There are growing demands for detailed and accurate land cover maps in land system research and planning. Macro-scale land cover maps normally cannot satisfy the studies that require detailed land cover maps at micro scales. In the meantime, applying conventional pixel-based classification methods in classifying high-resolution aerial imagery is ineffective to develop high accuracy land-cover m...

In this work, we evaluated the agreement between land cover maps generated by TerraClass and MapBiomas projects for Pará state and, more specifically: (1) describe the legends based on an international classification system, (2) analyze the differences among classes and (3) test PostGIS Raster from PostgreSQL database to work with classification products. The classifications were compared pixel...

The main objective of this project is to develop and test methodology using ENVISAT ASAR data for agricultural applications, with an emphasis on land use, land cover classification and rice mapping. An optimal pre-processing chain for ASAR data is first constructed to provide input data to the classification steps. Experiments at the Zhangzhou test site, Fujian province, southern China, indicat...

Describing and quantifying the spatial heterogeneity of land cover in urban systems is crucial for developing an ecological understanding of cities. This paper presents a new approach to quantifying the fine-scale heterogeneity in urban landscapes that capitalizes on the strengths of two commonly used approaches—visual interpretation and object-based image analysis. This new approach integrates...

Land cover classification with a high accuracy is necessary, especially in waste dump area, accurate land cover information is very important to eco-environment research, vegetation condition study and soil recovery destination. Funded by the international cooperation project Novel Indicator Technologies for Minesite Rehabilitation and sustainable development, a hierarchical object oriented lan...

Land cover and forest mapping supports decision makers in the course of making informed decisions for implementation of sustainable conservation and management plans of the forest resources and environmental monitoring. This research examines the value of integrating of ALOS PALSAR and Landsat data for improved forest and land cover mapping in Northern Tanzania. A separate and joint processing ...

The objective of this research is to assess the multitemporal very high resolution single polarization SAR data for urban land cover/land-use mapping using a novel knowledge-based SEM algorithm. Three-date RADARSAT-2 ultra-fine beam SAR data were collected over the rural-urban fringe of Greater Toronto Area. A modified Stochastic Expectation-Maximization (SEM) algorithm which employs an adaptiv...

Landscape epidemiology has made significant strides recently, driven in part by increasing availability of land cover data derived from remotely-sensed imagery. Using an example from a study of land cover effects on hantavirus dynamics at an Atlantic Forest site in eastern Paraguay, we demonstrate how automated classification methods can be used to stratify remotely-sensed land cover for studie...

This study aims to develop a land cover texture-based classification scheme applicable for ALOS PALSAR imageries of the upper Marikina watershed acquired 2007 – 2010. From the raw dual polarization bands of HH+HV that has a ground resolution of 25m, additional bands HH/HV and NL was computed for surface texture normalization. The classification scheme was based on texture analysis using grey le...

Supervised classification of hyperspectral image is considered to be the method of choice for improved land use/cover mapping. However, there is no single classifier which offers acceptable results across various study sites, sensors and resolutions. Further, the use of dimensionality reduction methods further increase the permutations and combinations required for selecting the appropriate cla...