Skip to main content

Advertisement

SpringerLink
Log in

Spatiotemporally monitoring forest landscape for giant panda habitat through a high learning-sensitive neural network in Guanyinshan Nature Reserve in the Qinling Mountains, China

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

During the 1970s and 1990s, Guanyinshan Nature Reserve (GNR), a giant panda (Ailuropoda melanoleuca) distribution area historically, had experienced periodic commercial logging. After officially logging stopping in 1998 and converting to a giant panda nature reserve in 2002, GNR got the chance on forest restoration. It is very necessary to monitor the spatiotemporal change of its forest habitat. It is also widely known that it is difficult to make accurate mapping the mountainous area based on images through traditional classification algorithm. So, this study aims to monitor the spatiotemporal change of mountainous habitat in GNR in order to provide proper suggestions for giant panda conservation. The research applied a multilayer perceptron model, a high learning-sensitive algorithm, to classify the land cover types and monitor habitat change in GNR by using Landsat images acquired in 1978, 1988, 1997 and 2007, respectively. Our results showed that: (1) three types of forests composed the main landscape of the GNR, and an increase of 7.7% forest coverage occurred within 30 years. (2) Due to logging, there were many forest clearing-cutting areas in 1997 and swaths of shrub–grass in 1978 and 1988. However, these two types of landscape were strongly reduced by 2007 due to more attention and protection. (3) A decrease in the number of patches, an increase in the mean patch size, and an over-time decreasing in the mean nearest neighbor distance all revealed a decreasing on habitat fragmentation. Therefore, reduction in detrimental human activities has helped enhance and expand giant panda habitat toward a healthier and more stable ecosystem.

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

Similar content being viewed by others

References

  • Benediktsson JA, Swain PH, Ersoy OK (1990) Neural network approaches versus statistical methods in classification of multisource remote sensing data. IEEE Trans Geosci Remote Sens 28(4):540–552

    Article  Google Scholar 

  • Bischof H, Schneider W, Pinz AJ (1992) Multispectral classification of Landsat-images using neural networks. IEEE Trans Geosci Remote Sens 30(3):482–490

    Article  Google Scholar 

  • Broadbent EN, Asner GP, Keller MK, Knapp DE, Oliveira PJC, Silva JN (2008) Forest fragmentation and edge effects from deforestation and selective logging in the Brazilian Amazon. Biol Conserv 141:1745–1757

    Article  Google Scholar 

  • Brown DG, Lusch DP, Duda KA (1998) Supervised classification of types of glaciated landscapes using digital elevation data. Geomorphology 21(3–4):233–250

    Article  Google Scholar 

  • Cao Q (2006) Qinling wild vertebrates effective protection and management of long-term mechanism. Master dissertation, Northwest A & T University (in Chinese)

  • Cao Q, Yang SQ, Yong YG (2003) Influence of the Forest Management on the activity of the giant panda in Longcaoping forest area. J Northwest For Univ 18(4):110–112 (in Chinese)

    Google Scholar 

  • Cao Q, Zhu Y, Ruan YQ, Yong LJ, Wang XH, Zhang WH (2009) Distribution patterns of giant panda in Guanyinshan and Foping nature reserves. Chin J Appl Ecol 20(9):2271–2276 (in Chinese)

    Google Scholar 

  • Chen LD, Liu XH, Fu BJ (1999) Evaluation on giant panda habitat fragmentation in Wolong Nature Reserve. Acta Ecol Sin 3:291–297 (in Chinese)

    Google Scholar 

  • Civco DL (1993) Artificial neural networks for land-cover classification and mapping. Int J Geogr Inf Sci 7(2):173–186

    Article  Google Scholar 

  • Fan LQ, Dong L, Zhang SL, Ran JH, Yue BS (2010) Landscape pattern of giant panda habitat in the Liangshan Mountains, Sichuan, China. Chin J Appl Environ Biol 16(2):179–184 (in Chinese)

    Article  Google Scholar 

  • Feng LG (2008) The administration differentiation of and protective measures for giant panda habitats in Qinling mountains of Shaanxi province. Shaanxi For Sci Technol 2:73–77 (in Chinese)

    Google Scholar 

  • Glista DJ, Devault TL, Dewoody JA (2009) A review of mitigation measures for reducing wildlife mortality on roadways. Landsc Urban Plan 91(1):1–7

    Article  Google Scholar 

  • Gopal S, Woodcock C (1996) Remote sensing of forest change using artificial neural networks. IEEE Trans Geosci Remote Sens 34(2):398–404

    Article  Google Scholar 

  • Jacobson A, Dhanota J, Godfrey J, Jacobson H, Rossman Z, Stanish A, Walker H, Riggio J (2015) A novel approach to mapping land conversion using Google Earth with an application to East Africa. Environ Model Softw 72:1–9

    Article  Google Scholar 

  • Jia XD, Liu XH, Yang XZ, Wu PF, Songer M, Cai Q, Xiangbo He, Zhu Y (2014) Seasonal activity patterns of ungulates in Qinling Mountains based on camera-trap data. Biodivers Sci 22:737–745 (in Chinese)

    Article  Google Scholar 

  • Jiang GS, Zhang MH, Ma JZ (2005) The fragmentation and impact factors of red deer habitat in Wandashan region, Heilongjiang Province, China. Acta Ecol Sin 7:1691–1698 (in Chinese)

    Google Scholar 

  • Kanellopoulos I, Wilkinson GG (1997) Strategies and best practice for neural network image classification. Int J Remote Sens 18(4):711–725

    Article  Google Scholar 

  • Laurance WF (1994) Rainforest fragmentation and the structure of small mammal communities in tropical Qeennsland. Biol Conserv 69:23–32

    Article  Google Scholar 

  • LFB (Longcaoping Forestry Bureau) (1989) History of longcaoping Forestry Bureau. (unpublished document, in Chinese)

  • Liu XH (2001) Mapping and modelling the habitat of giant pandas in Foping Nature Reserve, China. Febodruk Besloten Vennootschap (BV), Enschede

    Google Scholar 

  • Liu XH, Jin XL (2008) Habitat feature of giant pandas’ high-frequency activity areas on southern slope of the Qinling Mountains and habitat selection of giant panda. Chin J Ecol 27(1):2123–2128 (in Chinese)

    Google Scholar 

  • Liu JG, Ouyang ZY, Taylor WW, Groop R, Tan YC, Zhang HM (1999) A framework for evaluating the effects of human factors on wildlife habitat: the case of giant pandas. Conserv Biol 13(6):1360–1370

    Article  Google Scholar 

  • Liu XH, Skidmore AK, Van Oosten H (2002) Integration of classification methods for improvement of land-cover map accuracy. ISPRS J Photogramm Remote Sens 56(4):257–268

    Article  Google Scholar 

  • Liu XH, Skidmore AK, Van Oosten H (2003) An experimental study on spectral discrimination capability of a backpropagation neural network classifier. Int J Remote Sens 24(4):673–688

    Article  Google Scholar 

  • Liu XH, Toxopeus AG, Skidmore AK, Shao XM, Dang GD, Wang TJ, Prins HHT (2005) Giant panda habitat selection in Foping Nature Reserve, China. J Wildl Manag 69(4):1623–1632

    Article  Google Scholar 

  • Liu XH, Skidmore AK, Bronsveld MC (2006) Assessment of giant panda habitat based on integration of expert system and neural network. Chin J Appl Ecol 17(3):438–443 (in Chinese)

    Google Scholar 

  • Liu XH, Shao XM, He XB, Gao C, Tian RX, Zhu Y, Dang GD (2009) Giant panda habitat dynamics in a recovering nature reserve. In: Proceeding of the 23rd annual meeting and 2009 international congress for conservation biology. Beijing, pp 299–300

  • Liu XH, Wu PF, Songer M, Cai Q, He XB, Zhu Y, Shao XM (2013) Monitoring wildlife abundance and diversity with infra-red camera traps in Guanyinshan Nature Reserve of Shaanxi Province, China. Ecol Indic 33:121–128

    Article  Google Scholar 

  • Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw 15:101–124

    Article  Google Scholar 

  • Mas JF (2004) Mapping land use/cover in a tropical coastal area using satellite sensor data, GIS and artificial neural networks. Estuar Coast Shelf Sci 59:219–230

    Article  Google Scholar 

  • Mas JF, Flores JJ (2008) The application of artificial neural networks to the analysis of remotely sensed data. Int J Remote Sens 29(3):617–663

    Article  Google Scholar 

  • Paola JD, Schowengerdt RA (1995) A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification. IEEE Trans Geosci Remote Sens 33:981–996

    Article  Google Scholar 

  • Reid WV, Kenton RM (1989) Keeping options alive: the scientific basis for conserving biodiversity. World Resources Institute, Washington

    Google Scholar 

  • Rojas R (1996) Neural networks—a systematic introduction. Springer, Berlin

    Google Scholar 

  • Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL, the PDP Research Group (eds) Parallel distributed processing: explorations in the microstructure of cognition. MIT Press, Cambridge, pp 318–362

    Google Scholar 

  • Satir O, Erdogan MA (2016) Monitoring the land use/cover changes and habitat quality using Landsat dataset and landscape metrics under the immigration effect in subalpine eastern Turkey. Environ Earth Sci 75:1118

    Article  Google Scholar 

  • SFA (State Forestry Administration of China) (1989) World Wildlife Fund for Nature The giant pandas and habitats survey report in China. Science Press, Beijing (in Chinese)

    Google Scholar 

  • SFA (State Forestry Administration of China) (2006) The third national survey report on giant panda in China. Science Press, Beijing (in Chinese)

    Google Scholar 

  • Skidmore AK (1989) An expert system classifies eucalypt forest types using Thematic Mapper data and a digital terrain model. Photogramm Eng Remote Sens 55(10):1449–1464

    Google Scholar 

  • Wu ZJ, Li YM (2003) Effects of habitat fragmentation on survival of animal populations. Acta Ecol Sin 11:2424–2435 (in Chinese)

    Google Scholar 

  • Wu PF, Liu XH, Cai Q, He XB, Songer M, Zhu Y, Shao XM (2012) The application of infrared camera in mammal research in Guanyinshan Nature Reserve, Shaanxi. Acta Theriol Sin 32:67–71 (in Chinese)

    Google Scholar 

  • Wu PF, Liu XH, Shao XM, Zhu Y, Cai Q (2013) GIS application in evaluating the potential habitat of giant pandas in Guanyinshan Nature Reserve, Shaanxi Province. J Environ Inf 21:55–62

    Article  Google Scholar 

  • Zhang HK (2008) The study on vegetation and dominant plant flora characteristics in nature reserve in Guanyinshan Mountain, Shaanxi. Master Dissertation, Northwest A&F University (in Chinese)

  • Zhang S, Liu XH, Jin Q, Li JH, Jin XL, Wei FW (2004) The relationship between landscape pattern and the habitat of giant pandas on the southern slope of the middle Qinling Mountains. Acta Ecol Sin 24(9):1950–1957 (in Chinese)

    Google Scholar 

  • Zhang HK, Cheng HF, Zhu Y, Liu XH, Li DQ (2007) Status and conservation strategy of Giant Panda habitat in Qinling tunnel area of 108 national road. J Shaanxi Normal Univ (Nat Sci) 35:116–119 (in Chinese)

    Google Scholar 

  • Zheng YJ, Yin HJ, Ren JA (2012) With larch mainly invasive species of giant pandas to survive environmental impact and countermeasure research. Beijing Agric 24:81–82 (in Chinese)

    Google Scholar 

  • Zhou L, Yang X (2008) Use of neural networks for land cover classification from remotely sensed imagery. Int Arch Photogramm Remote Sens Spatial Inf Sci 37:575–578

    Google Scholar 

  • Zhou SQ, Huang JY, Tan YC, Zhou XP, Wang PY, Zhang HM (2006) Diversity of the plant community of giant pandas’ habitat in Wolong Nature Reserve. IV. The effects of human disturbance on the species diversity of the plant community. J Sichuan For Sci Technol 27(6):35–40 (in Chinese)

    Google Scholar 

Download references

Acknowledgements

This research received financial supports from (1) National Natural Science Foundation of China (NSFC) Project (41271194) and (2) International Project of Giant Panda Conservation Founded by State Forestry Administration (CM1424). We thank Ms Eve Bohnett for her work on editing and commenting on the paper draft and Ms Lanmei Liu for creating the map of the giant panda signs. We also thank Shaanxi Forest Department to provide the Shaanxi data from the third national survey on Giant Panda Population and Habitat, and Investigation and Planning Institute, State Forestry Administration to share partial data set from the fourth national survey.

Author information

Authors and Affiliations

  1. State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China

    Xuehua Liu

  2. College of Resources and Environmental Sciences, China Agricultural University, Beijing, 100193, China

    Pengfeng Wu & Xiaoming Shao

  3. Conservation Ecology Center, Smithsonian Conservation Biology Institute, Front Royal, VA, 22630, USA

    Melissa Songer

  4. Shaanxi Guanyinshan Nature Reserve, Foping County, 723400, Shaanxi, China

    Qiong Cai & Yun Zhu

  5. Shaanxi Foping Nature Reserve, Foping County, 723400, Shaanxi, China

    Xiangbo He

  6. College of Life Science, Shenyang Normal University, Shenyang, 110034, China

    Pengfeng Wu

Authors
  1. Xuehua Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pengfeng Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaoming Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Melissa Songer
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qiong Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yun Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xiangbo He
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xuehua Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, X., Wu, P., Shao, X. et al. Spatiotemporally monitoring forest landscape for giant panda habitat through a high learning-sensitive neural network in Guanyinshan Nature Reserve in the Qinling Mountains, China. Environ Earth Sci 76, 589 (2017). https://doi.org/10.1007/s12665-017-6926-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12665-017-6926-9

Keywords

Search

Navigation