Change detection and classification of land cover at Hustai National Park in Mongolia

doi:10.1016/j.jag.2009.03.004

International Journal of Applied Earth Observation and Geoinformation

Volume 11, Issue 4, August 2009, Pages 273-280

https://doi.org/10.1016/j.jag.2009.03.004 Get rights and content

Abstract

Land cover types of Hustai National Park (HNP) in Mongolia, a hotspot area with rare species, were classified and their temporal changes were evaluated using Landsat MSS TM/ETM data between 1994 and 2000. Maximum-likelihood classification analysis showed an overall accuracy of 88.0% and 85.0% for the 1994 and 2000 images, respectively. Kappa coefficients associated with the classification were resulted to 0.85 for 1994 and 0.82 for 2000 image. Land cover types revealed significant temporal changes in the classification maps between 1994 and 2000. The area has increased considerably by 166.5 km² for mountain steppe and by 12 km² for a sand dune. By contrast, agricultural areas and degraded areas affected by human being activity were decreased by 46.1 km² and 194.8 km² over the 6-year span, respectively. These areas were replaced by mountain steppe area. Specifically, forest area was noticeably fragmented, accompanied by the decrease of ∼400 ha. The forest area revealed a pattern with systematic gain and loss associated with the specific phenomenon called as 'forest free-south slope’. We discussed the potential environmental conditions responsible for the systematic pattern and addressed other biological impacts by outbreaks of forest pests and ungulates.

Introduction

Mongolia is one of the largest countries in the circumpolar boreal zone. It is located at the southernmost fringe of the Siberian taiga and the northernmost Central Asian deserts, including vast steppes, bordering the Russian Federation in the north and China in the south. Hustai National Park (HNP) is famous for the success story of the reintroduction and establishment of a viable population of the Przewalski horses (van Dierendonck and de Vries, 1996, King and Gurnell, 2007). In 1992, Mt. Hustai was chosen as one of the most suitable areas for the reintroduction and establishment of a free-roaming population of Przewalski horses (Equus ferus przewalskii) in Mongolia (FPPPH, 2004).

In study area, several research projects have been performed to study the vegetation, plant species composition, and the effect of ungulates and forest pest species in connection with environmental changes (Wallis de Vries et al., 1996, Tsogtbaatar et al., 2003, Usukhjargal, 2006). However, all of these studies depend on the data obtained from several field surveys. Those surveys are of importance, but are limited in terms of the synoptic view of land cover. Instead, satellite data can make simultaneous, synoptic, and repetitive observations, which could enable us to understand the synoptic temporal change in land cover types over the study area. We also tried to conduct field surveys and collect ground truth data in accordance with the satellite data.

This paper aims to investigate spatial and temporal land cover changes in HNP and understand the possible causes of the changes. Our study is a case study of land cover changes, therefore we have employed popular and widespread methods such as maximum-likelihood classification, accuracy assessment, and change detection (Morgan and Morris-Jones, 1983, John and Xiuping, 1999, Jensen, 2000, Foody, 2002, Skidmore, 2002, Hagner and Reese, 2007, and many others).

To do this, we applied land classification schemes to classify the land cover types using high resolution satellite data for the first time in this region. Based on the previously developed methodology such as maximum-likelihood classification and change detection techniques, we assessed the accuracy of the land classification techniques by comparison with supervised classification based on numerous ground truth data and training field data. Land cover changes between 1994 and 2000 were quantitatively presented with the results of accuracy assessments. We also suggested potential processes for the landscape changes in HNP from a forested area to shrubland or grassland. Environmental factors affecting the land cover changes such as ungulates, insects and human activities were also considered.

Section snippets

Study area

The study area, Hustai National Park (HNP) as indicated in Fig. 1, is located at the Daurian forest steppe eco-region (about 100 km southwest of the capital city Ulaanbaatar), which is one of the undisturbed areas of the steppe ecosystem in temperate Eurasia (Hilbig, 1995, Gunin et al., 1999). Hustain mountain is situated at the southern boundary of the discontinuous permafrost (Sharkhuu, 2003, Ishikawa et al., 2005). At present, HNP is approximately 350,000 ha in area (including the buffer zone)

Satellite and digital elevation data

In order to investigate long-term variation in land cover type in the study area over several years, we selected the two representative years of 1994 and 2000. The month selected was September, since the vegetation reaches a maximum at that time of year and provides us with an opportunity to accurately discriminate between land cover types. For better spatial resolution of the land cover, a Landsat 5 TM image taken in September 1994 and a Landsat 7 ETM+ image taken in September 2000 were

Methods

The maximum-likelihood classifier (MLC) has become popular and widespread in remote sensing because of its robustness (Strahler, 1980, Conese and Maselli, 1992, Ediriwickrema and Khorram, 1997, Zheng et al., 2005). Maximum-likelihood classifier assumes that the each class in each band can be described by a normal distribution. Each pixel is assigned to the class that has the highest probability. The probability p(ω_j|x) gives the likelihood that the correct class is ω_i for a pixel at position x.

Vegetation and environmental correlation

DCCA was performed with CANOCO 4.02 (Braak, 1988) and with transformed Braun–Blanquet scales (Kent and Coker, 1992). Transformation of the Braun–Blanquet scales was conducted as follows: cover type 1 for 1–5%, 2 for 6–25%, 3 for 26–50%, 4 for 51–75%, and 5 for 76–100%. A total number of 780 vegetation releves of samples and environmental variables were included in the DCCA. An environmental data matrix of eight factors was also made. The DCCA factors in all species and uses a detrending model

Summary and conclusion

In this study we used remote sensing classification and change detection methods to investigate land covers and their temporal changes in HNP. Classification analysis showed that the accuracy of MLC with DCCA in 1994 and 2000 was 88.0% and 87.4%, respectively. In DCCA, the relationships between vegetation and environment are important to integrated land cover classification.

The ecological characteristics of dominant plant species of the mountain steppe area were defined by their preference for

Acknowledgements

This research was supported by the Brain Korea 21 Project through the School of Earth and Environmental Science, SNU. We would like to thank HNP's administration for sponsoring field surveys. This study was also supported by the Korea Meteorological Administration Research and Development program under Grant CATER 2008-4210. We thank two anonymous reviewers for their invaluable comments and useful suggestions.

References (36)

C. Conese et al.
Use of error matrices to improve area estimates with maximum likelihood classification procedures
Remote Sensing of Environment
(1992)
R.G. Congalton
A review of assessing the accuracy of classifications of remotely sensed data
Remote Sensing of Environment
(1991)
G.M. Foody
Status of land cover classification accuracy assessment
Remote Sensing of Environment
(2002)
O. Hagner et al.
A method for calibrated maximum likelihood classification of forest types
Remote Sensing of Environment
(2007)
C. Liu et al.
Comparative assessment of the measures of thematic classification accuracy
Remote Sensing of Environment
(2007)
S.V. Stehman et al.
Design and analysis for thematic map accuracy assessment: fundamental principles
International Journal of Remote Sensing
(1998)
A.H. Strahler
The use of prior probabilities in maximum likelihood classification of remotely sensed data
Remote Sensing of Environment
(1980)
P.A.J. van Oort
Interpreting the change detection error matrix
Remote Sensing of Environment
(2007)
C.J.F.T. Braak
Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis
Ecology
(1986)
C.J.F.T. Braak
CANOCO—an extension of DECORANA to analyze species-environment relationships
Plant Ecology
(1988)

J. Boehner et al.

Environmental change modelling for Central and High Asia: pleistocene, present and future scenarios

Boreas

(2005)

J. Cermak et al.

Isolated mountain forests in Central Asian Deserts: a case study from the Govi Altay Mongolia

Mountain Ecosystems

(2005)

J. Cohen

A Coefficient of agreement for nominal scales

Educational and Psychological Measurement

(1960)

J. Cohen

Weighted kappa: nominal scale agreement provision for scaled disagreement or partial credit

Psychological Bulletin

(1968)

C. Dulamsuren et al.

Ground vegetation in the Mongolian taiga forest-steppe ecotone does not offer evidence for the human origin of grasslands

Applied Vegetation Science

(2005)

J. Ediriwickrema et al.

Hierarchical maximum-likelihood classification for improved accuracies

Geoscience and Remote Sensing, IEEE Transactions

(1997)

FPPPH, 2004. Foundation for the Preservation and Protection of the Przewalski Horse....

P.D. Gunin et al.

Vegetation dynamics of Mongolia

(1999)

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Change detection and classification of land cover at Hustai National Park in Mongolia

Abstract

Introduction

Section snippets

Study area

Satellite and digital elevation data

Methods

Vegetation and environmental correlation

Summary and conclusion

Acknowledgements

Remote Sensing of Environment

Remote Sensing of Environment

Remote Sensing of Environment

Remote Sensing of Environment

Remote Sensing of Environment

International Journal of Remote Sensing

Remote Sensing of Environment

Remote Sensing of Environment

Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis

Ecology

CANOCO—an extension of DECORANA to analyze species-environment relationships

Plant Ecology

Environmental change modelling for Central and High Asia: pleistocene, present and future scenarios

Boreas

Isolated mountain forests in Central Asian Deserts: a case study from the Govi Altay Mongolia

Mountain Ecosystems

A Coefficient of agreement for nominal scales

Educational and Psychological Measurement

Weighted kappa: nominal scale agreement provision for scaled disagreement or partial credit

Psychological Bulletin

Ground vegetation in the Mongolian taiga forest-steppe ecotone does not offer evidence for the human origin of grasslands

Applied Vegetation Science

Hierarchical maximum-likelihood classification for improved accuracies

Geoscience and Remote Sensing, IEEE Transactions

Vegetation dynamics of Mongolia