110 | 190 | 0.444 | 0.010 | -0.004 | 0.504 | 0.324 | 0.332 | |
VIIRSNDVI | 137 | 250 | 113 | 194 | 0.690 | 0.006 | -0.004 | 0.761 | 0.541 | 0.599 | |
VIIRSEVI | 142 | 278 | 136 | 192 | 0.445 | 0.009 | -0.005 | 0.495 | 0.323 | 0.317 | |
Dev | 0.042 | 0.05 | 0.07 | 0.005 | 0.250 | 0.625 | 0.760 | 0.30 | 0.08 | 0.170 | |
2016 | Gcc | 152 | 340 | 188 | 200 | 0.406 | 0.003 | -0.001 | 0.447 | 0.393 | 0.348 |
MODNDVI | 108 | 338 | 230 | 221 | 0.562 | 0.005 | -0.007 | 0.721 | 0.372 | 0.209 | |
MODEVI | 133 | 312 | 179 | 221 | 0.382 | 0.003 | -0.004 | 0.462 | 0.274 | 0.22 | |
VIIRSNDVI | 102 | 329 | 227 | 214 | 0.628 | 0.005 | -0.007 | 0.79 | 0.419 | 0.284 | |
VIIRSEVI | 146 | 265 | 119 | 205 | 0.451 | 0.004 | -0.004 | 0.51 | 0.350 | 0.354 | |
Dev | 0.25 | 0.09 | 0.005 | 0.07 | 0.20 | 0.29 | 0.82 | 0.28 | 0.11 | 0.30 | |
2017 | Gcc | 176 | 295 | 119 | 213 | 0.423 | 0.003 | -0.001 | 0.438 | 0.393 | 0.391 |
MODNDVI | 128 | 296 | 168 | 214 | 0.620 | 0.005 | -0.007 | 0.745 | 0.444 | 0.368 | |
MODEVI | 142 | 279 | 137 | 210 | 0.408 | 0.004 | -0.005 | 0.481 | 0.301 | 0.27 | |
VIIRSNDVI | 120 | 278 | 158 | 199 | 0.670 | 0.006 | -0.005 | 0.777 | 0.456 | 0.525 | |
VIIRSEVI | 145 | 256 | 111 | 200 | 0.516 | 0.006 | -0.005 | 0.591 | 0.379 | 0.405 | |
Dev | 0.32 | 0.06 | 0.17 | 0.04 | 0.24 | 0.43 | 0.82 | 0.32 | 0.05 | 0.003 |
[1] | Atzberger C, Klisch A, Mattiuzzi M , et al. 2013. Phenological metrics derived over the European continent from NDVI3g data and MODIS time series. Remote Sensing, 6(1):257-284. |
[2] | Beck P S, Atzberger C, Høgda K A , et al. 2006. Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI. Remote Sensing of Environment, 100(3):321-334. |
[3] | Brown T B, Hultine K R, Steltzer H , et al. 2016. Using phenocams to monitor our changing earth: Toward a global phenocam network. Frontiers in Ecology and the Environment, 14(2):84-93. |
[4] | Browning D M, Karl J W, Morin D , et al. 2017. Phenocams bridge the gap between field and satellite observations in an arid grassland ecosystem. Remote Sensing, 9(10):1071. |
[5] | Buermann W, Bikash P R, Jung M , et al. 2013. Earlier springs decrease peak summer productivity in north American boreal forests. Environmental Research Letter, 8(2):024-027. |
[6] | Cui T, Martz L, Guo X . 2017. Grassland phenology response to drought in the Canadian prairies. Remote Sensing, 9(12):1258. |
[7] | Fu Y, He H S, Zhao J , et al. 2018. Climate and spring phenology effects on autumn phenology in the Greater Khingan Mountains, northeastern China. Remote Sensing, 10(3):449. |
[8] | Ganjurjav H, Gao Q, Schwartz M W , et al. 2016. Complex responses of spring vegetation growth to climate in a moisture-limited alpine meadow. Scientific Reports, 6:23356. |
[9] | Gardner W, Ehlig C . 1963. The influence of soil water on transpiration by plants. Journal of Geophysical Research, 68(20):5719-5724. |
[10] | Garonna I, De Jong R, De Wit A J , et al. 2014. Strong contribution of autumn phenology to changes in satellite-derived growing season length estimates across Europe (1982-2011). Global Change Biology, 20(11):3457-3470. |
[11] | Garonna I, De Jong R, Schaepman M E . 2016. Variability and evolution of global land surface phenology over the past three decades (1982-2012). Global Change Biology, 22(4):1456-1468. |
[12] | Grömping U . 2006. Relative importance for linear regression in R: The package relaimpo. Journal of Statistical Software, 17(1):1-27. |
[13] | Huang K, Xia J, Wang Y , et al. 2018. Enhanced peak growth of global vegetation and its key mechanisms. Nature Ecology & Evolution, 2(12):1897. |
[14] | Hufkens K, Basler D, Milliman T , et al. 2018. An integrated phenology modelling framework in R. Methods in Ecology and Evolution, 9(5):1276-1285. |
[15] | Hufkens K, Keenan T F, Flanagan L B , et al. 2016. Productivity of north american grasslands is increased under future climate scenarios despite rising aridity. Nature Climate Change, 6(7):710. |
[16] | Inoue T, Nagai S, Kobayashi H , et al. 2015. Utilization of ground-based digital photography for the evaluation of seasonal changes in the aboveground green biomass and foliage phenology in a grassland ecosystem. Ecological Informatics, 25:1-9. |
[17] | Jolliffe I. . 2017. Principal component analysis. In: International Encyclopedia of Statistical Science, Springer:1094-1096. |
[18] | Karkauskaite P, Tagesson T, Fensholt R . 2017. Evaluation of the plant phenology index (ppi), NDVI and EVI for start-of-season trend analysis of the northern hemisphere boreal zone. Remote Sensing, 9(5):485. |
[19] | Klosterman S, Hufkens K, Gray J , et al. 2014. Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using phenocam imagery. Biogeosciences, 11(2):4305-4320. |
[20] | Legendre P, Legendre L . 1998. Numerical Ecology. Second English edition. Elsevier, Amsterdam . |
[21] | Liu Q, Fu Y H, Zeng Z , et al. 2016. Temperature, precipitation, and insolation effects on autumn vegetation phenology in temperate China. Global Change Biology, 22(2):644-655. |
[22] | Liu Y, Hill M J, Zhang X , et al. 2017. Using data from landsat, modis, viirs and phenocams to monitor the phenology of California oak/grass savanna and open grassland across spatial scales. Agricultural and Forest Meteorology, 237:311-325. |
[23] | Marshall M, Okuto E, Kang Y , et al. 2016. Global assessment of vegetation index and phenology lab (vip) and global inventory modeling and mapping studies (gimms) version 3 products. Biogeosciences, 13(3):625-639. |
[24] | Myneni R B, Keeling C, Tucker C J , et al. 1997. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature, 386(6626):698. |
[25] | Nemani R R, Keeling C D, Hashimoto H , et al. 2003. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 300(5625):1560-1563. |
[26] | Petach A R, Toomey M, Aubrecht D M , et al. 2014. Monitoring vegetation phenology using an infrared-enabled security camera. Agricultural and forest meteorology, 195:143-151. |
[27] | Piao S, Cui M, Chen A , et al. 2011. Altitude and temperature dependence of change in the spring vegetation green-up date from 1982 to 2006 in the Qinghai-Xizang Plateau. Agricultural and Forest Meteorology, 151(12):1599-1608. |
[28] | Piao S, Tan J, Chen A , et al. 2015. Leaf onset in the northern hemisphere triggered by daytime temperature. Nature Communications, 6:6911. |
[29] | Richardson A D, Black T A, Ciais P , et al. 2010. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 365(1555):3227-3246. |
[30] | Richardson A D, Hufkens K, Milliman T , et al. 2018a. Tracking vegetation phenology across diverse north American biomes using phenocam imagery. Scientific Data, 5:180028. |
[31] | Richardson A D, Hufkens K, Milliman T , et al. 2018b. Intercomparison of phenological transition dates derived from the phenocam dataset v1. 0 and MODIS satellite remote sensing. Scientific Reports, 8(1):5679. |
[32] | Richardson A D, Keenan T F, Migliavacca M , et al. 2013. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agricultural and Forest Meteorology, 169:156-173. |
[33] | Sonnentag O, Hufkens K, Teshera-Sterne C , et al. 2012. Digital repeat photography for phenological research in forest ecosystems. Agricultural and Forest Meteorology, 152:159-177. |
[34] | St Peter J, Hogland J, Hebblewhite M , et al. 2018. Linking phenological indices from digital cameras in Idaho and Montana to MODIS NDVI. Remote Sensing, 10(10):1612. |
[35] | Toomey M, Friedl M A, Frolking S , et al. 2015. Greenness indices from digital cameras predict the timing and seasonal dynamics of canopy-scale photosynthesis. Ecological Applications, 25(1):99-115. |
[36] | Vicente-Serrano S M, Gouveia C, Camarero J J , et al. 2013. Response of vegetation to drought time-scales across global land biomes. Proceedings of the National Academy of Sciences , 110(1):52-57. |
[37] | Wu C, Wang X, Wang H , et al. 2018. Contrasting responses of autumn-leaf senescence to daytime and night-time warming. Nature Climate Change, 8(12):1092. |
[38] | Xu M . 2013. A research on summer vegetation characteristics & short-time responses to experimental warming of alpine meadow in the Qinghai-tibetan plateau. Acta Ecologica Sinica, 33:2071-2083.(in Chinese) |
[39] | Zhang X, Jayavelu S, Liu L , et al. 2018. Evaluation of land surface phenology from viirs data using time series of phenocam imagery. Agricultural and Forest Meteorology, 256:137-149. |
[40] | Zhao J, Zhang H, Zhang Z , et al. 2015. Spatial and temporal changes in vegetation phenology at middle and high latitudes of the northern hemi- sphere over the past three decades. Remote Sensing, 7(8):10973-10995. |
[41] | Zhu L, Meng J . 2015. Determining the relative importance of climatic drivers on spring phenology in grassland ecosystems of semi-arid areas. International Journal of Biometeorology, 59(2):237-248. |
[42] | Zhu W, Tian H, Xu X , et al. 2012. Extension of the growing season due to delayed autumn over mid and high latitudes in North America during 1982-2006. Global Ecology and Biogeography, 21(2):260-271. |
No related articles found! |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||