The objective of this paper is to exploit the potential application of an evidential belief function model to landslide susceptibility mapping at Kuala Lumpur city and surrounding areas using geographic information system (GIS). At first, a landslide inventory map was prepared using aerial photographs, high resolution satellite images and field survey. A total 220 landslides were mapped and an inventory map was prepared. Then the landslide inventory was randomly split into a testing dataset 70% (153 landslides) and remaining 30% (67 landslides) data was used for validation purpose.

Land cover classification using remotely sensed data requires robust classification methods for the accurate mapping of complex land cover area of different categories. In this regard, support vector machines (SVMs) have recently received increasing attention. However, small number of training samples remains a bottleneck to design suitable supervised classifiers. On the other hand, adequate number of unlabeled data is available in remote sensing images which can be employed as additional source of information about margins.

The patterns of urban sprawl over a 20-year period presented in the study indicate unplanned development in the urban agglomerations of Ranchi, Jamshedpur and Dhanbad. The visual interpretation of Landsat (1986, 1991, 1996 and 2001) and IRS-P6 (2005) was used to map land use/land cover and analyse urban sprawl. The saturation of urban areas within municipal limits, along with pressure from the growing population, resulted in the densification of the core urban areas within Dhanbad and Jamshedpur.

Flood regulation is a widely valued and studied service provided by watersheds. Flood regulation benefits people directly by decreasing the socio-economic costs of flooding and indirectly by its positive impacts on cultural (e.g., fishing) and provisioning (e.g., water supply) ecosystem services. Like other regulating ecosystem services (e.g., pollination, water purification), flood regulation is often enhanced or replaced by technology, but the relative efficacy of natural versus technological features in controlling floods has scarcely been examined.

Critical to effective urban climate adaptation is a clearer understanding of the sensitivities of resource demand to changing climatic conditions and land cover situations. We used Bayesian Maximum Entropy (BME) stochastic procedures to estimate temperature and precipitation at the very small scale of urban Census Block Groups (CBGs) in Phoenix, Arizona and Portland, Oregon, and then compared average household water use patterns by climate conditions and land cover characteristics between and within the two cities.

This study aims to assess CO₂ levels in China, based on the dry air column-averaged mixing ratios of CO₂ measured by the Scanning Imaging Absorption spectrometer for Atmospheric CartograpHY (SCIAMACHY) instrument on board ENVISAT and emission inventories. A land cover and statistical data set, the Regional emission inventory in Asia (REAS), was also used in this study. The results show that the spatial variations of the CO₂ concentration in the whole of China are obvious, and with clear seasonal fluctuation during 2003–2005.

While crop rotation patterns can be complex with multiple crops rotated over several years, the most common rotation practice in the Central United States is biannual rotation between corn and soybeans. We analyzed the changes in crop rotation patterns from 2003 to 2010 using the Cropland Data Layer (CDL), which provides remotely sensed land cover layers for agricultural crops in the Central United States.

Monitoring land-cover change is often done by simple overlay of two classified maps from different dates. However, such analysis tends to overestimate the rate of change. Main error sources are the mis-registration between classified maps and their thematic accuracies. This study proposes a change detection method with morphological post-processing to improve change detection accuracy in comparison with traditional post-classification by taking into account these error sources.

In Malaysia, areas under oil palm plantations have increased dramatically since the early twentieth century and have resulted in multiple conversions of land change. This paper presents a spatial and temporal model for simulation of oil palm expansion in the Kuala Langat district, Malaysia. The model is an integration of cellular automata (CA), multi-criteria evaluation (MCE), and Markov chain (MC) analysis while MCE provides transition rules of CA iterations and MC analysis assigns a transition probability to each single pixel at the time steps.

The objective of this paper was to quantify long-term land use and land cover changes (LULCC) and to identify the spatial determinants of locations of most systematic transitions for the period 1957–2009 in the Jedeb watershed, Upper Blue Nile Basin. Black and white aerial photographs of 1957 and Landsat imageries of 1972 (MSS), 1986 (TM), 1994 (TM) and 2009 (TM) were used to derive ten land use and land cover classes by integrated use of Remote Sensing (RS) and Geographic Information System (GIS).

Despite numerous ecosystem services provided by urban trees, they are continually threatened by combined natural disturbances, invasive species, development and negligent management practices. This research characterizes the amount and cause of tree loss in Worcester, Massachusetts, in the northeast United States, and neighbouring towns between 2008 and 2010 using multi-source remotely sensed imagery and historical land cover maps (1976–2009).

Adequate quantification of evapotranspiration (ET) is crucial to assess how climate change and land cover change (LCC) interact with the hydrological cycle of terrestrial ecosystems. The Mongolian Plateau plays a unique role in the global climate system due to its ecological vulnerability, high sensitivity to climate change and disturbances, and limited water resources. Here, we used a version of the Terrestrial Ecosystem Model that has been modified to use Penman–Monteith (PM) based algorithms to calculate ET.