Abstract
The changing patterns of watersheds in a landscape, driven by human activities, play an important role in non-point source pollution processes. This paper aims to improve the location-weighted landscape contrast index using remote sensing and GIS technology to account for the effects of scale and ecological processes. The hydrological response unit (HRU) with a single land use and soil type was used as the smallest unit. The relationship between the landscape index and typical ecological processes was established by describing the influence of the landscape pattern on non-point source pollution. To verify the research method, this paper used the Yanshi River basin as a study area. The results showed that the relative intensity of non-point source pollution in different regions of the watershed and the location-weighted landscape contrast index based on the minimum HRU can qualitatively reflect the risk of regional nutrient loss.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Chen G, Hay G J, Carvalho L M T, Wulder M A. 2012. Objectbased change detection. International Journal of Remote Sensing, 33 (14): 4 434–4 457.
Chen L D, Fu B J, Xu J Y, Gong J. 2003. Location-weighted landscape contrast index: a scale independent approach for landscape pattern evaluation based on “Source-Sink” ecological processes. Acta Ecologica Sinica, 23 (11): 2 406–2 413. (in Chinese with English abstract)
Emili L A, Greene R P. 2013. Modeling agricultural nonpoint source pollution using a geographic information system approach. Environmental Management, 51 (1): 70–95.
Fichera C R, Modica G, Pollino M. 2012. Land Cover classification and change-detection analysis using multitemporal remote sensed imagery and landscape metrics. European Journal of Remote Sensing, 45 (1): 1–18.
Griffith J A. 2002. Geographic techniques and recent applications of remote sensing to landscape-water quality studies. Water, Air, and Soil Pollution, 138 (1–4): 181–197.
Herzog F, Lausch A, Müller E, Thulke H H, Steinhardt U, Lehmann S. 2001. Landscape metrics for assessment of landscape destruction and rehabilitation. Environmental Management, 27 (1): 91–107.
Herzog F, Lausch A. 2001. Supplementing land-use statistics with landscape metrics: some methodological considerations. Environmental Monitoring and Assessment, 72 (1): 37–50.
Jiang M Z, Chen H Y, Chen Q H. 2013. A method to analyze “source-sink” structure of non-point source pollution based on remote sensing technology. Environmental Pollution, 182: 135–140.
Kumar S, Mishra A. 2015. Critical erosion area identification based on hydrological response unit level for effective sedimentation control in a river basin. Water Resources Manage ment, 29 (6): 1 749–1 765.
Lausch A, Herzog F. 2002. Applicability of landscape metrics for the monitoring of landscape change: issues of scale, resolution and interpretability. Ecological Indicators, 2 (1–2): 3–15.
Liu D L, Hao S L, Liu X Z, Li B C, He S F, Warrington D N. 2013. Effects of land use classification on landscape metrics based on remote sensing and GIS. Environmental Earth Sciences, 68 (8): 2 229–2 237.
Pietroniro A, Prowse T, Hamlin L, Kouwen N, Soulis R. 1996. Application of a grouped response unit hydrological model to a northern wetland region. Hydrological Processes, 10 (10): 1 245–1 261.
Sun R H, Chen L D, Wang W, Wang Z M. 2012. Correlating landscape pattern with total nitrogen concentration using a location-weighted sink-source landscape index in the Haihe River Basin, China. Environment Science, 33 (6): 1 784–1 788. (in Chinese with English abstract)
Verburg P H, Van De Steeg J, Veldkamp A, Willemen L. 2009. From land cover change to land function dynamics: a major challenge to improve land characterization. Journal of Environmental Management, 90 (3): 1 327–1 335.
Wang K, Franklin S E, Guo X L, He Y H, McDermid G J. 2009. Problems in remote sensing of landscapes and habitats. Progress in Physical Geography, 33 (6): 747–768.
Yang X J, Liu Z. 2005. Quantifying landscape pattern and its change in an estuarine watershed using satellite imagery and landscape metrics. International Journal of Remote Sensing, 26 (23): 5 297–5 323.
Yang X J. 2005. Remote sensing and GIS applications for estuarine ecosystem analysis: an overview. International Journal of Remote Sensing, 26 (23): 5 347–5 356.
Yu X, Ng C, 2006. An integrated evaluation of landscape change using remote sensing and landscape metrics: a case study of Panyu, Guangzhou. International Journal of Remote Sensing, 27 (6): 1 075–1 092.
Zehe E, Ehret E, Pfister L, Blume T, Schröder B, WesthoffM, Jackisch C, Schymanski S J, Weiler M, Schulz K, Allroggen N Tronicke, van Schaik L, Dietrich P, Scherer U, Eccard J, Wulfmeyer V, Kleidon A. 2014. HESS Opinions: from response units tofunctional units: a thermodynamic reinterpretation of the HRU concept to link spatial organization and functioning of intermediate scale catchments. Hydrology and Earth System Sciences, 18 (11): 4 635–4 655.
Zhang X, Jiang W G, Zhou Y G, Tang H. 2009. Hydrological response unit division for Dongting Lake based on DEM and GIS. Geography and Geo-Information Science, 25 (4): 17–21. (in Chinese with English abstract)
Acknowledgement
The authors would like to thank the anonymous reviewers for their constructive comments and suggestions.
Author information
Authors and Affiliations
Corresponding author
Additional information
Supported by the National Key R&D Programs of China (Nos. 2017YFB0504201, 2015BAJ02B), the National Natural Science Foundation of China (Nos. 61473286, 61375002), and the Natural Science Foundation of Hainan Province (No. 20164178)
Rights and permissions
About this article
Cite this article
Zhang, X., Liu, Y. & Chen, Y. Development of a location-weighted landscape contrast index based on the minimum hydrological response unit. J. Ocean. Limnol. 36, 1236–1243 (2018). https://doi.org/10.1007/s00343-018-7069-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00343-018-7069-x