Yuanyuan Zhao

Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach

Remote sensing images are useful for monitoring the spatial distribution and growth of urban built-up areas because they can provide timely and synoptic views of urban land cover. Although the normalized difference built-up index (NDBI) is useful to map urban built-up areas, it still has some limitations. This study sought to improve the NDBI by using a semiautomatic segmentation approach. The proposed approach had more than 20% higher overall accuracy than the original method when both were implemented simultaneously at the National Olympic Park (NOP), Beijing, China. One reason for the improvement is that the proposed NDBI approach separates urban areas from barren and bare land to some extent. More importantly, the proposed method eliminates the original assumption that a positive NDBI value should indicate built-up areas and a positive normalized difference vegetation index (NDVI) value should indicate vegetation. The new method has improved universality and lower commission error compared with the original method.

Detecting land-use/land-cover change in rural-urban fringe areas using extended change-vector analysis

Abstract Detecting land-use/land-cover (LULC) changes in rural–urban fringe areas (RUFAs) timely and accurately using satellite imagery is essential for land-use planning and management in China. Although traditional spectral-based change-vector analysis (CVA) can effectively detect LULC change in many cases, it encounters difficulties in RUFAs because of deficiencies in the spectral information of satellite images. To detect LULC changes in RUFAs effectively, this paper proposes an extended CVA approach that incorporates textural change information into the traditional spectral-based CVA. The extended CVA was applied to three different pilot RUFAs in China with different remotely sensed data, including Landsat Thematic Mapper (TM), China–Brazil Earth Resources Satellite (CBERS) and Advanced Land Observing Satellite (ALOS) images. The results demonstrated the improvement of the extended CVA compared to the traditional spectral-based CVA with the overall accuracy increased between 4.66% and 8.00% and the kappa coefficient increased between 0.10 and 0.15, respectively. The advantage of the extended CVA lies in its integration of both spectral and textural change information to detect LULC changes, allowing for effective discrimination of LULC changes that are spectrally similar but texturally different in RUFAs. The extended CVA has great potential to be widely used for LULC-change detection in RUFAs, which are often heterogeneous and fragmental in nature, with rich textural information.

Assessing the potential impacts of urban expansion on regional carbon storage by linking the LUSD-urban and InVEST models

The timely and effective assessment of the impacts of urban expansion on regional carbon storage is an important issue in the fields of urban ecology and sustainability science. This study used a new model to assess the impacts of urban expansion on regional carbon storage by linking the LUSD-urban and InVEST models. First, the LUSD-urban model was used to simulate urban expansion. Then, the InVEST model was adopted to assess the impacts on regional carbon storage. The linked model combines the strengths of these two models. Not only can it simulate and project the process of urban expansion but it can also assess the impacts of urban expansion on regional carbon storage. A case study in Beijing showed that the relative error between the simulated carbon storage loss and the actual loss was less than 12%. We argue that the linked model can be applied to assess the ecological effects of future urban expansion. We developed a model to assess the impacts of urban expansion on carbon storage.The linked model combines the strengths of the LUSD-urban and the InVEST model.The potential impacts of future urban expansion on carbon storage can be estimated.A case study in Beijing confirms that the model is relatively efficient and accurate.

Differentiating climate- and human-induced drivers of grassland degradation in the Liao River Basin, China

Quantitatively distinguishing grassland degradation due to climatic variations from that due to human activities is of great significance to effectively governing degraded grassland and realizing sustainable utilization. The objective of this study was to differentiate these two types of drivers in the Liao River Basin during 1999–2009 using the residual trend (RESTREND) method and to evaluate the applicability of the method in semiarid and semihumid regions. The relationship between the normalized difference vegetation index (NDVI) and each climatic factor was first determined. Then, the primary driver of grassland degradation was identified by calculating the change trend of the normalized residuals between the observed and the predicted NDVI assuming that climate change was the only driver. We found that the RESTREND method can be used to quantitatively and effectively differentiate climate and human drivers of grassland degradation. We also found that the grassland degradation in the Liao River Basin was driven by both natural processes and human activities. The driving factors of grassland degradation varied greatly across the study area, which included regions having different precipitation and altitude. The degradation in the Horqin Sandy Land, with lower altitude, was driven mainly by human activities, whereas that in the Kungl Prairie, with higher altitude and lower precipitation, was caused primarily by climate change. Therefore, the drivers of degradation and local conditions should be considered in an appropriate strategy for grassland management to promote the sustainability of grasslands in the Liao River Basin.

Improving change vector analysis by cross-correlogram spectral matching for accurate detection of land-cover conversion

Time series of vegetation index (VI) information derived from remote sensing is important for land-cover change detection. Although traditional change vector analysis (TCVA) is an effective method for extracting land-cover change information from a time series of VI data, it has the disadvantage of being too sensitive to temporal fluctuations in VI values. The method tends to overestimate the changes and confuse the actual land-cover conversion with the land covers that have not been converted but experience significant VI changes. Cross-correlogram spectral matching (CCSM) can tell the degree of shape similarity between VI profiles and be used to detect land-cover conversion. However, this method may omit some land conversion in which the before and after land-cover types are rather similar in VI profile shape but differ significantly in absolute VI values. This article proposes a new approach that improves TCVA with an adapted use of CCSM. First, TCVA is employed for preliminary detection of land-cover changes. Second, the changes caused by temporal fluctuations of VI values are identified through the CCSM analysis and excluded to only keep the most likely land-cover conversions. Finally, classification is performed to map the different types of land-cover conversions. The improved change vector analysis (ICVA) was applied to detect land-cover conversions from 2000 to 2008, using a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced VI images for the Beijing–Tianjin–Tangshan urban agglomeration district, China. The results show that ICVA is able to detect land-cover conversion with a significantly higher accuracy (78.00%, κ = 0.56) than TCVA (64.00%, κ = 0.35) or CCSM (66.60%, κ = 0.27). The proposed approach is of particular value in distinguishing actual land-cover conversion from land-cover modifications resulting from phenological changes.

Alternative future analysis for assessing the potential impact of climate change on urban landscape dynamics

Assessing the impact of climate change on urban landscape dynamics (ULD) is the foundation for adapting to climate change and maintaining urban landscape sustainability. This paper demonstrates an alternative future analysis by coupling a system dynamics (SD) and a cellular automata (CA) model. The potential impact of different climate change scenarios on ULD from 2009 to 2030 was simulated and evaluated in the Beijing-Tianjin-Tangshan megalopolis cluster area (BTT-MCA). The results suggested that the integrated model, which combines the advantages of the SD and CA model, has the strengths of spatial quantification and flexibility. Meanwhile, the results showed that the influence of climate change would become more severe over time. In 2030, the potential urban area affected by climate change will be 343.60-1260.66 km(2) (5.55 -20.37 % of the total urban area, projected by the no-climate-change-effect scenario). Therefore, the effects of climate change should not be neglected when designing and managing urban landscape.

Monitoring vegetation dynamics by coupling linear trend analysis with change vector analysis: a case study in the Xilingol steppe in northern China

Timely and accurate monitoring of grassland vegetation dynamics is essential for sustainable grassland management in China. We coupled linear trend analysis (LTA) with change vector analysis (CVA) to improve the effectiveness of grassland monitoring. LTA was used to detect continuous inter-annual vegetation trends to identify significant change trend regions (SCTRs) in location and significant change trend periods (SCTPs) in time. Then CVA was used to depict intra-annual change intensities in SCTRs for a SCTP. The Xilingol steppe in northern China was selected to evaluate the methods performance. Digital images of degraded grasslands derived by the proposed method using data from the VEGETATION instrument on board the Systme Probatoire d’Observation de la Terre (SPOT/VGT) were compared to those derived from Landsat images of the same area. Linear regression analysis comparing degraded grassland areas from the two imagery sources showed good correspondence. An overall accuracy of 85.33% and a kappa coefficient of 0.66 were obtained through error matrix analysis. The results showed a general grassland degradation trend from 1998 to 2007. The SCTRs were mostly distributed in the north, and the grassland degradation trend in SCTRs was more significant from 1998 to 2001 and from 2003 to 2007 during the study period. About 19% of the vegetated area was composed of degraded steppe grassland for the two time periods.

The brightness temperature adjusted dust index: An improved approach to detect dust storms using MODIS imagery

Moderate Resolution Imaging Spectroradiometer (MODIS) imagery provides a good data source for timely and accurate monitoring of dust storms. However, effective MODIS-based dust indices are inadequate. In this study, we proposed an improved brightness temperature adjusted dust index (BADI) by integrating the brightness temperatures of three thermal infrared MODIS bands: band20 (3.66–3.84 μm), band31 (10.78–11.28 μm) and band32 (11.77–12.27 μm). We used the BADI to monitor several representative dust storms over the Northeast Asia between 2000 and 2011. When compared to commonly used MODIS-based dust indices, such as the brightness temperature difference index in band32 and band31 (BTD32-31) and the normalized difference dust index (NDDI), the BADI captured the spatial extent and density of dust storms more accurately. The BADI detected dust storm extent with an overall accuracy >90%, which was 7% and 29% higher than the results derived from BTD32-31 and NDDI, respectively. The BADI also demonstrated good agreement with the density indicator of MODIS Deep Blue Aerosol Optical Depth (R2 = 0.59, P < 0.01). We suggest that the BADI is an effective tool to monitor large-scale dust storms.

Land-use/land-cover change detection by using the extended change-vector analysis

Detecting land-use/land-cover (LULC) changes in rural-urban fringe areas (RUFAs) timely and accurately using satellite imagery is essential for land-use planning and management in China. Although traditional spectral-based change-vector analysis (CVA) can offer an effective method of LULC-change detection in many cases, it encounters difficulties in RUFAs because of deficiencies in the spectral information of satellite images. To detect LULC changes in RUFAs effectively, this paper proposes an extended CVA approach that incorporates textural change information into the traditional spectral-based CVA. The extended CVA was applied to the Haidian District, Beijing, China. The results demonstrated the improvement of the extended CVA compared to the traditional spectral-based CVA: overall accuracy increased from 90.67% to 95.33%, and the kappa coefficient increased from 0.81 to 0.91. The advantage of the extended CVA lies in its integration of both spectral and textural change information to detect LULC changes, allowing for effective discrimination of LULC changes that are spectrally similar but texturally different in RUFAs. The extended CVA has great potential to be widely used for LULC-change detection in RUFAs, which are often heterogeneous and fragmental in nature, with rich textural information.

Distinguishing the impacts of land use and arid process on natural potential productivity of cultivated land in the North Farming : Pastoral Zone of China

The paper distinguished the impacts of land use and arid process on the Natural Potential Productivity of Cultivated Land (NPPCL) in the North Farming - Pastoral Zone of China (NFPZC) from 1990 to 2000 with the integration of remote sensing technique and Geographical Information System (GIS). The arid processes in NFPZC from 1970 to 2006 were analyzed. The land use processes from 1990 to 2000 were investigated. The NPPCL in NFPZC from 1990 to 2000 were calculated by using the Thornthwaite-Memorial model. And finally the influences of land use and arid process on the NPPCL in NFPZC from 1995 to 2007 were distinguished by using the powerful spatial analysis function of GIS. The main results were as follows: (1) In spite of some climate variation, it still had an obvious arid process in the NFPZC during the past three decades. Such arid process made the NPPCL in the NFPZC decrease 16.61 million tons from 1990 to 1995 and 19.55 million tons from 1995 to 2000. (2) From 1990 to 2000, cultivated land in NFPZC changed intensively. It expanded from 231907 km2 in 1990 to 238032 km2 in 1995 and 244109 km2 in 2000. Such land use process caused the NPPCL in the NFPZC increase 5.36 million tons from 1990 to 1995 and 4.48 million tons from 1995 to 2000. (3) Influenced simultaneously by land use and arid process, NPPCL also changed obviously in NFPZC from 1990 to 2000 with 11.24 million tons decrease during 1990 and 1995 and 15.08 million tons decrease during 1995 and 2000 respectively. Spatially, the NPPCL is sensitive to arid process in the Northwest area of NFPZC, governed by Shanxi province, Gansu province and Ningxia autonomous region. While in the Northeast area of NFPZC governed by Hebei province and Shanxi provinces, land use play the dominate role to influence NPPCL. It suggested that the impacts of both the cultivated land loss and the climate change on cultivated land productivity should be simultaneously concerned to avoid food problems in China.