Recognizing the Relationship between Spatial Patterns in Water Quality and Land-Use/Cover Types: A Case Study of the Jinghe Oasis in Xinjiang, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Acquisition and Processing
- (1)
- As the data sources, this study applied GF-1 remote sensing images obtained in May and October 2015 (see http://www.cresda.com/CN/). These images were not influenced by clouds, fog, or snow cover, and their quality was good. It conducted radiation and orthographic corrections for the remote sensing image data combined with 1:50,000 scale digital elevation model (DEM) data. We established five land-use/cover types by using the Environment for Visualizing Images software (ENVI Version 5.0), namely: farmland, forest–grassland, water body, salinized land, and others, based on the actual conditions of the research zone. Finally, we generated a vector data map of the land-use/cover types for two stages of the research zones.
- (2)
- In total, this study collected 47 water sampling points, with 23 points collected in May 2015 and 24 points collected in October 2015. The samples were taken in a wide range of hydrological environments in May and October, which included the Kuitun and Jing River (P1, P2, P3, P4, P5, P6, P11, P13 (just in October), P14, P15, P16, P17, P18, P19, P20), the ditches of these rivers (P9, P10), and Ebinur Lake (P7, P8, P21, P22, P23, P24, P25). The surrounding land-use/cover types of the inflowing rivers contained farmland, forest–grassland and others, while that of the ditches contained salinized land and farmland, as well as others. Thus, these sampling points were collected mainly out of concern for the effects of different land-use/cover types on the surrounding surface water. Besides, the river water flows into Ebinur Lake and affects its water quality. It is not certain whether the inflowing rivers and surrounding land-use/cover types affect the water quality of the lake. Thus, it is necessary to take water from the inflowing rivers and its ditches as well as from Ebinur Lake itself. The information about the land-use/cover types within the one-km buffer zone of the water quality sampling points in the research area was obtained. The sampling points from the inflowing rivers were used to test the monitoring indices, including: chemical oxygen demand (COD), five-day biological oxygen demand (BOD5), suspended solids (SS), total phosphorus (TP), total nitrogen (TN), ammonia nitrogen (NH3-N), chromaticity (SD), and turbidity (NUT). The pillar industries in the Jinghe Oasis include salt production and Artemia breeding. Non-heavy industry is present; thus, point source pollution from industrial wastewater was not considered in the research zone. All of the polyethylene bottles were used to store the samples. The bottles were cleaned, dried, and sealed with deionized water before sampling. The samples were taken to the laboratory for measurements and analyses after collection. We applied dichromate titration, dilution, inoculation, gravimetry, ammonium molybdate spectrophotometry, alkaline potassium persulfate decomposition UV spectrophotometry, and Nessler’s reagent spectrophotometry to measure the COD, BOD5, SS, TP, TN, and NH3-N, respectively. The analyses of all of the samples were entrusted to and completed by Urumqi Jincheng Measurement Technology Co., Ltd. (Urumqi, China). Research samples were collected from agricultural land in Jinghe County and Tuotuo Village, which surround Ebinur Lake; a national ecological zone in Ebinur Lake called Bird Isle; and the Ganjia Lake Haloxylon natural conservation area.
2.3. Recognition of Water Quality Spatial Characteristics Based on the SOM Method with Non-Hierarchical K-Means Classification
2.4. Spatial Analysis of the Influences of Land Use/Cover Change on Water Quality
3. Results and Analysis
3.1. Spatial Framework of Water Quality in the Jinghe Oasis
3.2. Analysis of Land-Use/Cover Type and Its Relation to Water Quality at Different Layers
3.3. Analysis of Land-Use/Cover Changes in the Jinghe Oasis and their Correlation with Water Quality at Different Seasons
4. Discussion
5. Conclusions
- (1)
- Based on non-hierarchical k-means classification, 47 water quality sampling points were divided into six clusters using the SOM method, and the time sequence characteristics of the research zone were better recognized in the classification results. Clusters 1 to 3 comprised samples from the wet season (May 2015), whereas Clusters 4 to 6 comprised monitoring samples from the dry season (October 2015). In general, the COD, SS, NUT, TN, and NH3-N contents were high. The SD value was high in Clusters 1, 4, and 6. In addition, high BOD and TP values were mainly concentrated in Clusters 4 and 6. Based on these results, the water quality at different clusters of the research zone was further evaluated. The results show that Clusters 1 to 6 do not satisfy potable water quality standards.
- (2)
- The correlations between the land-use/cover types and water quality parameters for Clusters 1 to 6 were analyzed, according to the hierarchical results of the water quality parameters. The comprehensive analysis indicates that the farmland, forest–grassland, and salinized land exerted significant influences on the water quality parameters of the Jinghe Oasis. In Clusters 1, 2, and 6, the size of the water area, to a certain extent, also influenced changes in the water quality parameters.
- (3)
- During the wet and dry seasons, the influences that various land-use/cover types in the research zone had exhibited the following descending order of influence: farmland → forest–grassland → salinized land → water body → others, on the water quality parameters. Moreover, the influences were lower during the wet season than during the dry season.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Water Quality Parameters | Cluster 1 | Cluster 2 | Cluster 4 | Cluster 5 | Cluster 6 |
---|---|---|---|---|---|
COD | Exceed V | Exceed V | I | IV | V |
BOD5 | I | I | IV | I | IV |
TN | IV | IV | III | III | III |
NH3-N | II | II | I | I | I |
TP | II | II | II | III | II |
Time | LULC | Water Body | Saline Land | Farmland | Forest Grassland | Other Land | Total | User’s Accuracy (%) |
---|---|---|---|---|---|---|---|---|
May | Water body | 144 | 0 | 0 | 0 | 0 | 144 | 100 |
Saline land | 0 | 77 | 0 | 0 | 16 | 93 | 82.79 | |
Farmland | 0 | 36 | 101 | 0 | 0 | 137 | 73.72 | |
Forest–grassland | 0 | 36 | 0 | 101 | 0 | 137 | 73.72 | |
Other land types | 1 | 0 | 0 | 0 | 87 | 88 | 98.96 | |
Total | 145 | 149 | 101 | 101 | 103 | Overall = 89.9750% | ||
Producer’s accuracy (%) | 99.31 | 51.67 | 100 | 100 | 84.46 | Kappa = 0.8681 | ||
October | Water body | 144 | 0 | 0 | 0 | 0 | 144 | 100 |
Saline land | 0 | 57 | 0 | 0 | 26 | 83 | 86.67 | |
Farmland | 0 | 16 | 101 | 0 | 0 | 117 | 86.32 | |
Forest–grassland | 4 | 16 | 0 | 101 | 0 | 117 | 86.32 | |
Other land types | 0 | 0 | 0 | 0 | 77 | 77 | 100 | |
Total | 148 | 89 | 101 | 101 | 103 | Overall = 86.2848% | ||
Producer’s accuracy (%) | 97.29 | 64 | 100 | 100 | 74.75 | Kappa = 0.8184 |
Title | Parameters | Farmland | Forest–Grassland | Water Body | Salinized Land | Others |
---|---|---|---|---|---|---|
Cluster 1 | COD | −0.161 | 0.240 | 0.986 * | −0.110 | −0.361 |
BOD5 | 0.074 | 0.492 | −0.439 | −0.613 | 0.552 | |
SS | −0.271 | −0.710 ** | 0.801 | 0.619 | −0.384 | |
TP | −0.195 | 0.453 | 0.371 | 0.444 | 0.623 | |
TN | 0.464 | 0.524 | −0.721 * | −0.224 | 0.121 | |
NH3-N | −0.491 | 0.039 | 0.071 | −0.066 | 0.291 | |
SD | −0.296 | 0.448 | 0.415 | −0.426 | 0.396 | |
NUT | −0.261 | −0.724 ** | 0.550 | 0.756 ** | −0.612 | |
Cluster 2 | COD | −0.581 * | 0.613 * | 0.916 | −0.693 ** | 0.442 |
BOD5 | −0.004 | 0.455 | 0.055 | −0.545 | 0.242 | |
SS | 0.493 | −0.512 | −0.983 ** | 0.386 | 0.047 | |
TP | −0.222 | 0.531 | 0.850 | −0.129 | 0.382 | |
TN | 0.351 | 0.415 | −0.867 | −0.356 | −0.311 | |
NH3-N | −0.467 | 0.121 | 0.122 | −0.269 | 0.284 | |
SD | −0.226 | −0.073 | −0.051 | −0.217 | 0.473 | |
NUT | 0.639 * | −0.446 | −0.990 ** | 0.513 | −0.236 | |
Cluster 4 | COD | −0.652 * | 0.484 | / | 0.375 | −0.048 |
BOD5 | −0.482 | −0.402 | / | 0.505 | 0.688 | |
SS | −0.155 | 0.658 | / | −0.179 | −0.167 | |
TP | 0.872 ** | −0.398 | / | −0.791 * | 0.868 * | |
TN | 0.336 | 0.468 | / | −0.571 | −0.124 | |
NH3-N | −0.202 | −0.540 | / | 0.398 | 0.352 | |
SD | −0.543 | 0.825 * | / | 0.214 | −0.549 | |
NUT | 0.578 | 0.469 | / | −0.819 * | −0.129 | |
Cluster 5 | COD | 0.094 | −0.372 | / | 0.325 | −0.400 |
BOD5 | −0.881 ** | 0.503 | / | 0.774 * | −0.044 | |
SS | 0.621 | −0.533 | / | −0.284 | −0.380 | |
TP | 0.587 | −0.702 | / | −0.565 | 0.136 | |
TN | 0.735 | −0.588 | / | −0.184 | −0.604 | |
NH3-N | −0.675 | 0.108 | / | 0.487 | 0.308 | |
SD | −0.632 | 0.330 | / | 0.208 | 0.576 | |
NUT | 0.311 | 0.076 | / | −0.459 | 0.154 | |
Cluster 6 | COD | 0.489 | 0.401 | 0.980 * | −0.454 | 0.289 |
BOD5 | −0.256 | −0.884 ** | −0.660 | 0.367 | 0.327 | |
SS | −0.481 | 0.194 | 0.341 | −0.150 | −0.062 | |
TP | −0.545 | −0.656 | 0.269 | −0.060 | 0.206 | |
TN | −0.158 | −0.364 | −0.022 | 0.516 | 0.313 | |
NH3-N | −0.553 | −0.366 | 0.517 | −0.090 | 0.603 | |
SD | 0.811 | −0.037 | −0.857 | 0.249 | −0.282 | |
NUT | 0.450 | 0.165 | 0.764 | −0.497 | 0.636 |
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Zhang, F.; Wang, J.; Wang, X. Recognizing the Relationship between Spatial Patterns in Water Quality and Land-Use/Cover Types: A Case Study of the Jinghe Oasis in Xinjiang, China. Water 2018, 10, 646. https://doi.org/10.3390/w10050646
Zhang F, Wang J, Wang X. Recognizing the Relationship between Spatial Patterns in Water Quality and Land-Use/Cover Types: A Case Study of the Jinghe Oasis in Xinjiang, China. Water. 2018; 10(5):646. https://doi.org/10.3390/w10050646
Chicago/Turabian StyleZhang, Fei, Juan Wang, and Xiaoping Wang. 2018. "Recognizing the Relationship between Spatial Patterns in Water Quality and Land-Use/Cover Types: A Case Study of the Jinghe Oasis in Xinjiang, China" Water 10, no. 5: 646. https://doi.org/10.3390/w10050646
APA StyleZhang, F., Wang, J., & Wang, X. (2018). Recognizing the Relationship between Spatial Patterns in Water Quality and Land-Use/Cover Types: A Case Study of the Jinghe Oasis in Xinjiang, China. Water, 10(5), 646. https://doi.org/10.3390/w10050646