4th, November
Teo, H. C., Zeng, Y., Sarira, T. V., Fung, T. K., Zheng, Q., Song, X. P., ... & Koh, L. P. (2021). Global urban reforestation can be an important natural climate solution. Environmental Research Letters, 16(3), 034059.
Globally, cities with the highest average climate mitigation potential from urban reforestation are mainly located in the Northern Hemisphere and are highly concentrated in Asia. Approximately 18% of the urban area globally could be reforested, which could offset 1% of carbon emissions from cities. Wonder how much of an impact this will have.
3rd, November
Brilli, L., Carotenuto, F., Chiesi, M., Fiorillo, E., Genesio, L., Magno, R., ... & Gioli, B. (2022). An integrated approach to estimate how much urban afforestation can contribute to move towards carbon neutrality. Science of The Total Environment, 842, 156843.
The study simulated various afforestation scenarios but concluded that planting trees in cities had no significant impact on achieving carbon neutrality. Afforestation offsets urban carbon emissions in a range of 8.1% to 11%.
2nd, November
Grêt-Regamey, A., & Galleguillos-Torres, M. (2022). Global urban homogenization and the loss of emotions. Scientific Reports, 12(1), 22515.
The peri-urban area, between the city and the rural, causes people unpleasant and deactivating feelings.
1st, November
Riva, F., Martin, C. J., Millard, K., & Fahrig, L. (2022). Loss of the world's smallest forests. Global Change Biology, 28(24), 7164-7166.
We sometimes overlook small forests. This study emphasizes that the trend of deforestation appears higher in small forests than in large forests.
4th, October
Rößger, N., Sachs, T., Wille, C., Boike, J., & Kutzbach, L. (2022). Seasonal increase of methane emissions linked to warming in Siberian tundra. Nature Climate Change, 1-6.
This study is the first direct observation of increasing CH4 emissions in early summer in the tundra region. Summer season methane emissions have increased by approximately 2% yr-1 since 2004.
3rd, October
Yu, W., Zhou, W., Dawa, Z., Wang, J., Qian, Y., & Wang, W. (2021). Quantifying Urban Vegetation Dynamics from a Process Perspective Using Temporally Dense Landsat Imagery. Remote Sensing, 13(16), 3217.
Reported the importance of temporal intervals in identifying vegetation trends in urban areas. When using a linear regression model to estimate the long-term trends (e.g., decades), it could miss the hidden greening or browning trends.
2nd, October
Malenovský, Z., Regaieg, O., Yin, T., Lauret, N., Guilleux, J., Chavanon, E., ... & Gastellu-Etchegorry, J. P. (2021). Discrete anisotropic radiative transfer modelling of solar-induced chlorophyll fluorescence: Structural impacts in geometrically explicit vegetation canopies. Remote sensing of environment, 263, 112564.
When woody materials were shown in the DART simulation, it affected the SIF signal. Approximately, 15% and 8% of SIF were reduced in the dense and sparse canopy simulation scene, respectively. This indicates the structural changes affected the SIF.
1st, October
Song, J., Wang, J., Xia, X., Lin, R., Wang, Y., Zhou, M., & Fu, D. (2021). Characterization of urban heat islands using city lights: Insights from MODIS and VIIRS DNB Observations. Remote Sensing, 13(16), 3180.
Their attempt was to understand the urban heat island using the night light data set. They compared the degree of UHI in Beijing, the capital of China, and Pyongyang, the capital of North Korea. It would be more interesting to compare Beijing UHI with other megacities.
4th, September
Wang, R., Wan, B., Guo, Q., Hu, M., & Zhou, S. (2017). Mapping regional urban extent using NPP-VIIRS DNB and MODIS NDVI data. Remote Sensing, 9(8), 862.
Nighttime light and MODIS NDVI imagery to classify urban areas and expected these combination would be useful in identifying the urban extension. It would have been more interesting if this paper used the developed method and had reported where or how much of urban area was expanded in China and explained what it implies.
3rd, September
Esperon-Rodriguez, M., Tjoelker, M. G., Lenoir, J., Baumgartner, J. B., Beaumont, L. J., Nipperess, D. A., . . . Gallagher, R. V. (2022). Climate change increases global risk to urban forests. Nature Climate Change. doi:10.1038/s41558-022-01465-8
The risk of urban trees and shrubs to climate change increased towards the equator. Urban vegetation was more at risk from temperature changes than precipitation changes.
2nd, September
Huang, K. (2022). Urban forests facing climate risks. Nature Climate Change. doi:10.1038/s41558-022-01481-8
Urban forests are the most essential area in urban since it mitigates the urban heat island. However, the urban tree and shrub species are facing risks due to climate change. The author emphasizes the importance of electing the tree species that will survive the future climate conditions and should monitor and manage the trees that are already planted in the urban area.
1st, September
Li, Z., Zhang, Q., Li, J., Yang, X., Wu, Y., Zhang, Z., ... & Zhang, Y. (2020). Solar-induced chlorophyll fluorescence and its link to canopy photosynthesis in maize from continuous ground measurements. Remote Sensing of Environment, 236, 111420.
Maize SIF was also affected by the structure changes. The structure influenced the SIF-GPP and SIF yield-LUE relationships.
4th, August
Brede, B., Terryn, L., Barbier, N., Bartholomeus, H. M., Bartolo, R., Calders, K., ... & Herold, M. (2022). Non-destructive estimation of individual tree biomass: Allometric models, terrestrial and UAV laser scanning. Remote Sensing of Environment, 280, 113180.
When comparing tree height and crown diameter between TLS and UAV data, tree height had a larger R-square than the crown diameter. When estimating the volume of the tree, DBH usually explained most of it. Finally, strategy IIa uses the locally collected TLS data for nondestructive calibration and inventory-based application showed the lowest RSME among the five strategies.
3rd, August
Qin, H., Zhou, W., Qian, Y., Zhang, H., & Yao, Y. (2022). Estimating aboveground carbon stocks of urban trees by synergizing ICESat-2 LiDAR with GF-2 data. Urban Forestry & Urban Greening, 127728.
This paper integrated LiDAR and satellite imagery to estimate the carbon stocks in the urban area (Beijing). They suggest the new method would reduce the underestimation of aboveground carbon stocks in urban trees. Since the satellite data contains the tree height information, to estimate the aboveground carbon stock the allometric equation had to be used although they have used the LiDAR data. However, understanding figure 7 was quite difficult.
2nd, August
Chen, B., Wu, S., Song, Y., Webster, C., Xu, B., & Gong, P. (2022). Contrasting inequality in human exposure to greenspace between cities of Global North and Global South. Nature communications, 13(1), 1-9.
There was a contrasting green exposure difference between southern and northern cities (compared to 1,028 global cities). Southern cities had one-third lower exposure levels than northern cities. This paper identified the cause of low exposure in southern cities.
1st, August
Asner, G. P., Nepstad, D., Cardinot, G., & Ray, D. (2004). Drought stress and carbon uptake in an Amazon forest measured with spaceborne imaging spectroscopy. Proceedings of the National Academy of Sciences, 101(16), 6039-6044.
It is difficult to identify whether trees are undergoing drought stress in the Amazon forest when using the traditional vegetation index. The hyperspectral canopy water metrics (SWAM), which have been presented in this study, showed the most sensitive response to drought stress.
4th, July
Hickey, L. J., Nave, L. E., Nadelhoffer, K. J., Clay, C., Marini, A. I., & Gough, C. M. (2022). Mechanistically-grounded pathways connect remotely sensed canopy structure to soil respiration. Science of The Total Environment, 158267.
This paper pointed out that taller, denser, and more complex canopies have higher interior humidity levels. This could reduce the loss of soil evaporation, but curious if this could also indicate that leaves in the taller trees or higher levels have low leaf water contents. However, it is interesting that LiDAR-derived canopy structure could be related to soil respiration.
3rd, July
Chang, C. Y., Wen, J., Han, J., Kira, O., LeVonne, J., Melkonian, J., ... & Sun, Y. (2021). Unpacking the drivers of diurnal dynamics of sun-induced chlorophyll fluorescence (SIF): Canopy structure, plant physiology, instrument configuration and retrieval methods. Remote Sensing of Environment, 265, 112672.
Although crops were not subjected to any environmental stress, at 13hh SIF was reduced due to changes in the canopy structure. Furthermore, Fs yield in the top-canopy (low LAI) showed a U-shaped diurnal pattern, while the under-canopy layer (2 < LAI) showed an inverted U-shape diurnal pattern.
2nd, July
Xie, S., Liu, L., Zhang, X., & Yang, J. (2022). Mapping the annual dynamics of land cover in Beijing from 2001 to 2020 using Landsat dense time series stack. ISPRS Journal of Photogrammetry and Remote Sensing, 185, 201-218.
Beijing and cover change were detected from 2000 to 2020 using the CCDC algorithm as I have tried in Seoul. However, the land cover classification was done with Markov random field model, not the random forest. This method seems to increase the accuracy of the land cover changes (around 93%). But still curious how they have handled the line error of the Landsat satellite.
1st, July
Herrero-Huerta, M., Lindenbergh, R., & Gard, W. (2018). Leaf movements of indoor plants monitored by terrestrial LiDAR. Frontiers in plant science, 9, 189.
The leaf angle diurnal cycle of the pot plants' individual leaves was observed through LiDAR. The maximum change in vertical angle occurred in the afternoon and was 69.6°. The movement of the leaves is to lower the temperature of the leaves.
4th, June
Jonard, F., De Canniere, S., Brüggemann, N., Gentine, P., Gianotti, D. S., Lobet, G., ... & Vereecken, H. (2020). Value of sun-induced chlorophyll fluorescence for quantifying hydrological states and fluxes: Current status and challenges. Agricultural and Forest Meteorology, 291, 108088.
This paper identified the Fs variation could be explained by the soil water deficit. When mild drought stress occurred the Fs could instantly increase but when drought stress becomes severe, the Fs eventually decrease.
3rd, June
Biskup, B., Scharr, H., Schurr, U., & Rascher, U. W. E. (2007). A stereo imaging system for measuring structural parameters of plant canopies. Plant, cell & environment, 30(10), 1299-1308.
The soybean leaf inclination angle was increased by approximately 10 degrees compared to the control soybean plants during the 7 days of the drought stress experiment.
2nd, June
Cendrero-Mateo, M. P., Carmo-Silva, A. E., Porcar-Castell, A., Hamerlynck, E. P., Papuga, S. A., & Moran, M. S. (2015). Dynamic response of plant chlorophyll fluorescence to light, water and nutrient availability. Functional Plant Biology, 42(8), 746-757.
Trees subjected to drought stress Fs could be increased or decreased by the PAR values. When PAR values were less than 300 μmol m–2 s–1 Fs increased and when PAR values were between 300 and 500 μmol m–2 s–1 Fs decreased .
1st, June
Oh, J. W., Ngarambe, J., Duhirwe, P. N., Yun, G. Y., & Santamouris, M. (2020). Using deep-learning to forecast the magnitude and characteristics of urban heat island in Seoul Korea. Scientific reports, 10(1), 1-13.
The approach to analyzing urban heat islands with deep-learning was interesting. A new index called UHI-hours was created in this study. However, it is surprising that the nine-year patterns of the UHI-hours in summer and winter were similar. Also curious why the figure which shows the summer albedo and air temperature/UHI-hours is missing.
4th, May
Akbari, H., Matthews, H. D., & Seto, D. (2012). The long-term effect of increasing the albedo of urban areas. Environmental Research Letters, 7(2), 024004.
The effect of the increase in albedo was simulated in this study. The increased albedo did not significantly affect the CO2 emission scenario, but the surface temperature decreased. This study concluded that increasing the albedo of 1m2 of the surface in high-temperature regions (e.g., urban) by 0.01 decreases the long-term global temperature by approximately 3fK.
3rd, May
Shan, N., Ju, W., Migliavacca, M., Martini, D., Guanter, L., Chen, J., ... & Zhang, Y. (2019). Modeling canopy conductance and transpiration from solar-induced chlorophyll fluorescence. Agricultural and Forest Meteorology, 268, 189-201.
The author estimated canopy transpiration from the observed SIF signal in three different biomes. The stomatal conductance was extracted by integrating the SIF signal with the Ball-Berry model. Although this new method provided a new pathway to estimate transpiration, it still had limitations under drought stress conditions.
2nd, May
Lin, J., Shen, Q., Wu, J., Zhao, W., & Liu, L. (2022). Assessing the Potential of Downscaled Far Red Solar-Induced Chlorophyll Fluorescence from the Canopy to Leaf Level for Drought Monitoring in Winter Wheat. Remote Sensing, 14(6), 1357.
This paper identified that SIF was more related to plant physiology than the canopy structure under drought conditions. Also, the total emitted SIF was more related to the soil moisture than the vegetation index in the shorter timescales.
1st, May
Damm, A., Cogliati, S., Colombo, R., Fritsche, L., Genangeli, A., Genesio, L., ... & Miglietta, F. (2022). Response times of remote sensing measured sun-induced chlorophyll fluorescence, surface temperature and vegetation indices to evolving soil water limitation in a crop canopy. Remote Sensing of Environment, 273, 112957.
The stem diameter and height of the water-limited plants were followed by the well-watered plants. Only the leaf chlorophyll content showed a decreasing trend over time. This experimental study showed that SIF detected the water-limited condition fast compared to other remote sensing parameters such as vegetation indexes and surface temperature. Also, SIF showed a double reaction to water-limited conditions: increased in the short-term and decreased afterward.
4th, April
Vilfan, N., Van der Tol, C., Muller, O., Rascher, U., & Verhoef, W. (2016). Fluspect-B: A model for leaf fluorescence, reflectance and transmittance spectra. Remote Sensing of Environment, 186, 596-615.
The FLUSPECT model is based on the PROSPECT model and calculates the forward and backward chlorophyll a fluorescence from 640 nm to 850 nm of photosystem I and photosystem II. This model is installed in the DART model, which would be helpful to simulate canopy-level SiF.
3rd, April
Zhang, Y., Guanter, L., Berry, J. A., van der Tol, C., Yang, X., Tang, J., & Zhang, F. (2016). Model-based analysis of the relationship between sun-induced chlorophyll fluorescence and gross primary production for remote sensing applications. Remote Sensing of Environment, 187, 145-155.
Leaf angle is another important structural factor that influences SiF signal in addition to leaf area index. However, leaf angle has less influence on modeled canopy photosynthesis. This was an interesting founding since SiF and photosynthesis are known to have a positive relationship.
2nd, April
Xu, S., Liu, Z., Zhao, L., Zhao, H., & Ren, S. (2018). Diurnal response of sun-induced fluorescence and PRI to water stress in maize using a near-surface remote sensing platform. Remote Sensing, 10(10), 1510.
This paper monitored a decreasing trend in SIF in water-stressed vegetation compared to the control vegetation as a result of leaf inclination angel changes.
1st, April
Du, S., Liu, L., Liu, X., & Hu, J. (2017). Response of canopy solar-induced chlorophyll fluorescence to the absorbed photosynthetically active radiation absorbed by chlorophyll. Remote Sensing, 9(9), 911.
The fraction of all SIF photons, emitted from all leaves that escape from the canopy showed the largest value for a planophile canopy structure and the lowest value for the erectophile canopy structure.
4th, March
Xu, S., Atherton, J., Riikonen, A., Zhang, C., Oivukkamäki, J., MacArthur, A., ... & Porcar-Castell, A. (2021). Structural and photosynthetic dynamics mediate the response of SIF to water stress in a potato crop. Remote Sensing of Environment, 263, 112555.
Leaf angle distribution is expected to vary under drought stress conditions and decrease canopy-level SIF at the diurnal scale.
3rd, March
Åkerblom, M., Raumonen, P., Casella, E., Disney, M. I., Danson, F. M., Gaulton, R., ... & Kaasalainen, M. (2018). Non-intersecting leaf insertion algorithm for tree structure models. Interface Focus, 8(2), 20170045.
The QSM based model generates the leaves based on the setting. However, the generated leaves have the same shape with different sizes. Hope the QSM-FaNNI model could generate the leaves from the measured TSL data to be realistic.
2nd, March
Chen, X., Mo, X., Hu, S., & Liu, S. (2019). Relationship between fluorescence yield and photochemical yield under water stress and intermediate light conditions. Journal of Experimental Botany, 70(1), 301-313.
The Fs also increased in water-stressed trees in this study. The increase of the Fs could appear when there is no photorespiration or when PAR was < 300 umol m-2 s-1. However, the trade-off between, and changes in the photochemistry, heat dissipation, and chlorophyll fluorescence pathways are still not clear.
1st, March
Wang, S., Huang, C., Zhang, L., Lin, Y., Cen, Y., & Wu, T. (2016). Monitoring and assessing the 2012 drought in the great plains: Analyzing satellite-retrieved solar-induced chlorophyll fluorescence, drought indices, and gross primary production. Remote Sensing, 8(2), 61.
Used GOME-2 SIF data to identify if SIF could capture the 2012 drought. They have found that SIF was more sensitive in detecting vegetation drought stress than GPP and other vegetation and water index. If SIF is more sensitive to drought wonder how SIF and GPP have a good correlation.
4th, February
Liu, W., Atherton, J. M., Mottus, M., MacArthur, A., Teemu, H., Maseyk, K., ... & Porcar Castell, J. A. (2017). Upscaling of solar induced chlorophyll fluorescence from leaf to canopy using the DART model and a realistic 3D forest scene. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.
If we use the Blender software and create a 3D tree from the LiDAR data, it would be a great opportunity to extract canopy level SiF from the DART model simulation. However, since we did not measure the tree wood (bark, stem) reflectance data there would be some assumptions.
3rd, February
Berner, L. T., & Goetz, S. J. (2022). Satellite observations document trends consistent with a boreal forest biome shift. Global change biology.
In the Arctic region, a browning trend occurred at sites which as high summer air temperatures and VPD. Conversely, a greening trend occurred at sites that had low summer air temperatures and in low or high elevation regions. It was interesting that when tree cover increases the browning trend increased. Also, this paper used the maximum NDVI value instead of the annual integrated growing season NDVI since Landsat had low temporal resolution.
2nd, February
Yin, G., Verger, A., Descals, A., Filella, I., & Peñuelas, J. (2022). A Broadband Green-Red Vegetation Index for Monitoring Gross Primary Production Phenology. Journal of Remote Sensing, 2022.
As the number of satellites that can use the 531 nm band is limited, this study suggested an alternative index, the Green-Red vegetation index. From the result, the GRVI showed a high correlation with the gross primary production and phenology (R2=0.97).
1st, February
Kim, D., & Jin, J. (2018). Does happiness data say urban parks are worth it?. Landscape and Urban Planning, 178, 1-11.
This paper reported that the green space per person increased from 10 m2 in 2015 to 11 m2 in 2015. However, the result was different from Seoul Solution 2015 report, which was 14.26 m2 in 2014.
4th, January
Jimenez, R. B., Lane, K. J., Hutyra, L. R., & Fabian, M. P. (2022). Spatial resolution of Normalized Difference Vegetation Index and greenness exposure misclassification in an urban cohort. Journal of Exposure Science & Environmental Epidemiology, 32(2), 213-222.
By comparing NDVI using various spatial resolutions from 1 m to 250 m, this paper recommended the use of high spatial resolution data when estimating NDVI in urban regions since vegetated features might not be able to detect in coarse resolution data.
3rd, January
Pringle, M. J. (2013). Robust prediction of time-integrated NDVI. International Journal of Remote Sensing, 34(13), 4791-4811.
Unlike AVHRR and MODIS satellites, Landsat has difficulty in using time-integrated NDVI for two reasons. The first reason is due to the temporal resolution (16 days). Furthermore, when clouds or snow occurs more observed data will be missing. The second difficulty was due to the outliers.
2nd, January
Yan, J., Zhang, G., Ling, H., & Han, F. (2022). Comparison of time-integrated NDVI and annual maximum NDVI for assessing grassland dynamics. Ecological Indicators, 136, 108611.
Compared how integrated NDVI differ with maximum NDVI. The NDVI was based on the MODIS satellite. The time-integrated NDVI captured spatial and temporal heterogeneity of grassland and correlates well with the precipitation or temperature than the maximum NDVI.
1st, January
Smith, I. A., Winbourne, J. B., Tieskens, K. F., Jones, T. S., Bromley, F. L., Li, D., & Hutyra, L. R. (2021). A satellite-based model for estimating latent heat flux from urban vegetation. Frontiers in Ecology and Evolution, 573.
This paper developed a model to estimate the urban latent heat flux. The modeled latent heat flux agreed well with the intensity of EVI and the amount of the impervious surface area. It would be a useful tool for understanding the vegetation cooling effect and helpful to manage urban heat islands.