Muye Gan

Monitoring Urban Greenness Dynamics Using Multiple Endmember Spectral Mixture Analysis

Urban greenness is increasingly recognized as an essential constituent of the urban environment and can provide a range of services and enhance residents’ quality of life. Understanding the pattern of urban greenness and exploring its spatiotemporal dynamics would contribute valuable information for urban planning. In this paper, we investigated the pattern of urban greenness in Hangzhou, China, over the past two decades using time series Landsat-5 TM data obtained in 1990, 2002, and 2010. Multiple endmember spectral mixture analysis was used to derive vegetation cover fractions at the subpixel level. An RGB-vegetation fraction model, change intensity analysis and the concentric technique were integrated to reveal the detailed, spatial characteristics and the overall pattern of change in the vegetation cover fraction. Our results demonstrated the ability of multiple endmember spectral mixture analysis to accurately model the vegetation cover fraction in pixels despite the complex spectral confusion of different land cover types. The integration of multiple techniques revealed various changing patterns in urban greenness in this region. The overall vegetation cover has exhibited a drastic decrease over the past two decades, while no significant change occurred in the scenic spots that were studied. Meanwhile, a remarkable recovery of greenness was observed in the existing urban area. The increasing coverage of small green patches has played a vital role in the recovery of urban greenness. These changing patterns were more obvious during the period from 2002 to 2010 than from 1990 to 2002, and they revealed the combined effects of rapid urbanization and greening policies. This work demonstrates the usefulness of time series of vegetation cover fractions for conducting accurate and in-depth studies of the long-term trajectories of urban greenness to obtain meaningful information for sustainable urban development.

Comparison of machine-learning methods for above-ground biomass estimation based on Landsat imagery

Abstract. Biomass is one significant biophysical parameter of a forest ecosystem, and accurate biomass estimation on the regional scale provides important information for carbon-cycle investigation and sustainable forest management. In this study, Landsat satellite imagery data combined with field-based measurements were integrated through comparisons of five regression approaches [stepwise linear regression, K-nearest neighbor, support vector regression, random forest (RF), and stochastic gradient boosting] with two different candidate variable strategies to implement the optimal spatial above-ground biomass (AGB) estimation. The results suggested that RF algorithm exhibited the best performance by 10-fold cross-validation with respect to R2 (0.63) and root-mean-square error (26.44  ton/ha). Consequently, the map of estimated AGB was generated with a mean value of 89.34  ton/ha in northwestern Zhejiang Province, China, with a similar pattern to the distribution mode of local forest species. This research indicates that machine-learning approaches associated with Landsat imagery provide an economical way for biomass estimation. Moreover, ensemble methods using all candidate variables, especially for Landsat images, provide an alternative for regional biomass simulation.

Rural Settlement Subdivision by Using Landscape Metrics as Spatial Contextual Information

Multiple policy projects have changed land use and land cover (LULC) in China’s rural regions over the past years, resulting in two types of rural settlements: new-fashioned and old-fashioned. Precise extraction of and discrimination between these two settlement types are vital for sustainable land use development. It is difficult to identify these two types via remote sensing images due to their similarities in spectrum, texture, and geometry. This study attempts to discriminate different types of rural settlements by using a spatial contextual information extraction method based on Gaofen 2 (GF-2) images, which integrate hierarchical multi-scale segmentation and landscape analysis. A preliminary LULC map was derived by using only traditional spectral and geometrical features from a finer scale. Subsequently, a vertical connection was built between superobjects and subobjects, and landscape metrics were computed. The vertical connection was used for assigning landscape contextual information to subobjects. Finally, a classification phase was conducted, in which only multi-scale contextual information was adopted, to discriminate between new-fashioned and old-fashioned rural settlements. Compared with previous studies on multi-scale contextual information, this paper employs landscape metrics to quantify contextual characteristics, rather than traditional spectral, textural, and topological relationship information, from superobjects. Our findings indicate that this approach effectively identified and discriminated two types of rural settlements, with accuracies over 80% for both producers and users. A comparison with a conventional top-down hierarchical classification scheme showed that this novel approach improved accuracy, precision, and recall. Our results confirm that multi-scale contextual information with landscape metrics provides valuable spatial information for classification, and indicates the practicability, applicability, and effectiveness of this synthesized approach in distinguishing different types of rural settlements.

Discrimination of Settlement and Industrial Area Using Landscape Metrics in Rural Region

Detailed and precise information of land-use and land-cover (LULC) in rural area is essential for land-use planning, environment and energy management. The confusion in mapping residential and industrial areas brings problems in energy management, environmental management and sustainable land use development. However, they remain ambiguous in the former rural LULC mapping, and this insufficient supervision leads to inefficient land exploitation and a great waste of land resources. Hence, the extent and area of residential and industrial cover need to be revealed urgently. However, spectral and textural information is not sufficient for classification heterogeneity due to the similarity between different LULC types. Meanwhile, the contextual information about the relationship between a LULC feature and its surroundings still has potential in classification application. This paper attempts to discriminate settlement and industry area using landscape metrics. A feasible classification scheme integrating landscape metrics, chessboard segmentation and object-based image analysis (OBIA) is proposed. First LULC map is generated from GeoEye-1 image, which delineated distribution of different land-cover materials using traditional OBIA method with spectrum and texture information. Then, a chessboard segmentation of the whole LULC map is conducted to create landscape units in a uniform spatial area. Landscape characteristics in each square of chessboard are adopted in the classification algorithm subsequently. To analyze landscape unit scale effect, a variety of chessboard scales are tested, with overall accuracy ranging from 75% to 88%, and Kappa coefficient from 0.51 to 0.76. Optimal chessboard scale is obtained through accuracy assessment comparison. This classification scheme is then compared to two other approaches: a top-down hierarchical classification network using only spectral, textural and shape properties, and lacunarity based hierarchical classification. The distinction approach proposed is overwhelming by achieving the highest value in overall accuracy, Kappa coefficient and McNemar test. The results show that landscape properties from chessboard segment squares could provide valuable information in classification.

Using nonparametric modeling approaches and remote sensing imagery to estimate ecological welfare forest biomass

The spatial distribution of forest biomass is closely related with carbon cycle, climate change, forest productivity, and biodiversity. Efficient quantification of biomass provides important information about forest quality and health. With the rising awareness of sustainable development, the ecological benefits of forest biomass attract more attention compared to traditional wood supply function. In this study, two nonparametric modeling approaches, random forest (RF) and support vector machine were adopted to estimate above ground biomass (AGB) using widely used Landsat imagery in the region, especially within the ecological forest of Fuyang District in Zhejiang Province, China. Correlation analysis was accomplished and model parameters were optimized during the modeling process. As a result, the best performance modeling method RF was implemented to produce an AGB estimation map. The predicted map of AGB in the study area showed obvious spatial variability and demonstrated that within the current ecological forest zone, as well as the protected areas, the average of AGB were higher than the ordinary forest. The quantification of AGB was proven to have a close relationship with the local forest policy and management pattern, which indicated that combining remote-sensing imagery and forest biophysical property would provide considerable guidance for making beneficial decisions.

Dynamics of Hierarchical Urban Green Space Patches and Implications for Management Policy

Accurately quantifying the variation of urban green space is the prerequisite for fully understanding its ecosystem services. However, knowledge about the spatiotemporal dynamics of urban green space is still insufficient due to multiple challenges that remain in mapping green spaces within heterogeneous urban environments. This paper uses the city of Hangzhou to demonstrate an analysis methodology that integrates sub-pixel mapping technology and landscape analysis to fully investigate the spatiotemporal pattern and variation of hierarchical urban green space patches. Firstly, multiple endmember spectral mixture analysis was applied to time series Landsat data to derive green space coverage at the sub-pixel level. Landscape metric analysis was then employed to characterize the variation pattern of urban green space patches. Results indicate that Hangzhou has experienced a significant loss of urban greenness, producing a more fragmented and isolated vegetation landscape. Additionally, a remarkable amelioration of urban greenness occurred in the city core from 2002 to 2013, characterized by the significant increase of small-sized green space patches. The green space network has been formed as a consequence of new urban greening strategies in Hangzhou. These strategies have greatly fragmented the built-up areas and enriched the diversity of the urban landscape. Gradient analysis further revealed a distinct pattern of urban green space landscape variation in the process of urbanization. By integrating both sub-pixel mapping technology and landscape analysis, our approach revealed the subtle variation of urban green space patches which are otherwise easy to overlook. Findings from this study will help us to refine our understanding of the evolution of heterogeneous urban environments.

Monitoring the trajectory of urban nighttime light hotspots using a Gaussian volume model

Abstract Urban nighttime light hotspot is an ideal representation of the spatial heterogeneity of human activities within a city, which is sensitive to regional urban expansion pattern. However, most of previous studies related to nighttime light imageries focused on extracting urban extent, leaving the spatial variation of radiance intensity insufficiently explored. With the help of global radiance calibrated DMSP-OLS datasets (NTLgrc), we proposed an innovative framework to explore the spatio-temporal trajectory of polycentric urban nighttime light hotspots. Firstly, NTLgrc was inter-annually calibrated to improve the consistency. Secondly, multi-resolution segmentation and region-growing SVM classification were employed to remove blooming effect and to extract potential clusters. At last, the urban hotspots were identified by a Gaussian volume model, and the resulting parameters were used to quantitatively depict hotspot features (i.e., intensity, morphology and centroid dynamics). The result shows that our framework successfully captures hotspots in polycentric urban area, whose Ra2 are over 0.9. Meanwhile, the spatio-temporal dynamics of the hotspot features intuitively reveal the impact of the regional urban growth pattern and planning strategies on human activities. Compared to previous studies, our framework is more robust and offers an effective way to describe hotspot pattern. Also, it provides a more comprehensive and spatial-explicit understanding regarding the interaction between urbanization pattern and human activities. Our findings are expected to be beneficial to governors in term of sustainable urban planning and decision making.

Carbon emissions induced by land-use and land-cover change from 1970 to 2010 in Zhejiang, China

Land-use and land-cover change (LUCC) is a crucial factor affecting carbon emissions. Zhejiang Province has witnessed unprecedented LUCC concomitant with rapid urbanization from 1970 to 2010. In this study, remote sensing, geographic information system (GIS) and the Intergovernmental Panel on Climate Change (IPCC) method were combined to quantify changes in both vegetation carbon storage and soil organic carbon (SOC) storage resulting from LUCC during 1970-1990 and 1990-2010. For both 1970-1990 and 1990-2010, the results showed successive decrease in farmlands (2.8 × 105 ha or -9.15% and 5.9 × 105 ha or -20.49%, respectively) and grasslands (3.4 × 104 ha or -10.73% and 1.5 × 105 ha or -54.1%, respectively), and continuous increase in forests (2.0 × 104 ha or 0.33% and 1.7 × 105 ha or 2.81%, respectively) and built-up lands (2.07 × 105 ha or 78.41% and 6.49 × 105 ha or 137.8%, respectively). From 1970 to 1990, approximately 8.3 Tg of the total carbon sink declined, including a 0.4 Tg reduction in vegetation carbon and a 7.9 Tg reduction in SOC. While from 1990 to 2010, approximately 17.5 Tg of carbon storage declined, comprising a 2.8 Tg of carbon accumulated by vegetation, and a 20.3 Tg reduction in SOC. Overall, LUCC has resulted in huge amount of carbon emissions in Zhejiang from 1970 to 2010. Efficient planning for LUCC and gradual mitigation of carbon emissions are indispensable for future urban development in China under increasing pressure from global warming.

Delineating Urban Boundaries Using Landsat 8 Multispectral Data and VIIRS Nighttime Light Data

Administering an urban boundary (UB) is increasingly important for curbing disorderly urban land expansion. The traditionally manual digitalization is time-consuming, and it is difficult to connect UB in the urban fringe due to the fragmented urban pattern in daytime data. Nighttime light (NTL) data is a powerful tool used to map the urban extent, but both the blooming effect and the coarse spatial resolution make the urban product unable to meet the requirements of high-precision urban study. In this study, precise UB is extracted by a practical and effective method using NTL data and Landsat 8 data. Hangzhou, a megacity experiencing rapid urban sprawl, was selected to test the proposed method. Firstly, the rough UB was identified by the search mode of the concentric zones model (CZM) and the variance-based approach. Secondly, a buffer area was constructed to encompass the precise UB that is near the rough UB within a certain distance. Finally, the edge detection method was adopted to obtain the precise UB with a spatial resolution of 30 m. The experimental results show that a good performance was achieved and that it solved the largest disadvantage of the NTL data-blooming effect. The findings indicated that cities with a similar level of socio-economic status can be processed together when applied to larger-scale applications.

Monitoring coastal land reclamation and land use change around Hangzhou Bay using Landsat dataset (1970s-2014)

The coastal region is an important potential land resource, and reclamation is a valid means to utilize land and expand human living space. Since the 1970s, large-scale reclamation projects have taken place in eastern coastal regions, China. To examine the reclamation program around the Hangzhou Bay in Zhejiang Province, China-using a time-series Landsat dataset in 1976, 1980, 1990, 2000, 2005, 2010 and 2014, a visual interpretation is applied to extract artificial coastline and reclamation land-use information. The result showed that during the year 1976 to 2014 period, the total reclamation area around Hangzhou Bay is 1039.84 km2, and the project was mainly occurred in south of Hangzhou Bay, particularly in Ningbo and Shaoxing county. In addition, between 1976 and 1980, the speed of reclamation was higher than any other period, followed by period from 2006 to 2009. Moreover, the early reclamation lands were mainly used for cropland and aqua-farm ponds. After the year 1990, industrial warehouse space and land for harbor and wharf first appeared, and both of them have increased markedly. The land use types tend to be of diversity overall since 21st century.