Skip to main content

Advertisement

SpringerLink
Log in

Monitoring forest dynamics with multi-scale and time series imagery

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

To learn the forest dynamics and evaluate the ecosystem services of forest effectively, a timely acquisition of spatial and quantitative information of forestland is very necessary. Here, a new method was proposed for mapping forest cover changes by combining multi-scale satellite remote-sensing imagery with time series data. Using time series Normalized Difference Vegetation Index products derived from the Moderate Resolution Imaging Spectroradiometer images (MODIS-NDVI) and Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) images as data source, a hierarchy stepwise analysis from coarse scale to fine scale was developed for detecting the forest change area. At the coarse scale, MODIS-NDVI data with 1-km resolution were used to detect the changes in land cover types and a land cover change map was constructed using NDVI values at vegetation growing seasons. At the fine scale, based on the results at the coarse scale, Landsat TM/ETM+ data with 30-m resolution were used to precisely detect the forest change location and forest change trend by analyzing time series forest vegetation indices (IFZ). The method was tested using the data for Hubei Province, China. The MODIS-NDVI data from 2001 to 2012 were used to detect the land cover changes, and the overall accuracy was 94.02 % at the coarse scale. At the fine scale, the available TM/ETM+ images at vegetation growing seasons between 2001 and 2012 were used to locate and verify forest changes in the Three Gorges Reservoir Area, and the overall accuracy was 94.53 %. The accuracy of the two layer hierarchical monitoring results indicated that the multi-scale monitoring method is feasible and reliable.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Alongi, D. M. (2015). The impact of climate change on mangrove forests. Current Climate Change Reports, 1(1), 30–39.

    Article  Google Scholar 

  • Arenas-Castro, S., Fernández-Haeger, J., & Jordano-Barbudo, D. (2014). Evaluation and comparison of QuickBird and ADS40-SH52 multispectral imagery for mapping Iberian wild pear trees (Pyrus bourgaeana, Decne) in a Mediterranean mixed forest. Forests, 5(6), 1304–1330.

    Article  Google Scholar 

  • Campbell, M. O. N. (2004). Traditional forest protection and woodlots in the coastal savannah of Ghana. Environmental Conservation, 31(03), 225–232.

    Article  Google Scholar 

  • Chehata, N., Orny, C., Boukir, S., Guyon, D., & Wigneron, J. P. (2014). Object-based change detection in wind storm-damaged forest using high-resolution multispectral images. International Journal of Remote Sensing, 35(13), 4758–4777.

    Article  Google Scholar 

  • Chen, J., Zhu, X., Vogelmann, J. E., Gao, F., & Jin, S. (2011a). A simple and effective method for filling gaps in Landsat ETM+ SLC-off images. Remote Sensing of Environment, 115(4), 1053–1064.

    Article  Google Scholar 

  • Chen, G., Hay, G. J., Castilla, G., St-Onge, B., & Powers, R. (2011b). A multiscale geographic object-based image analysis to estimate lidar-measured forest canopy height using QuickBird imagery. International Journal of Geographical Information Science, 25(6), 877–893.

    Article  Google Scholar 

  • Coppin, Jonckheere, P., Nackaerts, I., Muys, K., & Lambin, B. (2004). Digital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing, 25(9), 1565–1596.

    Article  Google Scholar 

  • Ernst, C., Mayaux, P., Verhegghen, A., Bodart, C., Christophe, M., & Defourny, P. (2013). National forest cover change in Congo Basin: deforestation, reforestation, degradation and regeneration for the years 1990, 2000 and 2005. Global Change Biology, 19(4), 1173–1187.

    Article  Google Scholar 

  • Fuller, D. O., Jessup, T. C., & Salim, A. (2004). Loss of forest cover in Kalimantan, Indonesia, since the 1997–1998 El Niño. Conservation Biology, 18(1), 249–254.

    Article  Google Scholar 

  • Gao, F., Masek, J., & Wolfe, R. E. (2009). Automated registration and orthorectification package for Landsat and Landsat-like data processing. Journal of Applied Remote Sensing, 3(1), 691–701.

    Article  Google Scholar 

  • Hansen, M., DeFries, R. S., Townshend, J. R. G., & Sohlberg, R. (2000). Global land cover classification at 1 km spatial resolution using a classification tree approach. International Journal of Remote Sensing, 21(6), 1331–1364.

  • Hansen, M. C., Roy, D. P., Lindquist, E., Adusei, B., Justice, C. O., & Altstatt, A. (2008). A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin. Remote Sensing of Environment, 112(5), 2495–2513.

    Article  Google Scholar 

  • Helmer, E. H., & Ruefenacht, B. (2005). Cloud-free satellite image mosaics with regression trees and histogram matching. Photogrammetric Engineering & Remote Sensing, 71(9), 1079–1089.

    Article  Google Scholar 

  • Houghton, R. A. (2013). Keeping management effects separate from environmental effects in terrestrial carbon accounting. Global Change Biology, 19(9), 2609–2612.

    Article  Google Scholar 

  • Huang, C., Song, K., Kim, S., Townshend, J. R., Davis, P., Masek, J. G., & Goward, S. N. (2008). Use of a dark object concept and support vector machines to automate forest cover change analysis. Remote Sensing of Environment, 112(3), 970–985.

    Article  Google Scholar 

  • Huang, C., Goward, S. N., Masek, J. G., Thomas, N., Zhu, Z., & Vogelmann, J. E. (2010). An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sensing of Environment, 114(1), 183–198.

    Article  Google Scholar 

  • Huemmrich, K. F., & Goward, S. N. (1997). Vegetation canopy PAR absorptance and NDVI: an assessment for ten tree species with the SAIL model. Remote Sensing of Environment, 61(2), 254–269.

    Article  Google Scholar 

  • Im, J., Jensen, J. R., & Tullis, J. A. (2008). Object-based change detection using correlation image analysis and image segmentation. International Journal of Remote Sensing, 29(2), 399–423.

    Article  Google Scholar 

  • Irish, R. R., Barker, J. L., Goward, S. N., & Arvidson, T. (2006). Characterization of the Landsat-7 ETM+ automated cloud-cover assessment (ACCA) algorithm. Photogrammetric Engineering and Remote Sensing, 72(10), 1179–1188.

    Article  Google Scholar 

  • Jin, S., Yang, L., Danielson, P., Homer, C., Fry, J., & Xian, G. (2013). A comprehensive change detection method for updating the National Land Cover Database to circa 2011. Remote Sensing of Environment, 132(10), 159–175.

  • Lamers, P., & Junginger, M. (2013). The ‘debt’is in the detail: a synthesis of recent temporal forest carbon analyses on woody biomass for energy. Biofuels, Bioproducts and Biorefining, 7(4), 373–385.

    Article  CAS  Google Scholar 

  • Law, B. E. (2014). Regional analysis of drought and heat impacts on forests: current and future science directions. Global Change Biology, 20(12), 3595–3599.

    Article  Google Scholar 

  • Leuning, R., Cleugh, H. A., Zegelin, S. J., & Hughes, D. (2005). Carbon and water fluxes over a temperate Eucalyptus forest and a tropical wet/dry savanna in Australia: measurements and comparison with MODIS remote sensing estimates. Agricultural and Forest Meteorology, 129(3), 151–173.

    Article  Google Scholar 

  • Masek, J. G., Vermote, E. F., Saleous, N. E., Wolfe, R., Hall, F. G., Huemmrich, K. F., & Lim, T. K. (2006). A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geoscience and Remote Sensing Letters, 3(1), 68–72.

    Article  Google Scholar 

  • Melaas, E. K., Friedl, M. A., & Zhu, Z. (2013). Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM+ data. Remote Sensing of Environment, 132(6), 176–185.

  • Nielsen, A. A. (2007). The regularized iteratively reweighted MAD method for change detection in multi- and hyperspectral data. IEEE Transactions on Image Processing, 16(2), 463–478.

    Article  Google Scholar 

  • Nilson, T., & Kuusk, A. (1989). A reflectance model for the homogeneous plant canopy and its inversion. Remote Sensing of Environment, 27(2), 157–167.

    Article  Google Scholar 

  • Quin, G., Pinel-Puyssegur, B., Nicolas, J. M., & Loreaux, P. (2014). MIMOSA: an automatic change detection method for SAR time series. IEEE Transactions on Geoscience and Remote Sensing, 52(9), 5349–5363.

    Article  Google Scholar 

  • Regos, A., D’Amen, M., Herrando, S., Guisan, A., & Brotons, L. (2015). Fire management, climate change and their interacting effects on birds in complex Mediterranean landscapes: dynamic distribution modelling of an early-successional species—the near-threatened Dartford Warbler (Sylvia undata). Journal of Ornithology, 156(1), 275–286.

  • Soudani, K., François, C., Le Maire, G., Le Dantec, V., & Dufrêne, E. (2006). Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands. Remote Sensing of Environment, 102(1), 161–175.

    Article  Google Scholar 

  • Stow, D., Hamada, Y., Coulter, L., & Anguelova, Z. (2008). Monitoring shrubland habitat changes through object-based change identification with airborne multispectral imagery. Remote Sensing of Environment, 112(3), 1051–1061.

    Article  Google Scholar 

  • Tan, Y. Y., Wang, X., Li, C. H., Cai, Y. P., Yang, Z. F., & Wang, Y. L. (2012). Estimation of ecological flow requirement in Zoige Alpine Wetland of southwest China. Environmental Earth Sciences, 66(5), 1525–1533.

    Article  Google Scholar 

  • Webster, M., Forest, C., Reilly, J., Babiker, M., Kicklighter, D., Mayer, M., et al. (2003). Uncertainty analysis of climate change and policy response. Climatic Change, 61(3), 295–320.

    Article  Google Scholar 

  • Xin, Q., Olofsson, P., Zhu, Z., Tan, B., & Woodcock, C. E. (2013). Toward near real-time monitoring of forest disturbance by fusion of MODIS and Landsat data. Remote Sensing of Environment, 135(4), 234–247.

Download references

Acknowledgments

This research is supported by National Science Foundation for Distinguished Young Scholars of China (grant no. 41501365) and National High Technology and Development Program of China (863 Program) (grant no. 2012AA12A304). Additional funding and supporting were also provided by Fundamental Research Funds for the Central Universities (grant no. 2014QC018), Key Laboratory for National Geographic Census and Monitoring, and National Administration of Surveying, Mapping and Geoinformation (grant no. 2013NGCM05).

Author information

Authors and Affiliations

  1. College of Horticultural and Forestry Sciences, Key Laboratory of Horticultural Plant Biology, Ministry of Education, Huazhong Agricultural University, No. 1, Shizishan Street, Hongshan District, Wuhan, 430070, China

    Chunbo Huang, Zhixiang Zhou, Di Wang & Yuanyong Dian

Authors
  1. Chunbo Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhixiang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Di Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuanyong Dian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Zhixiang Zhou or Yuanyong Dian.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, C., Zhou, Z., Wang, D. et al. Monitoring forest dynamics with multi-scale and time series imagery. Environ Monit Assess 188, 273 (2016). https://doi.org/10.1007/s10661-016-5271-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-016-5271-x

Keywords

Search

Navigation