2019

4th, December

Wu, J. (2019). Developing General Equations for Urban Tree Biomass Estimation with High-Resolution Satellite Imagery. Sustainability, 11(16), 4347. 

- There was a high correlation between urban tree dry weight biomass (including belowground) and crown-level NDVI (using WorldView 2 satellite) by following power functions.

3rd, December

Dassot, M., Colin, A., Santenoise, P., Fournier, M., & Constant, T. (2012). Terrestrial laser scanning for measuring the solid wood volume, including branches, of adult standing trees in the forest environment. Computers and Electronics in Agriculture, 89, 86-93. 

- Used the TLS-geometric fitting method, which uses the cylinder fitting, to estimate the tree volume, and compared with the destructive volume measurements. The result came out to be quite accurate. However, the author mentioned the destructive method could contain errors since it is manually done.

2nd, December

Gobster, P.H., Hadavi, H., Rigolon, A., & Stewart W.P. (2020). Measuring landscape change, lot by lot: Greening activity in response to a vacant land reuse program. Landscape and Urban Planning, 196, 103729.

- When the residents take ownership of a vacant land became greening.  As a result of the greening program there have been trade-offs between social and ecological goals in the short-term, but wonder how the changed greening area (in the past, vacant land) maintains.  

1st, December 

Wu, D., Phinn, S., Johansen, K., Robson, A., Muir, J., & Searle, C. (2018). Estimating Changes in Leaf Area, Leaf Area Density, and Vertical Leaf Area Profile for Mango, Avocado, and Macadamia Tree Crowns Using Terrestrial Laser Scanning. Remote Sensing, 10(11), 1750. 

- I am not sure how accurate the CloudCompare software CANUPO segmentation algorithm will be. 

4th, November

Stokes, E. C., & Seto, K. C. (2019). Characterizing urban infrastructural transitions for the Sustainable Development Goals using multi-temporal land, population, and nighttime light data. Remote Sensing of Environment, 234, 111430. 

- The meaning (classification) of urbanization was very interesting in this paper. They have compared two contrary, US and India, and identified urban development is not the only case of urbanization. 

3rd, November

Calders, K., Schenkels, T., Bartholomeus, H., Armston, J., Verbesselt, J., & Herold, M. (2015). Monitoring spring phenology with high temporal resolution terrestrial LiDAR measurements. Agricultural and forest meteorology, 203, 158-168. 

- The LiDAR-based PAI also showed a similar pattern as the green index (which could be calculated from the RGB camera image) when tree leaves are fully expanded. This paper pointed out those phenomena in four plots were caused by the wind speed (the maximum wind speed was the fastest at that date). I understand the LiDAR sensor is sensitive in wind. However, wind speed during the scan is important. The highest wind speed in a day seems less relevant.

2nd, November

Disney, M., Burt, A., Calders, K., Schaaf, C., & Stovall, A. (2019). Innovations in ground and airborne technologies as reference and for training and validation: terrestrial laser scanning (TLS). Surveys in Geophysics, 1-22.

- The needs/importance of terrestrial laser scanning to estimate above-ground biomass without destructive measurements. The main uncertainty occurs by the wood density when using the TLS data to estimate AGB. Although it is difficult to compare with the real data (by destructing), TLS based AGB is needed for calibration and validation. 

1st, November

Lee, J. H., Ko, Y., & McPherson, E. G. (2016). The feasibility of remotely sensed data to estimate urban tree dimensions and biomass. Urban forestry & urban greening, 16, 208-220. 

- Used LiDAR data and airborne photos to estimate tree structures such as DBH, height, and crown cover area. With about 800 tree dataset the author used the allometric equation to identify the biomass. 

4th, October

Wilkes, P., Disney, M., Vicari, M. B., Calders, K., & Burt, A. (2018). Estimating urban above ground biomass with multi-scale LiDAR. Carbon balance and management, 13(1), 10. 

- Estimated how much carbon is stored in the urban forest. Converted tree volume to above-ground biomass and multiplied the carbon factor (0.471). The tree volume was estimated by quantitative structure modeling. However, many trees were discarded from the modeling caused by the incomplete cover, sparsed measurement, and wind effect.  

3rd, October

Rajendra, Y. D., Mehrotra, S. C., Kale, K. V., Manza, R. R., Dhumal, R. K., Nagne, A. D., & Vibhute, A. D. (2014). Evaluation of Partially Overlapping 3D Point Cloud's Registration by using ICP variant and CloudCompare. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(8), 891.

-  Useful information bout Faro 3D Laser Scanner.

2nd, October

Calders, K., Phinn, S., Ferrari, R., Leon, J., Armston, J., Asner, G. P., & Disney, M. (2019). 3D Imaging Insights into Forests and Coral Reefs. Trends in ecology & evolution.

- Mostly papers I read was about how LiDAR sensor was used in forest ecosystem. From this paper I could widen my sight. The coral reefs was also an important factor of climate change and LiDAR sensor provided quantitative and detailed structural information. 

1st, October

Hosoi, F., & Omasa, K. (2009). Detecting seasonal change of broad-leaved woody canopy leaf area density profile using 3D portable LIDAR imaging. Functional Plant Biology, 36(11), 998-1005. 

- Used LiDAR sensor to obtain leaf area density seasonal variation . Although the plot was small (2 by 2 m) the results were clear.  The  maximum LAD was shown at 11 m height during the summer season and decreased in November measurement.  

4th, September

Bienert, A., Scheller, S., Keane, E., Mullooly, G., & Mohan, F. (2006). Application of terrestrial laser scanners for the determination of forest inventory parameters. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(5). 

- This paper gave me an idea of how to extract the ground data from the raw LiDAR data. The hint was the density of the z-axis, then do not have to waste time to eliminate the ground data manually.

3rd, September

Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., ... & Tuanmu, M. N. (2015). Mapping tree density at a global scale. Nature, 525(7568), 201. 

- Estimated global tree number (DBH larger than 10 cm) and found out 3.04 trillion trees exist. Global tree number decreased by 46% caused by human activity (i.e.civilization). Tundra had the highest tree density among 12 ecosystems. 

2nd, September

Croft, T. A. (1978). Nighttime images of the earth from space. Scientific American, 239(1), 86-101. 

- The nighttime light data was also used in (or start in) 1970s. In the early days the satellite imagery was influenced by the moonlight, but detecting human activity was clear.  This study focused on gas flares.    

1st, September

Henderson, J. V., Storeygard, A., & Weil, D. N. (2012). Measuring economic growth from outer space. American economic review, 102(2), 994-1028. 

- Developed a statistical model to improve estimates of economic growth (e.g. GDP) for various countries by using the nighttime light data.  It was interesting to see the clear difference between South and North Korea. 

4th, August

Keola, S., Andersson, M., & Hall, O. (2015). Monitoring economic development from space: using nighttime light and land cover data to measure economic growth. World Development, 66, 322-334. 

- The author combined nighttime light and land cover data to monitor economic growth. The land cover data was used since agriculture and forestry does not emit light but should be considered in economic growth.  

3rd, August 

Oveland, I., Hauglin, M., Gobakken, T., Næsset, E., & Maalen-Johansen, I. (2017). Automatic estimation of tree position and stem diameter using a moving terrestrial laser scanner. Remote Sensing, 9(4), 350. 

- I was interested in how to detect tree position in the pointcloud data. However, this paper used an algorithm created by Bergstrom (Matlab code). The accuracy of the DBH estimates was high and the algorithm result was satisfied in this paper but they have mentioned how to improve it. 

2nd, August 

Van Bueren, E., van Bohemen, H., Itard, L., & Visscher, H. (2012). Sustainable urban environments. An Ecosystems Approach. 

- This chapter divided the urban form and list the characteristic of each urban forms. Also pointed out the conflicts of the compact city. I was interested in the side effects of the green and open spaces in compact cities(read Chapter 10).

1st, August 

Stagoll, K., Lindenmayer, D. B., Knight, E., Fischer, J., & Manning, A. D. (2012). Large trees are keystone structures in urban parks. Conservation Letters, 5(2), 115-122. 

- Large trees provide habitat for urban wildlife. The size of the trees per hectare had a weak relationship with the bird richness. There was a functional difference when tree DBH was less than 40 cm and larger than 50 cm. The number of trees per hectare, however, had a positive effect.

4th, July

Czerniak, J., Hargreaves, G., & Beardsley, J. (2007). Large parks. New York: Princeton Architectural Press. 

- Try to understand about large parks. The definition of large in this book was  the size of the park, which is lager than 500  acres. 

3rd, July

Zhou, S., Kang, F., Li, W., Kan, J., Zheng, Y., & He, G. (2019). Extracting Diameter at Breast Height with a Handheld Mobile LiDAR System in an Outdoor Environment. Sensors, 19(14), 3212. 

- Used mobile LiDAR to extract tree DBH. The ground point clouds were removed with RANSAC algorithm. For accurate DBH estimation, this paper used various algorithms and extract the inliers point clouds. The errors could be made by the walking speed.  

2nd, July

Herrmann, D. L., Schifman, L. A., & Shuster, W. D. (2018). Widespread loss of intermediate soil horizons in urban landscapes. Proceedings of the National Academy of Sciences, 115(26), 6751-6755. 

- It is easy to overlook soil in an urban ecosystem. Since all urban infrastructures are build up on top of the soil layer by soil mounding it should not be ignored.  By analyzing 11 cities soil, they identified B horizons, which is difficult to be replaced, are lost in urban soil. The B horizon contains a large number of minerals and organic matter. Thus, the missing B horizon will affect the urban ecosystem function. 

1st, July 

Choi, H., Song, Y., & Jang, Y. (2019). Urban Forest Growth and Gap Dynamics Detected by Yearly Repeated Airborne Light Detection and Ranging (LiDAR): A Case Study of Cheonan, South Korea. Remote Sensing, 11(13), 1551. 

- In the urban forest, the vertical and LAI components do not change much within several years (~3 years), but the spatial distribution of canopy and LAD evidently changes during a year. This study pointed out the importance of suggesting a rage of the acquisition date and grid size for LiDAR measurements. 

4th, June

Pastore, M. A., Lee, T. D., Hobbie, S. E., & Reich, P. B. (2019). Strong photosynthetic acclimation and enhanced water‐use efficiency in grassland functional groups persist over 21 years of CO2 enrichment, independent of nitrogen supply. Global change biology. 

- It was interesting to see a long-term measurement of acclimation CO2 over different species.  One wonder is what happened in year 2002. 

3rd, June

Dronova, I., Friedman, M., McRae, I., Kong, F., & Yin, H. (2018). Spatio-temporal non-uniformity of urban park greenness and thermal characteristics in a semi-arid region. Urban forestry & urban greening, 34, 44-54.  

- Compared city surface brightness temperature and urban park NDVI among 135 parks in one year (spatio-temporal). Their trends were not synchronous but had a significant negative correlation.    

2nd, June

Kim, H. Y., Ko, J., Kang, S., & Tenhunen, J. (2013). Impacts of climate change on paddy rice yield in a temperate climate. Global change biology, 19(2), 548-562. 

- This paper simulated how rice yield changes in long-term (increase air temperature). Both 2050 and 2100 year rice yield decreased gradually. It was interesting to know the change of rice yield also depends on the location (More changes in higher latitude).  

1st, June

Moskal, L. M., & Zheng, G. (2012). Retrieving forest inventory variables with terrestrial laser scanning (TLS) in urban heterogeneous forest. Remote Sensing, 4(1), 1-20. 

- Developed an algorithm. When processing the DBH measurement, the occluded trees were assumed to be a circle, which is unrealistic. However, accuracy were very high (r2=0.9).

4th, May 2019

Kankare, V., Holopainen, M., Vastaranta, M., Puttonen, E., Yu, X., Hyyppä, J., ... & Alho, P. (2013). Individual tree biomass estimation using terrestrial laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing, 75, 64-75. 

-  Most of the DBH estimation using LiDAR was manually done. High r-square between LiDAR and field measurement would come out of this reason. However, this paper stressed out the importance of branch biomass when estimating the aboveground biomass.

3rd, May 2019

Bazezew, M. N., Hussin, Y. A., & Kloosterman, E. H. (2018). Integrating Airborne LiDAR and Terrestrial Laser Scanner forest parameters for accurate above-ground biomass/carbon estimation in Ayer Hitam tropical forest, Malaysia. International journal of applied earth observation and geoinformation, 73, 638-652. 

-  By combining both airborne and terrestrial LiDAR system, most of the tree was detected including understory vegetation (only 6% were missing). It is well known that estimating tree DBH and height is difficult in the dense forest. Although this paper had a high correlation between automatic and manual measurements of DBH it was mostly based on the given program algorithm.

2nd, May 2019

Jo, H. K., & Ahn, T. W. (2012). Carbon storage and uptake by deciduous tree species for urban landscape. Journal of the Korean Institute of Landscape Architecture, 40(5), 160-168. 

-  This study used DBH value to estimate carbon storage and uptake from individual trees (common tree species in Korea urban landscape design). The correlations between DBH and carbon storage and uptake showed high r-square (0.94 to 0.99). 

1st, May 2019

Heo, H. K., Lee, D. K., Park, J. H., & Thorne, J. H. (2019). Estimating the heights and diameters at breast height of trees in an urban park and along a street using mobile LiDAR. Landscape and Ecological Engineering, 1-11. 

- To improve the LiDAR measurement in an urban park, the individual tree should be detected without overlapping with other trees. When trees are overlapped it was difficult to estimate the DBH since one algorithm only simulates in round shape, which rarely appears in the real word. 

4th, April 2019

Song, Y., Jain, A. K., & McIsaac, G. F. (2013). Implementation of dynamic crop growth processes into a land surface model: evaluation of energy, water and carbon fluxes under corn and soybean rotation. Biogeosciences, 10(12), 8039-8066. 

- Could understand the overall importance of the carbon allocation in the land surface model. The main crop type was corn and soybean in this paper. To accurately model the GPP, carbon allocation and the crop root distribution data were important. 

3rd, April 2019

Smith, M. N., Stark, S. C., Taylor, T. C., Ferreira, M. L., de Oliveira, E., Restrepo‐Coupe, N., ... & Figueira, M. (2019). Seasonal and drought‐related changes in leaf area profiles depend on height and light environment in an Amazon forest. New Phytologist. 

- Monthly measured the Amazon forest using LiDAR equipment for 4 years. They have identified the opposite results from their expectation.

2nd, April 2019

Campioli, M., Vicca, S., Luyssaert, S., Bilcke, J., Ceschia, E., Chapin Iii, F. S., ... & Olefeldt, D. (2015). Biomass production efficiency controlled by management in temperate and boreal ecosystems. Nature Geoscience, 8(11), 843.

- Understand about the biomass production efficiency (BPE). BPE could be used as NPP. Most of the natural ecosystem BPE is around 0.4 but when managing the ecosystem it increases to or more than 0.5. Also, the managed cropland ecosystem BPE was near 0.6. 

1st, April 2019

Li, X., Zhang, C., Li, W., Ricard, R., Meng, Q., & Zhang, W. (2015). Assessing street-level urban greenery using Google Street View and a modified green view index. Urban Forestry & Urban Greening, 14(3), 675-685. 

- The street greenery information were less informed. Thus, this study used Google street view information (one location have 6 direction and 3 field of view) and mapped the greenness view index. Although it is useful the map does not show the individual tree location or species of trees. 

4th, March 2019

Li, X., Ratti, C., & Seiferling, I. (2018). Quantifying the shade provision of street trees in urban landscape: A case study in Boston, USA, using Google Street View. Landscape and Urban Planning, 169, 81-91. 

- Used Google street view images to quantify the street tree cover shade. From the result they have identified the street tree cover decrease about 18% of the sky view factor, which indicates solar radiation is blocked by the trees.

3rd, March 2019

Gratani, L., & Varone, L. (2014). Atmospheric carbon dioxide concentration variations in Rome: relationship with traffic level and urban park size. Urban ecosystems, 17(2), 501-511. 

- They identified how much the difference in traffic level and green cover space affects the CO2 emissions over the urban area. The low level of traffic and the higher the green space, the less CO2  was emitted. 

2nd, March 2019

Zhang, Q., & Seto, K. C. (2011). Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data. Remote Sensing of Environment, 115(9), 2320-2329. 

- This paper monitored nighttime light of four countries (USA, China, Japan, and India) to identify the urban changes. Since previous studies quantified urbanization at a city scale, this study quantified urbanization on a global scale. When comparing with the Landsat satellite imagery, nighttime light imagery well captured the growth of the urban. The classification of urbanization in five stages was also interesting. 

1st, March 2019

Sutton, P., Roberts, D., Elvidge, C., & Baugh, K. (2001). Census from Heaven: An estimate of the global human population using night-time satellite imagery. International Journal of Remote Sensing, 22(16), 3061-3076. 

- For night time satellite data, DMSP OLS imagery was widely used. A single threshold was not recommended to divide levels of industrialization. Mostly, 40%, 80%, and 90% threshold was used to capture the urban population. To delineate the urban extension, AVHRR's NDVI and Landsat TM imagery were also used in the previous studies. 

4th, February 2019

Seto, K. C., & Fragkias, M. (2005). Quantifying spatiotemporal patterns of urban land-use change in four cities of China with time series landscape metrics. Landscape ecology, 20(7), 871-888. 

- During about 10 years (from 1988 to 1999), the urban area has rapidly extended. This paper used 10 Landsat Thematic Mapper (TM) images to develop maps of urban extent. More detail methods are listed in Seto et al (2002). The decreasing values of urban patch size indicate that new urban centers are growing faster than existing urban areas.

3rd, February 2019

Florida, R., Mellander, C., & Gulden, T. (2012). Global metropolis: assessing economic activity in urban centers based on nighttime satellite images. The Professional Geographer, 64(2), 178-187. 

- This paper used satellite data to estimate economic activity across the world cities and metropolitan area. The author identified Asia is the emerging country in the global economic urbanization (e.g. Japan, South Korea, and Hong Kong). 

2nd, February 2019

Henderson, M., Yeh, E. T., Gong, P., Elvidge, C., & Baugh, K. (2003). Validation of urban boundaries derived from global night-time satellite imagery. International Journal of Remote Sensing, 24(3), 595-609. 

- Night-time satellite imagery was used to monitor urban expansion around the globe. However, determining the appropriate threshold was challenging. 

1st, February 2019

Jiang, Y., van Groenigen, K. J., Huang, S., Hungate, B. A., van Kessel, C., Hu, S., ... & Chen, J. (2017). Higher yields and lower methane emissions with new rice cultivars. Global change biology, 23(11), 4728-4738. 

- Higher HI emphasizes a large proportion of carbon is allocated in grain production. Thus, some studies relates HI to methane emission, since the higher HI will generate smaller amount of carbon to the roots. 

4th, January 2019

Nouri, H., Beecham, S., Anderson, S., & Nagler, P. (2014). High spatial resolution WorldView-2 imagery for mapping NDVI and its relationship to temporal urban landscape evapotranspiration factors. Remote sensing, 6(1), 580-602. 

- This paper used high-resolution WorldView-2 satellite imagery, which has not been widely published, to identify the relationship between NDVI and ET in a mixed landscape urban park. Curious how they have managed with the tree shadows and it was interesting to know that NDVI and ET correlation decrease as trees undergoes a water stress condition.

3rd, January 2019

Sun, Y., Xie, S., & Zhao, S. (2019). Valuing urban green spaces in mitigating climate change: a city‐wide estimate of aboveground carbon stored in urban green spaces of China's Capital. Global change biology. 

- The benefits of the urban tree (green space) in cities. It absorbs the carbon dioxide and stores carbon in trees. They manually collected the data and also used the satellite data.  When using the satellite data, mixed pixels are critical, when moving 10 m to 250 m resolution above ground carbon stores was 76% underestimated. 

2nd, January 2019

Goward, S. N., Tucker, C. J., & Dye, D. G. (1985). North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer. Vegetatio, 64(1), 3-14. 

- During the growing season, the integrated daily mean  NDVI showed a linear relationship with NPP.

1st, January 2019

Jiang, Y., Qian, H., Wang, L., Feng, J., Huang, S., Hungate, B. A., ... & Li, Y. (2018). Limited potential of harvest index improvement to reduce methane emissions from rice paddies. Global change biology. 

- Higher harvest index reduced methane emissions mostly in continuous flooded rice paddy.  Thus, plant breeding strategies are needed. This paper was quite different with our site result.