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- Taylor & Francis
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- © 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
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- 2474-9508
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- 10.1080/24749508.2018.1481657
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Abstract
GEOLOGY, ECOLOGY, AND LANDSCAPES 2019, VOL. 3, NO. 1, 37–45 INWASCON https://doi.org/10.1080/24749508.2018.1481657 RESEARCH ARTICLE Land use change and classification in Chaohu Lake catchment from multi-temporal remotely sensed images T. D. T. Oyedotun Department of Geography, Faculty of Earth and Environmental Sciences (FEES), Leslie Cummins Building, University of Guyana, Guyana, Southern America ABSTRACT ARTICLE HISTORY Received 22 March 2018 Chaohu lake and its surrounding have received considerable attention in terms of water Accepted 22 May 2018 and sediment pollution investigations. In this study, the trend of changes in four key land uses in the catchment area was examined. Maximum Likelihood Supervised Classification KEYWORDS (MLC) in ESRI© ArcGIS was applied to subsets of Landsat MSS, TM, ETM and OLI/TIRS Land use and land cover images of 1979 to 2015. The results showed that the water bodies’ position remained (LULC); change detection; relatively stable, while the portions of land used for urban activities and agriculture image classification; chaohu; consistently increased. The built-up areas increased from 3.5% in 1979 to 25.1% in 2015. accuracy assessment Similarly, the agricultural land use increased from the area coverage of 29.8% in 1979 to 45.2% in 2015. Forested/vegetated land of the catchment substantially decreased during this period, from 59.8% in 1979 to 22.9% in 2015. Decades of improper land use activities and resource utilisation have led to ecological degradation of the basin which has manifested in serious eutrophication of the lake. The conflict between the economic development and the need to protect the ecosystem of Chaohu catchment has now become a management issue. Deliberate planning should be made to reduce the rate of conversion of the forested land area to agriculture and urban use. 1. Introduction (Rawat & Kumar, 2015), the understanding of which are essential in environmental management Land use and land cover (LULC) changes are con- and wider decision making. sidered important parameters in measuring or asses- Change in LULC is not static. It is a dynamic and sing global changes at different spatial and temporal continuous processes (Mondal, Sharma, Garg, & scales (Islam, Jashimuddin, Nath, & Nath, 2017). Kappas, 2016). Continuous observation of these These changes are mostly driven by anthropogenic changes is highly essential in overall environmental activities and, on many occasions, these changes and ecosystem services monitoring (e.g. Lal & directly impact both human and the natural environ- Anouncia, 2015). With the advent of remote sensing ment (Ruiz-Luna & Berlanga-Robles, 2003). Impacts and Geographical Information System (GIS) technol- of changes in LULC on both human and natural ogies, the monitoring and detection of LULC changes environments are diverse. For example, the changes are now made possible at low cost and with better influence the composition of the atmosphere and the accuracy (Lo & Choi, 2004). Remotely sensed data, exchanges of energies and materials (e.g. Kindu, e.g. Landsat, provides valuable and continuous Schneider, Teketay, & Knoke et al., 2013), causing records of the earth’s landscape for the past four biological diversities and contributing to vegetation decades, approximately. The archive of these data reduction (e.g. Zeng, Wu, Zhan, & Zhang, 2008), soil (Landsat series, especially) is now made freely avail- erosion (e.g. Zha et al., 2015), ecosystem services able to scientific community, and thus serves as a alteration (e.g. Mas et al., 2004), and un-sustainability repertoire of information of identifying or monitor- of socio-economic activities and natural resources ing the changes imposed on physical and human (Vescovi, Park, & Vlek, 2002). environment (Islam et al., 2017). With notable global and local population growth, Lake Chaohu and its basin have been the subject of increasing pressures are daily exerted on limited nat- different studies in the last few decades because of its ural environment and thereby contributing to importance as the area of ancient civilisation and changes in land use and land cover of the environ- current diverse human activities (Li et al., 2009;Wu ment (Islam et al., 2017). Monitoring land use/land et al., 2010). Examples of recent studies on this vital cover changes is very important in the understanding basin include: investigation of the influence of land- of landscape dynamics at, or between, a period CONTACT T. D. T. Oyedotun oyedotuntim@yahoo.com Department of Geography, Faculty of Earth and Environmental Sciences (FEES), Leslie Cummins Building, University of Guyana, Turkeyen Campus, Greater Georgetown Guyana, Southern America. © 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 38 T. D. T. OYEDOTUN use changes on absorbed nitrogen and phosphorus 32.6°C (17.3°C in average) (Chen, Yang, Dong, & Liu, loadings in the basin (Jiang et al., 2014); examination 2013; Jiang et al., 2014; Tang et al., 2015; Xue et al., of seasonal variation in the microcystin concentration 2017). There are eight major inflowing rivers to Lake in the lake (Yang et al., 2006); phosphorus distribu- Chaohu and one outflowing river. These inflowing tion and heavy metal pollution in the sediments of rivers account for over 80% of the total runoff volume the lake (Zan et al., 2011); assessments of heavy from the catchment area to feed the lake and there is metals in sediment of the urban river systems within only one outlet (Xue et al., 2017). The inflow rivers the lake (Shao et al., 2016); and so on. The conversion are Zhegao, Nanfei, Shiwuli, Pai, Fengle, Hangbu, of land cover to various land uses are threatening the Baishitian and Zhao Rivers while the only outflow is fragile ecosystems within the basin. In this study, Yuxi River, which is the only channel linking the lake 1979 is considered as the base year to examine the to the Yangtze River (Figure 1). In the 1950s, the trend of changes in key LULC for over 30 years and urban population of Lake Chaohu Basin was ca the impact of this dynamics on the lake parameters. 200,000; however, increasing immigration in the pro- vince has led to increase in the population to ca 1.6 million people in 1985; ca. 8 million in 2009 and 2. Study area currently ca 9.1 million (Jiang et al., 2014; Xue et al., 2017). With changes in human population are Lake Chaohu in Anhui Province is the fifth largest changes in land use pattern, and the associated freshwater lake in China. It is a shallow eutrophic effects. lake located in the lower Yangtze basin with a surface area of 770 km at 31°25′–31°43′N, 117°17′–117°51′E (Figure 1) and a mean depth of approximately 3.0m 3. Methods (Jiang et al., 2014; Tang et al., 2015; Xue, Zhang, 3.1 Data and data preparation Duan, & Ma, 2017). This catchment area, 13,350 km , is characterised by an intermediate of Landsat Satellite data: Multi-Spectral Scanner (MSS), subtropical to warm temperate monsoon climate Thematic Mapper (TM), Enhanced Thematic Mapper with an annual mean temperature of 15°C–16.1°C, (ETM) and Operational Land Imager (OLI)— an annual mean precipitation of 1100mm, and a sur- Thermal Infrared Sensor (TIRS) for the year 1979, face water temperature that varies between 5.2 and 1984, 1990, 1995, 2000, 2005, 2010 and 2015 Figure 1. The Study Area (Inset: Location of Lake Chaohu in China). Source: The data for the map were extracted from the DIVA- GIS database (http://www.diva-gis.org/), ©1995-1998, LizardTech, Inc. GEOLOGY, ECOLOGY, AND LANDSCAPES 39 Table 2. Classes delineated based on supervised classification. respectively (Table 1), sourced from the US S/ Geological Survey (USGS) depository (https://earthex No Class Description plorer.usgs.gov) were used for land use/cover classi- 1 Water River, open/surface water, lakes, ponds, fication. These imageries were registered in the same streams, reservoirs 2 Built Up Areas/ Residential industrial, commercial or mixed up projection, the Universal Transverse Mercator Settlements areas—including networks of roads, (UTM) projection World Geodetic System (WGS) transportation 3. Vegetation Mixed and uncultivated forest areas 1984 Zone 50N and the Spectral Bands Green (0.5– 4 Agriculture/ Organised or unorganised agricultural 0.6 µm), Red (0.6–0.7 µm) and Near Infrared (0.8– Agricultural practices, crops fields and fallow lands, etc. Land 1.1 µm) as composite images were used for the clas- sification. Landsat images are known to be very vital in the classification of different landscape categories “minimal confusion” from the land covers being and components at larger scale (Butt, Shabbir, mapped (Butt et al., 2015). Ahmad, & Aziz, 2015). 3.3 Accuracy assessment 3.2 Image classification The generated classified land use maps were verified In LULC, image classification is the mostly used con- using the Google Earth images of December 1984 (for ventional land use change observation and detection 1979, being the oldest historical images in Google Earth method because of its ability to create series of land for the area) and those closest to the dates of Landsat cover maps (El Garouani, Mulla, El Garouani, & Image acquisition (Table 1) respectively. An accuracy Knight, 2017). The Maximum Likelihood assessment for the classified images was done in Classification (MLC) supervised method in ArcGIS ArcGIS 10.2. From the classifier, several reference points was applied to classify the series of Landsat bands for were generated randomly for each supervised image the 35 years under study, after the images have been using the nearest Google Earth map for each of the pre-processed, geo-referenced, mosaicking and sub- image year as Ground Truth value, respectively. The setting to the Area of Interest (AoI). MLC is based on randomly generated points were identified and assigned the likelihood of a pixel (Picture element) belonging to different classes as Ground Truth values based on the a class, with the assumption (in theory) that this like- Google Earth maps. The correctly identified points in lihood is equal for all classes, that the input bands are Google Earth Maps were considered as the classified evenly and uniformly distributed (El Garouani et al., values used for Confusion/Error Matrix and Kappa sta- 2017). The steps taken for image classification involved tistics generated in ArcGIS and processed in Microsoft the creation of training samples, as polygons, for each Excel spreadsheet. of the observed four (4) classes from the colour com- The confusion (error) matrix analysed for accuracy posite images through the on-screen digitisation of the assessment in this study makes it easier to visualise the thematic classes. The delineated classes were water, performance of the accuracy assessment. Here, Users’ built-up/settlement areas, vegetation and agriculture/ Accuracy, Producers’ Accuracy, Overall Accuracy and agricultural lands (Table 2). The training sample poly- Kappa Co-efficient, arising from error matrix analyses, gons, after delimitation around the representative of arereported. Theoverall accuracy forthe imageclassifi- respective land cover types, were derived from each of cation was obtained by dividing the sum of the entries in the imageries (from 1979 to 2015), saved as separate the “from-to” agreement of the confusion matrix with signature files (.sig extension) and applied on the the total number of the examined pixels in the classifica- images for the supervised classification in ArcGIS’s tion (Islam et al., 2017). Kappa co-efficient, a nonpara- MLC. This approach has been the most widely used metric statistic which describes the relationship between in LULC change assessment with greater acceptable categories of variables not only for the diagonal elements accuracies (e.g. Butt et al., 2015; El Garouani et al., (Rossiter, 2014)but also forall theelementsinthecon- 2017; Islam et al., 2017; etc.). It works on the basis fusion matrix (Butt et al., 2015), was also calculated to that a satisfactory spectral significance is the one with assess the level of agreement among the data values generated in the image classification, using the Kappa Table 1. Specification of the Landsat data analysed. co-efficient equation of Afify (2011:190). Satellite Sensor Spatial Resolution (m) Acquisition Date Landsat – 3 MSS 79 06/08/1979 Landsat – 5 MSS 30 03/08/1984 4. Results and discussion Landsat – 5 TM 30 25/12/1990 Landsat – 5 TM 30 07/12/1995 4.1 Land use change analysis Landsat – 7 ETM 30 16/09/2000 Landsat – 7 ETM 30 08/10/2005 The classified land use maps of Chaohu Lake basin Landsat – 7 ETM 30 19/03/2010 Landsat – 8 OLI-TIRS 30 25/10/2015 from the year 1979 to 2015 are presented in Figure 2. 40 T. D. T. OYEDOTUN The figure presents the aerial distribution of the land other uses within the basin. In 1979, the uncultivated use categories for the years under consideration, at vegetated/forested areas of the basin stood at 59.8% but approximate five-year interval. A total of four classes this was decreased to 34.1% in 1984, then a slight were produced for each of the images (water, built-up increase of 43.8% in 1990 before resumption of the areas/settlements, vegetation and agriculture/agricul- downward trend in 1995 at 21.3%, in 2000 at 26.6%, tural land). The outputs for classification of images in 2005 at 21.3%, in 2010 at 22.1% and 22.9% in 2015 were compared in terms of the total area for each respectively (Figure 3). Water system (Riverine classes) land use category. As presented in Table 3 and remained relatively constant through the temporal per- Figures 2 and 3, the land use classes that has consis- iod considered here. tently increased are built-up areas and agricultural land Overall, uncultivated vegetation land shrank consid- respectively, at the detriment of vegetated (uncultivated erably at 36.88% between 1979 and 2015 while there was land areas). Of all the land use categories, built-up areas increase of 15.45% (representing 2062.58 km of the (see Table 2 for definitions) increased from 3.5% of the basin) in the areas occupied by land cultivated for agri- 2 2 total area of the basin (area of 477.15 km ) to ~6.6% in cultural purpose and 21.69% (representing 2895.62 km 1984, 7.5% in 1990, ~7.0% in 1995, 18.9% in 2000, of the basin) developed for settlements and other urban 24.1% in 2005, 24.9% in 2010 and 25.1% in 2015 land uses. The increasing trend of these two classes of respectively (Table 3, Figure 3). This is followed by land use within this watershed basin is an indication that land used for agricultural purposes, with a total cover- economic advancement (Figure 4) in, and probable age area of 29.8% (area of 3974.58 km ) in 1979 to population migration (Figure 5)to, theareahave 52.4% in 1984, reduced to 42.3% in 1990 before increas- accounted for the induced anthropogenic influence on ing to 65.3% in 1995, 47.6% in 2000, 48% in 2005, the land use in the basin. Encroaching into vegetated 46.1% in 2010 and 45.2% in 2015 respectively land by agriculture practices and the need for urban (Figure 3). The substantial increase of these two cate- expansion are some of the driving force for land use gories of land use was made possible by the probable changes in watershed areas (Butt et al., 2015). conversion of forested/highly vegetated areas for these Development and construction of new housing schemes, Figure 2. Classified maps of Lake Chaohu basin from 1979 to 2015. (Images are classified from Landsat Satellite data sourced from the US Geological Survey (USGS) depository (https://earthexplorer.usgs.gov.) The shapefile used for area extraction was from the DIVA-GIS database (http://www.diva-gis.org/) ©1995-1998, LizardTech, Inc.”) GEOLOGY, ECOLOGY, AND LANDSCAPES 41 Table 3. Land-cover classes and area represented by each class in square kilometres. Area in Square km (km ) Land cover classes 1979 1984 1990 1995 2000 2005 2010 2015 Water 915.07 927.44 845.61 857.81 897.84 893.44 881.27 890.46 Built-up Areas 477.15 874.82 1002.18 930.63 2533.41 3210.92 3335.25 3362.97 Vegetation 7983.21 4553.34 5850.52 2845.51 3556.59 2846.19 2951.15 3058.78 Agriculture 3974.58 6994.40 5651.68 8716.04 6362.16 6399.44 6182.34 6037.79 Catchment Area 13,350 13,350 13,350 13,350 13,350 13,350 13,350 13,350 Figure 3. Yearly percentages of four land use cover at Lake Chaohu Basin. farmhouses, roads, bridges, pavements, recreational facil- 4.2 Overall accuracy and Kappa (k^) statistics for ities and other structures are the main reasons which 1979 to 2015 supervised classification caused the land area classified as built-up areas in the For the supervised classification of 1979 to 2015 images to have increased from 477.15 km in 1979 to images, the overall accuracies were 87% (1979), 3362.97km in 2015, an increase of 21.69%. 86% (1984), 86% (1990), 82% (1995), 85% (2000), Easy accessibility to the water in the basin (e.g. to 86% (2005), 82% (2010) and 81% (2015) respec- streams, rivers and the lake) for agricultural purposes tively while the Kappa statistics were 0.869 (1979), and the increasing demand for food for the growing 0.860 (1984), 0.866 (1990), 0.819 (1995), 0.851 population (Figure 5) have, directly and indirectly, (2000), 0.868 (2005), 0.825 (2010) and 0.831 caused the land being use for agricultural purposes (2015) for the images respectively (Table 4). to increase in the basin within the last four decades. Kappa statistics here measured the identified classi- However, this has led to the depletion of uncultivated fication of the remotely sensed images and the vegetated land and minor reduction of many streams ground truth referenced data, and this is used output and the dried up of few river tributaries. The here to check the accuracy of the classification riverine system within the basin was very stable dur- measured. A Kappa value of between 0.81 and ing the decades investigated in this study. Figure 4. Yearly gross domestic product for Hefei and Chaohu (the main cities within Lake Chaohu Basin). Data source: https:// www.ceicdata.com/en/china/gross-domestic-product 42 T. D. T. OYEDOTUN Figure 5. Yearly population at Hefei and Chaohu cities (the main cities at Lake Chaohu Basin). Data source: http://population. city/china/hefei/ http://population.city/china/chaohu/ 1.00 is an indication of almost perfect or perfect continuous utilisation of fertilisers for agricultural classification between the two measurements in the practises, the development and advancement of classification system (Islam et al., 2017). Thus, pro- industrial/urban areas are,also, evidence of increasing ducer’s accuracies and user’s accuracies for each of anthropogenic influences in the basin (Zan et al., the classes classified in this process as well as the 2011). overall accuracy and the Kappa value for the Although the central government through the images are satisfactory, suggesting that the land Ministry of Environmental Protection of People’s use classification in this analysis is good enough Republic of China and other local governments to detect the changing scenarios of Lake Chaohu have set up many pollution control measures for basin. the lake, Chaohu has remained one of the most PA*—Producer’s Accuracy; UC: User’s Accuracy. eutrophicated lakes in China (Wang et al., 2012) because of the continuous discharge of municipal and industrial wastewater, domestic wastewater, 4.3 Evidence and influence of land use change on agricultural fertilisers and soil erosion (Song, Wu, Lake Chaohu & Jin, 2008), which are direct results of increasing Evidence of consistent trend of growth in built-up trend of urbanisation in the basin. Widespread inci- areas (urbanisation) and agricultural intensification dence of linear alkylbenzenes (LABs) in sampled in Lake Chaohu basin for the past four decades sediments from Chaohu is an indication of the have significantly deteriorated the environmental domestic sewage from Hefei, the rapidly growing conditions of the lake and its watershed. The urban city within the Chaohu Lake catchment importance of the basin’s water resources to the basin (see Wang et al., 2012). 9.1 million inhabitants are evidenced on their In the early 1980s when eutrophication began to total and heavy dependence on the lake as the occur in Chaohu Lake, the heavy metals within the primary source of domestic water usage (Qin sediments and pore water were relatively low (Tu et al., 2013) and irrigation (Cui, Huang, Chen, & et al., 1990). However, with increasing and extensive Morse, 2009). The land use pattern in the basin has farming activities (post 1980s: (Figures (2,3)), changed considerably because of the increasing (Table 3)), many toxic metals in the sediments started development of agriculture, of diverse industries showing continuous increases within the Chaohu and increasing buildings for urban settlements Lake catchment in the last three decades (e.g. Wen, (Figure 2, Table 3). In addition, the basin has Shan, & Zhang, 2012; Zan et al., 2011) thereby been characterised with the conversion of vegetated severely contaminating both the sediments and pore land to urban and industrial uses (built-up areas) water (e.g. Huang et al., 2013; Qin et al., 2014). in the recent decades (Huang, Zhan, Yan, Wu, & Similarly, input from increasing industrial and Denga, 2013; Jiang et al., 2014). domestic wastewater arising from the increasing The ecological environment in Lake Chaohu built-up areas have resulted in high metal toxic con- Basins is very fragile. Decades of improper land use tents of the estuarine systems of Nanfei and Zhegao activities and resource utilisation have led to ecologi- Rivers and other parts of the Chaohu catchment cal degradation of the basin which are manifested in basin (Wen et al., 2012). Changing land use, climate serious eutrophication of the lake, resulting from and geology of Chaohu basin play important roles in increasing anthropogenic inputs from the lake’s the transportation of nutrients and sediments from watershed (Jiang et al., 2014; Wang, Zhang, & the basin areas to the network of streams/rivers in, Liang, 2012; Xue et al., 2017; Zan et al., 2012). The and eventually, to the Lake, causing increasing GEOLOGY, ECOLOGY, AND LANDSCAPES 43 Table 4. Accuracies and Kappa Statistics of the supervised land use classification of Lake Chaohu Basin. 1979 1984 1990 1995 2000 2005 2010 2015 Classes PA (%) UC (%) PA (%) UC (%) PA (%) UC (%) PA (%) UC (%) PA (%) UC (%) PA (%) UC (%) PA (%) UC (%) PA (%) UC (%) Water 92.98 100.00 55.61 100 80.70 100.00 59.65 100.00 31.58 100.00 52.63 100.00 38.60 100.00 95.45 79.25 Built-up Areas 57.69 93.75 56.15 93.75 100.00 93.75 100.00 93.75 96.15 93.75 42.31 93.75 100.08 93.75 84.15 71.88 Vegetation 92.93 80.00 59.59 80 87.88 80.00 70.20 80.00 77.37 80.00 77.58 80.00 73.33 80.00 75.00 93.91 Agriculture 84.62 87.30 60 87.3 100.15 87.30 100.92 87.30 66.15 87.30 50.77 87.30 95.38 87.30 82.35 75.68 Overall accuracy 0.870 0.861 0.867 0.821 0.854 0.868 0.827 0.814 Kappa Coefficient 0.869 0.860 0.866 0.819 0.851 0.8682 0.825 0.813 44 T. D. T. OYEDOTUN absorption of nutrients (e.g. nitrogen and phos- land use and land cover changes. Further investiga- phorus) in the catchment (e.g. Jiang et al., 2014). tion along this line is hereby recommended. The rapid economic development of the last three decades, with astronomical increase in Gross Disclosure statement Domestic Product (GDP) of Hefei and Chaohu cities (the two main cities within the basin, Figure 4) and No potential conflict of interest was reported by the author. strong population growth because of in-migration (Figure 5) are the two major evidences of anthropo- genic presence in Chaohu catchment. The anthropo- ORCID genic disturbances (such as built-up area sewages, T. D. T. Oyedotun http://orcid.org/0000-0002-3926- aquiculture, fertiliser usages, nutrients inputs, etc.) have imposed hydrological alterations and promoted the increase of eutrophic species in the lake ecosys- tem (e.g. Chen et al., 2013). Conflict between the References economic development and the need to protect the Afify, H. A. (2011). 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Journal
Geology Ecology and Landscapes – Taylor & Francis
Published: Jan 2, 2019
Keywords: Land use and land cover (LULC); change detection; image classification; chaohu; accuracy assessment