Kai Jia

Simulating urban land use change by incorporating an autologistic regression model into a CLUE-S model

The Conversion of Land Use and its Effects at Small regional extent (CLUE-S) model is a widely used method to simulate land use change. An ordinary logistic regression model was integrated into the CLUE-S model to identify explanatory variables without considering the spatial autocorrelation effect. Using image-derived maps of the Changsha-Zhuzhou-Xiangtan urban agglomeration, the CLUE-S model was integrated with the ordinary logistic regression and autologistic regression models in this paper to simulate land use change in 2000, 2005 and 2009 based on an observation map from 1995. Significant positive spatial autocorrelation was detected in residuals of ordinary logistic models. Some variables that were much more significant than they should be were selected. Autologistic regression models, which used autocovariate incorporation, were better able to identify driving factors. The Receiver Operating Characteristic Curve (ROC) values of autologistic regression models were larger than 0.8 and the pseudo R2 values were improved, compared with results of logistic regression model. By overlapping the observation maps, the Kappa values of the ordinary logistic regression model (OL)-CLUE-S and autologistic regression model (AL)-CLUE-S models were larger than 0.75. The results showed that the simulation results were indeed accurate. The Kappa fuzzy (Kfuzzy) values of the AL-CLUE-S models (0.780, 0.773, 0.606) were larger than the values of the OL-CLUE-S models (0.759, 0.760, 0.599) during the three periods. The AL-CLUE-S models performed better than the OL-CLUE-S models in the simulation of land use change. The results showed that it is reasonable to integrate autocovariates into CLUE-S models. However, the Kfuzzy values decreased with prolonged duration of simulation and the maximum range of time was not discussed in this paper.

Evaluating the Vegetation Recovery in the Damage Area of Wenchuan Earthquake Using MODIS Data

The catastrophic 8.0 Richter magnitude earthquake that occurred on 12 May 2008 in Wenchuan, China caused extensive damage to vegetation due to widespread landslides and debris flows. In the past five years, the Chinese government has implemented a series of measures to restore the vegetation in the severely afflicted area. How is the vegetation recovering? It is necessary and important to evaluate the vegetation recovery effect in earthquake-stricken areas. Based on MODIS NDVI data from 2005 to 2013, the vegetation damage area was extracted by the quantified threshold detection method. The vegetation recovery rate after five years following the earthquake was evaluated with respect to counties, altitude, fault zones, earthquake intensity, soil texture and vegetation types, and assessed over time. We have proposed a new method to obtain the threshold with vegetation damage quantitatively, and have concluded that: (1) The threshold with vegetation damage was 13.47%, and 62.09% of the field points were located in the extracted damaged area; (2) The total vegetation damage area was 475,688 ha, which accounts for 14.34% of the study area and was primarily distributed in the central fault zone, the southwest mountainous areas and along rivers in the Midwest region of the study area; (3) Vegetation recovery in the damaged area was better in the northeast regions of the study area, and in the western portion of the Wenchuan-Maoxian fracture; vegetation recovery was better with increasing altitude; there is no obvious relationship between clay content in the topsoil and vegetation recovery; (4) Meadows recovered best and the worst recovery was in mixed coniferous broad-leaved forest; (5) 81,338 ha of vegetation in the damage area is currently undergoing degradation and the main vegetation types in the degradation area are coniferous forest (31.39%) and scrub (34.17%); (6) From 2009 to 2013, 41% has been restored to the level before the earthquake, 9% has not returned but 50% will continue to recover. The Chinese government usually requires five years as a period for post-disaster reconstruction. This paper could be regarded as a guidance for Chinese government departments, whereby additional investment is encouraged for vegetation recovery.

Spatio-Temporal Change of Lake Water Extent in Wuhan Urban Agglomeration Based on Landsat Images from 1987 to 2015

Urban lakes play an important role in urban development and environmental protection for the Wuhan urban agglomeration. Under the impacts of urbanization and climate change, understanding urban lake-water extent dynamics is significant. However, few studies on the lake-water extent changes for the Wuhan urban agglomeration exist. This research employed 1375 seasonally continuous Landsat TM/ETM+/OLI data scenes to evaluate the lake-water extent changes from 1987 to 2015. The random forest model was used to extract water bodies based on eleven feature variables, including six remote-sensing spectral bands and five spectral indices. An accuracy assessment yielded a mean classification accuracy of 93.11%, with a standard deviation of 2.26%. The calculated results revealed the following: (1) The average maximum lake-water area of the Wuhan urban agglomeration was 2262.17 km2 from 1987 to 2002, and it decreased to 2020.78 km2 from 2005 to 2015, with a loss of 241.39 km2 (10.67%). (2) The lake-water areas of loss of Wuhan, Huanggang, Xianning, and Xiaogan cities, were 114.83 km2, 44.40 km2, 45.39 km2, and 31.18 km2, respectively, with percentages of loss of 14.30%, 11.83%, 13.16%, and 23.05%, respectively. (3) The lake-water areas in the Wuhan urban agglomeration were 226.29 km2, 322.71 km2, 460.35 km2, 400.79 km2, 535.51 km2, and 635.42 km2 under water inundation frequencies of 5%–10%, 10%–20%, 20%–40%, 40%–60%, 60%–80%, and 80%–100%, respectively. The Wuhan urban agglomeration was approved as the pilot area for national comprehensive reform, for promoting resource-saving and environmentally friendly developments. This study could be used as guidance for lake protection and water resource management.

Detection of the spatial patterns of water storage variation over China in recent 70 years

Terrestrial water storage (TWS) variation is crucial for global hydrological cycles and water resources management under climatic changes. In the previous studies, changes in water storage of some part of China have been studied with GRACE data in recent ten years. However, the spatial pattern of changes in water storage over China may be different in a long period. Here, we aimed to present long-term spatial patterns of TWS over China between 1948 to 2015 by unique Global Land Data Assimilation System Version 2 data and identify possible factors to water storage changes. The results revealed that the inner-annual variations in TWS of China exhibited remarkable downward trends with decreased rate of 0.1 cm/yr. Meanwhile, we found that spatial patterns of TWS in China can be divided into three distinct sub-regions of TWS region with increased, TWS region with decreased, TWS region with insignificant variation. The Northeast had decreased trends (−0.05 cm/yr) due to climate change and anthropogenic activities. Urban expansion is a non-ignorable factor to TWS reduction in Jing-Jin-Ji region (r = 0.61); the west had increased from 1948 to 2015 (0.03 cm/yr) due to precipitation increased and recharge by glacier melt; the south had insignificant trends and TWS varied with precipitation (r = 0.78).

The Impact of Precipitation Deficit and Urbanization on Variations in Water Storage in the Beijing-Tianjin-Hebei Urban Agglomeration

Depletion of water resources has threatened water security in the Beijing-Tianjin-Hebei urban agglomeration, China. However, the relative importance of precipitation and urbanization to water storage change has not been sufficiently studied. In this study, both terrestrial water storage (TWS) and groundwater storage (GWS) change in Jing-Jin-Ji from 1979 to the 2010s were investigated, based on the global land data assimilation system (GLDAS) and the EartH2Observe (E2O) outputs, and we used a night light index as an index of urbanization. The results showed that TWS anomaly varied in three stages: significant increase from 1981 to 1996, rapid decrease from 1996 to 2002 and increase from 2002 to the 2010s. Simultaneously, GWS has decreased with about 41.5 cm (500% of GWS in 1979). Both urbanization and precipitation change influenced urban water resource variability. Urbanization was a relatively important factor to the depletion of TWS (explains 83%) and GWS (explains 94%) since the 1980s and the precipitation deficit explains 72% and 64% of TWS and GWS variabilities. It indicates that urbanization coupled with precipitation deficit has been a more important factor that impacted depletion of both TWS and GWS than climate change only, in the Jing-Jin-Ji region. Moreover, we suggested that the cumulative effect should be considered when discussing the relationship between influence factors and water storage change.

Dynamic Change Analysis of Surface Water in the Yangtze River Basin Based on MODIS Products

The use of remote sensing to monitor surface water bodies has gradually matured. Long-term serial water change analysis and floods monitoring are currently research hotspots of remote sensing hydrology. However, these studies are also faced with some problems, such as coarse temporal or spatial resolution of some remote sensing data. In general, flood monitoring requires high temporal resolution, and small-scale surface water extraction requires high spatial resolution. The machine learning method has been proven to be effective against long-term serial surface water extraction, such as random forests (RFs). MODIS data are well suited for large-scale surface water dynamic analysis and flood monitoring because of its short return cycle and medium spatial resolution. In this paper, the Yangtze River Basin (YRB) in China was selected as the study area, and two MODIS products (MOD09A1 and MOD13Q1) and RF method were used to extract the surface water from 2000 to 2016. Considering the disadvantages of temporal or spatial resolution of these two MODIS products, this study also presents a data fusion method to combine them and get higher spatiotemporal resolution water results. Finally, 762 surface water maps from 2000 to 2016 are obtained, whose temporal and spatial resolution is every eight days and 250 m, respectively. In addition, water extent variation is analyzed and compared to observed precipitation data. The main conclusions are as follows: (1) this constructed approach for long-term serial surface water extraction based on the RF classifier is feasible, and a good fusion method is used to obtain the surface water body with higher spatiotemporal resolution; (2) the maximum area of the surface water extent is 48.53 × 103 km2, and seasonal and permanent water areas are 20.51 × 103 km2 and 28.01 × 103 km2, respectively; (3) surface water area is increasing in the YRB, such that seasonal water area decreased by 3450 km2, and the permanent water area increased by 3565 km2 in 2001–2015; (4) precipitation is the main factor causing variation in the surface water bodies, and they both show an increasing trend in 2000–2016. As such, the approach is worth referring to other remote sensing applications, and these products are very both valuable for water resource management and flood monitoring in the study area.