2020

4th,  December 

Li, F., Zheng, W., Wang, Y., Liang, J., Xie, S., Guo, S., ... & Yu, C. (2019). Urban green space fragmentation and urbanization: a spatiotemporal perspective. Forests, 10(4), 333. 

• The reduction in green space in Beijing was caused by fragmentation. The greenbelt in Beijing decreased by 24% during the three decades. 

3rd, December 

Albarakat, R., & Lakshmi, V. (2019). Comparison of normalized difference vegetation index derived from Landsat, MODIS, and AVHRR for the Mesopotamian marshes between 2002 and 2018. Remote sensing, 11(10), 1245. 

• Calculated NDVI showed different patterns and values in different satellites (i.e., MODIS, Landsat, and AVHRR). However, this paper mentioned there was only 0.02 bias between Landsat and  AVHRR. 

2nd, December 

Chen, C., Park, T., Wang, X., Piao, S., Xu, B., Chaturvedi, R. K., ... & Myneni, R. B. (2019). China and India lead in greening of the world through land-use management. Nature sustainability, 2(2), 122-129. 

• Found human land-use management is an essential factor in greening Earth. More than half of the greening was derived from cropland and forests. 

1st, December 

Sun, L., Chen, J., Li, Q., & Huang, D. (2020). Dramatic uneven urbanization of large cities throughout the world in recent decades. Nature communications, 11(1), 1-9. 

• Monitored how global large urban changed during recent decades and identified an uneven distribution of urbanization at different economic levels. Also, more than 10% of built-up areas showed significant greening from 2001 to 2018. Seoul was one of the cities. 

4th, November

Santos, T., Tenedório, J. A., & Gonçalves, J. A. (2016). Quantifying the city’s green area potential gain using remote sensing data. Sustainability, 8(12), 1247. 

• This study has estimated the potential green space at the rooftop by using satellite images and LiDAR sensor. The approach method was interesting. In Korea, building rooftops is mostly flat but in European countries, they had to consider the slope of the rooftop. This study, however, found an approximately 2.2 million m2 potential area in Lisbon, Portugal.

3rd, November

Liu, X., Huang, Y., Xu, X., Li, X., Li, X., Ciais, P., ... & Gong, P. (2020). High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. Nature Sustainability, 1-7. 

• Over the past 30 years global urban area expended 9,687km2 per year. However the rate of urban expansion is faster than population growth in urban. 

2nd, November

Li, X., Gong, P., & Liang, L. (2015). A 30-year (1984–2013) record of annual urban dynamics of Beijing City derived from Landsat data. Remote Sensing of Environment, 166, 78-90. 

• This paper focused on how urban areas extended during the 30 years. They have developed a method to classify land cover types using the Landsat spectral information from a base image and also the NDVI time series. The overall accuracy was above 90% and the urban growth rates increased dynamically during 2000-2013 in Beijing, China (99.5 km2/year). Similarly, bare soil land cover NDVI values varied with season, which is also caused by other nearby vegetation. 

1st, November 

Reinmann, A. B., Smith, I. A., Thompson, J. R., & Hutyra, L. R. (2020). Urbanization and fragmentation mediate temperate forest carbon cycle response to climate. Environmental Research Letters, 15(11), 114036. 

• Compared to natural forests and urban forests, urban forests have a higher carbon sink per unit area but vulnerable to climate warming. Urban forest fragmentation is inevitable as urbanization increase. This fragmentation increases the forest sensitivity to heat and leads negative impact of forest carbon sequestration. 

4th, October

Stefanov, W. L., Ramsey, M. S., & Christensen, P. R. (2001). Monitoring urban land cover change: An expert system approach to land cover classification of semiarid to arid urban centers. Remote sensing of Environment, 77(2), 173-185. 

• It is challenging to classify urban area since various land cover type happens in a small space (i.e., heterogeneous). I had a similar problem when classifying urban land cover types, and thought the fuzzy classification algorithm would help to increase the accuracy of the classification. According to this study, however, the fuzzy classification algorithm did not significantly improve the accuracy of the urban land cover type classification. Also, maximum likelihood classifier seems to be one of the most popular methods of classification in remote sensing. 

3rd, October

Schneider, A. (2012). Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach. Remote Sensing of Environment, 124, 689-704. 

• This paper used Landsat data to monitor 3 urban and peri-urban areas in China. By reading this paper, I was able to confirm that the forest average NDVI during the summer season exceeds 0.6. Also, I could briefly understand the history of remote sensing in urban areas. The boosted decision trees (DT) algorithm outperformed the maximum likelihood and the support vector machines classifier and was superior at handling missing data. 

2nd, October

Takane, Y., Kikegawa, Y., Hara, M., & Grimmond, C. S. B. (2019). Urban warming and future air-conditioning use in an Asian megacity: importance of positive feedback. npj Climate and Atmospheric Science, 2(1), 1-11. 

• Recently, I got interested in urban anthropogenic heat (e.g., air-condition). This paper focused on Asia megacity, Japan. Identified anthropogenic heat should not be neglected in future urban climate projection, especially in hot cities where significant AC use is common. 

1st, October

Miller, R. B., & Small, C. (2003). Cities from space: potential applications of remote sensing in urban environmental research and policy. Environmental Science & Policy, 6(2), 129-137. 

• Although it is now possible to measure a lot of variables about the urban environment from space by using the satellite, there are still some variables (e.g., urban energy consumption, soil, population density) need to be observed. 

4th, September 

Ki, D., & Lee, S. Analyzing the effects of Green View Index of neighborhood streets on walking time using Google Street View and deep learning. Landscape and Urban Planning, 205, 103920. 

• This paper showed the limitation of the NDVI and used the Google street view image and identified the green view index is more related to walking time than the greenery variables, such as park area and number of street trees. This study will be useful in street tree planning. 

3rd, September

Werbin, Z. R., Heidari, L., Buckley, S., Brochu, P., Butler, L. J., Connolly, C., ... & Hutyra, L. R. (2020). A tree-planting decision support tool for urban heat mitigation. PloS one, 15(10), e0224959. 

• It is interesting to see how trees are selected for the user. Nowadays we are trying to collect data from phone applications from citizen sciences. Hope, our collected data could be used to the public for better use, decision making, and so on. 

2nd, September

Zhu, Z., & Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. Remote sensing of Environment, 144, 152-171. 

• Trying to understand the method of the CCDC algorithm and apply the method in the Seoul area. However, the cropland and grass and deciduous and mixed forest land cover type were easily misclassified in my research. The sampling points for training should be increased to higher the accuracy of CCDC. 

1st, September

Cheon, S., & Kim, J. A. (2020). Quantifying the influence of urban sources on night light emissions. Landscape and Urban Planning, 204, 103936. 

• According to this study, green areas in Seoul account for 42%. However, this study focused on the night light emissions. In Seoul, the highest light emissions were higher than 200 nW/ cm2sr and mostly the high light emission was caused by the commercial area. They have developed a quantitative model and expected to estimate a high level of the baseline light-emission propensity in Seoul.

4th, August 

Paredes, M., & Quiles, M. J. (2015). The effects of cold stress on photosynthesis in Hibiscus plants. PLoS One, 10(9), e0137472. 

• This paper identifies how the Hibiscus plant is affected by cold stress. It was interesting to see how plant response to coldness, since many study focus on heat or water stress. The PSII yield value of the control plat showed around 0.6, which is similar with my rooftop experiment result. 

3rd, August 

Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote sensing of environment, 113(5), 893-903. 

• Provides equations and rescaling factors of how Landsat data-set convert digital numbers to absolute units.  

2nd, August 

Su, Y., Wu, F., Ao, Z., Jin, S., Qin, F., Liu, B., ... & Guo, Q. (2019). Evaluating maize phenotype dynamics under drought stress using terrestrial lidar. Plant methods, 15(1), 11. 

• Global climate change has brought more frequent heatwaves and droughts. Thus, LiDAR was used to monitor how maize response to drought.  Interestingly, yield data had no significant difference with the control group.  The experiment lasts for 95 days but in my point of view, the maize phenotypes seem to be affected by the phenology than the drought. 

1st, August 

Junttila, S., Sugano, J., Vastaranta, M., Linnakoski, R., Kaartinen, H., Kukko, A., ... & Hyyppä, J. (2018). Can leaf water content be estimated using multispectral terrestrial laser scanning? A case study with Norway Spruce seedlings. Frontiers in plant science, 9, 299. 

• Unexpectedly, the seedlings were highly resistant to drought stress. The equivalent water thickness targeted by this study decreased 35 days after the start of the experiment. Although LiDAR was used in this paper, only reflectance data was utilized.

4th, July

Ke, Y., Im, J., Lee, J., Gong, H., & Ryu, Y. (2015). Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observations. Remote Sensing of Environment, 164, 298-313. 

• Compared to Landsat 7, Landsat 8 has a narrower near-infrared waveband. Thus, the Landsat 8 had been less influenced by water vapor absorption. However, Landsat 8 and Landsat 7 derived NDVI showed good agreement, with low bias errors (±0.05) and strong R2 (0.84 to 0.98).

3rd, July

de Jong, R., Verbesselt, J., Schaepman, M. E., & De Bruin, S. (2012). Trend changes in global greening and browning: contribution of short‐term trends to longer‐term change. Global Change Biology, 18(2), 642-655. 

• Monitored global NDVI changes for 27 years. Greening and browning were shown in about 15% of the global land area.  In detail, the Northern Hemisphere showed a greening trend while browning trends were shown in the Southern Hemisphere. 

2nd, July

Marrs, J. K., Reblin, J. S., Logan, B. A., Allen, D. W., Reinmann, A. B., Bombard, D. M., ... & Hutyra, L. R. Solar‐induced fluorescence does not track photosynthetic carbon assimilation following induced stomatal closure. Geophysical Research Letters, e2020GL087956.

• Identified how SiF and other functions change when water stress is performed on a single tree in different branches. Water stress was applied using abscisic acid and pressure cuff. From the experiment, this paper concluded that leaf-level chlorophyll fluorescence does not have a significant relationship with the photosynthesis. 

1st, July

Helm, L. T., Shi, H., Lerdau, M. T., & Yang, X. (2020). Solar‐induced chlorophyll fluorescence and short‐term photosynthetic response to drought. Ecological Applications. 

• Drought experiment was performed on a leaf-level scale to monitor how vegetation functions change by stress. The net photosynthesis, stomatal conductance, and electron transport rate showed a significant change when drought stress occurred. However, SiF values decreased by 21% compared to the controlled trees. 

4th, June

Butnor, J. R., Doolittle, J. A., Johnsen, K. H., Samuelson, L., Stokes, T., & Kress, L. (2003). Utility of ground‐penetrating radar as a root biomass survey tool in forest systems. Soil Science Society of America Journal, 67(5), 1607-1615. 

• Estimating root biomass is a challenging task. The ground-penetrating radar (GPR) sensor is a powerful tool to visualize the below-ground condition. I expected the GPR is similar to the LiDAR but it was sensitive to the soil conditions (e.g, soil water contents) and depend upon the sampling skills. From the given equation, the GPR seems to underestimate the root biomass. This could be caused by the transmission range (0 - 30 cm) and GPR mainly observes the fine root are. It would be interesting to estimate total tree biomass by using both GPR and LiDAR. 

3rd, June

Meineke, E., Youngsteadt, E., Dunn, R. R., & Frank, S. D. (2016). Urban warming reduces aboveground carbon storage. Proceedings of the Royal Society B: Biological Sciences, 283(1840), 20161574. 

• This paper expects the urban trees will sequester less carbon in the future caused by warming.  The willow oak tree photosynthesis and growth result showed a decreasing pattern caused by urban warming and reduced around 27 tonnes of carbon only in the Raleigh area. 

2nd, June

Vicari, M. B., Pisek, J., & Disney, M. (2019). New estimates of leaf angle distribution from terrestrial LiDAR: Comparison with measured and modelled estimates from nine broadleaf tree species. Agricultural and forest meteorology, 264, 322-333. 

• This paper measured the leaf angle distribution with a terrestrial LiDAR. The method was fascinating since the processing time was fast (less than 2 min per tree) but also had high accuracy. The scanning should be made in multiple positions (>2) within a 20 m distance between the target tree and the sensor. 

1st, June

Winbourne, J. B., Jones, T. S., Garvey, S. M., Harrison, J. L., Wang, L., Li, D., ... & Hutyra, L. R. (2020). Tree Transpiration and Urban Temperatures: Current Understanding, Implications, and Future Research Directions. BioScience. 

• Urban trees are well known to reduce the air/surface temperate by the tree shade and evapotranspiration. However, caused by the urban extend larger trees could be removed and replaced by young trees. This could increase the number of trees and lead to increase canopy crown cover but the older trees (i.e. existed trees) could provide more benefit to the urban ecosystem. 

4th, May

Wan, P., Wang, T., Zhang, W., Liang, X., Skidmore, A. K., & Yan, G. (2019). Quantification of occlusions influencing the tree stem curve retrieving from single-scan terrestrial laser scanning data. Forest Ecosystems, 6(1), 43. 

• The occlusion effect is an unavoidable phenomenon when using the LiDAR scanner over a dense area. To reduce the occlusion effect, many studies use multiple-scan mode. However, this paper identified the single-scene mode also has the capacity to accurately estimate tree structures such as DBH when the scanning area is small (10 by 10 m) or when the occlusion rate is less than 35%.

3rd, May

Watson, G. W. (1998). Tree growth after trenching and compensatory crown pruning. Journal of Arboriculture, 24, 47-53. 

• The pruning did not affect the diameter growth but it increased the branch growth and decreased the dieback.  

2nd, May

Williams, C. J., LePage, B. A., Vann, D. R., Tange, T., Ikeda, H., Ando, M., ... & Sweda, T. (2003). Structure, allometry, and biomass of plantation Metasequoia glyptostroboides in Japan. Forest Ecology and Management, 180(1-3), 287-301. 

• In Metasequoia tree, approximately 87% of above ground biomass was allocated to the stem, 9% to branch, and 4% to foliage. The foliage biomass had high correlation with the branch diameter.  Interestingly, lager diameter trees had lower foliage biomass. 

1st, May

Yao, Z., Liu, J., Zhao, X., Long, D., & Wang, L. (2015). Spatial dynamics of aboveground carbon stock in urban green space: a case study of Xi’an, China. Journal of Arid Land, 7(3), 350-360.

• They have estimated the above ground carbon storage in Xian urban green space in the years 2004 and 2010 by using Landsat data. The green space includes trees but also shrubs and herbs. The average annual growth was 8,796 tons. They have used the forest-based allometric equation and multiply a coefficients to represent the urban green space carbon storage. 

4th, April

Nowak, D. J. (1993). Atmospheric carbon reduction by urban trees. Journal of environmental management, 37(3), 207-217. 

• In the 1990s limited study estimated urban tree biomass. This study estimated the total tree carbon storage value in Oakland, which was 11 metric tons per hectare. Approximately 20%, 60%, and 20% of the tree biomass were distributed to crowns, trunk, and top roots, respectively.  Tree death will releases the stored carbon back to the atmosphere and the impact of carbon storage decrease by time, urban trees are only part of a solution to reduce atmospheric carbon.  

3rd, April

Davies, Z. G., Edmondson, J. L., Heinemeyer, A., Leake, J. R., & Gaston, K. J. (2011). Mapping an urban ecosystem service: quantifying above‐ground carbon storage at a city‐wide scale. Journal of applied ecology, 48(5), 1125-1134.

• The largest carbon storage area on the urban land cover was a place owned and managed by the public. The public sites include roadside verges, parks, and recreation grounds. Also, this study pointed out the allometric equation does not fully consider the vegetation structure and management form.

2nd, April

Nowak, D. J., Stevens, J. C., Sisinni, S. M., & Luley, C. J. (2002). Effects of urban tree management and species selection on atmospheric carbon dioxide. Journal of Arboriculture. 28 (3): 113-122., 28(3). 

• When estimating urban trees above ground biomass, 0.9 was multiplied by the forest-derived allometric equation to account for the pruning effect.

1st, April 

Ngo, K. M., & Lum, S. (2018). Aboveground biomass estimation of tropical street trees. Journal of Urban Ecology, 4(1), jux020. 

• Forest trees' allometric equation overestimates the urban tree above-ground biomass (AGB) around 68 to 427% depending on the tree species. Since urban trees are regularly pruned, different architecture from forest trees could be a reason.

4th, March

Alvarez, E., Duque, A., Saldarriaga, J., Cabrera, K., de Las Salas, G., del Valle, I., ... & Rodríguez, L. (2012). Tree above-ground biomass allometries for carbon stocks estimation in the natural forests of Colombia. Forest Ecology and management, 267, 297-308. 

• The allometric equation is an essential tool for understanding the storage and flux of carbon in trees. The simplest equation is based on tree DBH, however, it is known that the estimation of tree biomass could be significantly improved by including tree height and wood density. This study has added these inputs and estimated the natural forest carbon stocks over Colombia. 

3rd, March

Jin, J., Gergel, S. E., Lu, Y., Coops, N. C., & Wang, C. (2019). Asian cities are greening while some north american cities are browning: Long-term greenspace patterns in 16 cities of the pan-pacific region. Ecosystems, 1-17. 

• Monitoring 16 cities for 28 years using Landsat image. Compared to how NDVI changes in the urban-suburban region between Asia and North America (Pan-Pacific region). Although the GPD increased, in some cities the NDVI was declined. Asian cities have less fragmented vegetation patch and North American cities showed directional greenspace patterns. 

2nd, March

Liang, X., Wang, Y., Jaakkola, A., Kukko, A., Kaartinen, H., Hyyppä, J., ... & Liu, J. (2015). Forest data collection using terrestrial image-based point clouds from a handheld camera compared to terrestrial and personal laser scanning. IEEE transactions on geoscience and remote sensing, 53(9), 5117-5132. 

• Large point cloud data can increase the uncertainty of tree structure information.

1st, March

Zheng, Q., Weng, Q., & Wang, K. (2020). Correcting the Pixel Blooming Effect (PiBE) of DMSP-OLS nighttime light imagery. Remote Sensing of Environment, 240, 111707. 

• Blur effects can be identified when using DMSP-OLS night light (NTL) images. These effects are critical in the urban region since DMSP-OLS NTL imagery is widely used to monitor the urban area expansion, which has high heterogeneity in a relatively small area. However, this paper effectively corrected the blurring effect by using two-stage deblurring approach.

4th, February 

Burt, A., Disney, M., & Calders, K. (2019). Extracting individual trees from lidar point clouds using treeseg. Methods in Ecology and Evolution, 10(3), 438-445. 

• The Treeseg method automatically extracts tree-level point clouds from larger area point clouds. The maximum accuracy is 96% and it is open source software. Therefore, it is useful for TSL users.

3rd, February 

Herrero-Huerta, M., Lindenbergh, R., & Gard, W. (2018). Leaf movements of indoor plants monitored by terrestrial LiDAR. Frontiers in plant science, 9, 189. 

• They have monitored how plant moves by time and light conditions.  By using the point cloud data, it is quantitatively confirmed plants exposed in natural light conditions always have a lager variation compared to the plant under the darkness light condition. 

2nd, February 

Du, S., Lindenbergh, R., Ledoux, H., Stoter, J., & Nan, L. (2019). AdTree: Accurate, Detailed, and Automatic Modelling of Laser-Scanned Trees. Remote Sensing, 11(18), 2074. 

• The AdTree method seems to perform better than the TreeQSM method (This could be told by the thin branch detection.). However, it would have been more useful if there were some comparisons of how implicit tree attributes differ by methods.  

1st, February 

Meng, L.,  Mao, J., Zhou, Y., Richardson, A. D., Lee, X., Thornton P. E., ... & Jia, G. (2020). Urban warming advances spring phenology but reduces the response of phenology to temperature in the conterminous United States.Proceedings of the National Academy of Sciences, 201911117

• The authors compared the urban and rural region start of season and climate variables over 85 cities across the US with the 14 years MODIS satellite data.  Consequently, they identified that in the cold region, the rate of advancement will likely slow down under future warming. 

4th, January 

Calders, K., Newnham, G., Burt, A., Murphy, S., Raumonen, P., Herold, M., ... & Kaasalainen, M. (2015). Nondestructive estimates of above‐ground biomass using terrestrial laser scanning. Methods in Ecology and Evolution, 6(2), 198-208. 

• When using TreeQSM  model, the patch size (d) and the minimum number of point cloud data (nmin) parameters should undergo the sensitivity analysis. 

3rd, January

Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., & Yan, G. (2016). An easy-to-use airborne LiDAR data filtering method based on cloth simulation. Remote Sensing, 8(6), 501. 

• The cloth simulation filtering algorithm would be a useful tool to separate LiDAR point cloud data to the ground and non-ground matrix.  The algorithm was tested in diverse sites and had relatively high accuracy.  However, when there were rapid slops or bridge the algorithm had low accuracy compared to flat topography. I think this algorithm could be used in my study site (urban park), which does not have a rapid slop.

2nd, January

Cabo, C., Ordóñez, C., López-Sánchez, C. A., & Armesto, J. (2018). Automatic dendrometry: Tree detection, tree height and diameter estimation using terrestrial laser scanning. International journal of applied earth observation and geoinformation, 69, 164-174. 

• It was easy to understand how to use the terrestrial LiDAR data-set. This paper would be very useful to beginners such as myself. However, I am still stuck in the height-normalization section. 

1st, January 

Smith, I. A., Dearborn, V. K., & Hutyra, L. R. (2019). Live fast, die young: Accelerated growth, mortality, and turnover in street trees. PloS one, 14(5), e0215846. 

• Urban street trees grow fast but also die fast compared to rural forest trees. For this reason, although urban trees seem to increase their biomass, the carbon storage in urban street trees decreases over time. Also, wondered what causes this high mortality rate in urban street trees.