The Classification of Grassland Types Based on Object-Based Image Analysis with Multisource Data

The spatial distribution of different grassland types is important for effectively analyzing spatial patterns, obtaining key vegetation parameters using remote sensing (e.g., biomass, leaf area index, net primary production), and using and protecting grasslands. Existing classifications of grasslands by remote sensing are mostly divided according to the fractional vegetation cover or biomass, but classifications according to grassland types are scarce. In this study, we focused on the classification of different grassland types using remote sensing based on object-based image analysis (OBIA) with multitemporal images in combination with a 30-m digital elevation model (DEM) and the normalized difference vegetation index (NDVI). The grasslands were located in Hulunber, Inner Mongolia, and an autonomous region of China. The support vector machine (SVM) and random forest (RF) machine learning classifiers were selected for the classification. The results revealed the following: 1) It is feasible to generally extract different grassland types on the basis of OBIA with multisource data; the overall classification accuracy and Kappa value exceeded 90% and 0.9, respectively, using the SVM and RF machine learning classifiers, and the classification accuracy of the different grassland types ranged from 61.64% to 98.71%; 2) Multitemporal images and auxiliary data (DEM and NDVI) improved the separability of different grassland types. The information in the growing season was conducive for distinguishing temperate meadow steppe from temperate steppe and was favorable for extracting lowland meadow and swamp in the nongrowing season. The DEM and NDVI also effectively reduced the number of image segmentation objects and improved the segmentation effects; 3) Spectral and textural features were more important than geometric features in this study. A few main variables played a major role in the classification, while a large number of variables had either no significant effect or a negative effect on the classification results when the optimal feature subset was determined. This study provides a scientific basis and reference for the classification of various grassland types by remote sensing, including the data selection, image segmentation, feature selection, classifier selection, and parameter settings.     

基于本体的高分影像乡村居民地的提取方法探讨

为了提高高分辨率遥感影像乡村居民地信息的精度和完整性,采用World ViewⅡ卫星遥感数据,在基于对象影像分析方法的支持下,开展基于本体建模的乡村居民地信息提取研究。首先提出高分辨率遥感影像乡村居民地认知框架,再获得乡村居民地模型的形式化表达,建立乡村居民地本体的概念化模型,最后根据本体模型提取乡村居民地信息。该结果具有良好的精度和完整性,说明基于本体建模提取乡村居民地信息的方法是可行的。     

Land cover changes and fragmentation in mountain neotropical ecosystems of Oaxaca,Mexico under community forest management

Changes in land cover have a direct impact on forest ecosystem goods and services. In this study, changes in land cover in Sierra de Juarez–Oaxaca ecosystems were estimated using a consistent processing of Landsat images and OBIA methodology. Additionally, landscape analyses using FRAGSTAT were conducted. In 2014, Sierra de Juarez–Oaxaca was covered by approximately 84% of forests, mainly pine-oak and cloud forests. After extensive deforestation until 2001, this trend was reversed and the forest cover surface area in 2014 was slightly higher than in 1979. The comparison of the landscape structure of the forested and agricultural lands suggests an increase in habitat heterogeneity. However, interspersion and juxtaposition indices, showing the patch shape by patch area and perimeter, were similar throughout the study period(1979–2014). Social and economic drivers can explain this situation: namely, community organization, forest enterprises, payment for ecosystem services programs, and changes of agricultural activity. Communities in the Sierra of Oaxaca have reforested degraded lands, created community forest enterprises, and preserved the forest under conservation schemes like those proposed by the Mexican payment for ecosystem services programs. However, their sustainable management faces internal challenges and has become highly dependent on political and institutional decisions beyond their control.     

High-precision monitoring of urban structures to understand changes in multiple ecosystem services

To safeguard the well-being of urban dwellers, it is vital to restore, protect and enhance urban green infrastructures (uGI), their related ecosystem services (ES) and the associated benefits for a large number of inhabitants. This study maps and monitors land cover between 2012 and 2018 in the fast-growing German city of Leipzig to produce precise information using OBIA and very high-resolution digital orthophotos. Based on this, this research pinpoints spatially differentiated multiple ES. Research has revealed that essential ES, which comprise regulating, socio-cultural and cultural-aesthetic services, have a multifunctional impact on the human urban habitat. The study provides insight into each ES type by evaluating specific classes of objects within the urban environment in a spatially explicit way and at a very high scale of resolution. In doing so, it illustrates variations in the provision of ES and renders visible disparities in the accessibility to uGI in Leipzig. By analysing the number and stands of trees and their respective height development, the study confirms that intensive management is successfully rejuvenating the urban forest, but also that foliage in this forest is suffering from drought. The mapping procedure reveals a high spatial and temporal variation in the rates of carbon storage. This is also the case for the provision of recreation areas which has an impact on the equitable distribution of ES to Leipzig’s inhabitants. Residential areas with a relatively high uGI on the outskirts of the city actually register lower market rents and rent growth rates than in those districts which lie closer to the city centre and have a comparably lower uGI. Thus, market rents and uGI have become decoupled in the fast growing city. In order to ensure and maintain the well-being of all residents in a fair way, fast growing cities like Leipzig must make even greater efforts in urban planning.