Land cover changes affect ecological landscape spatial pattern, and evolving landscape patterns inevitably cause an evolution in ecosystem functionality. Various ecological landscape variables, such as biological productivity (plant biomass and stock capacity), soil nutrients (organic matter and N content) and water source conservation capacity are identified as landscape function characteristics. A quantitative method and digital model for analyzing evolving landscape functionality in the headwaters areas of the Yangtze River, China were devised.

Understanding changes in soil quality resulting from land use and land management changes is important to design sustainable land management plans or interventions. This study evaluated the influence of land use and land cover (LULC) on key soil quality indicators (SQIs) within a small watershed (Jedeb) in the Blue Nile Basin of Ethiopia. Factor analysis based on principal component analysis (PCA) was used to determine different SQIs.

Image segmentation is a basic and important procedure in object-based classification of remote-sensing data. This study presents an approach to multi-scale optimal segmentation (OS), given that single-scale segmentation may not be the most suitable approach to map a variety of land-cover types characterized by various spatial structures; it objectively measures the appropriate segmentation scale for each object at various scales and projects them onto a single layer. A 1.8 m spatial resolution Worldview-2 image was used to perform successive multi-scale segmentations.

Information about changes in, and causes of, land use/land cover (LULC) is crucial for land use resource planning. We investigated the processes involved in LULC change (LUCC) in the Dongjiang Watershed, in Southern China, over a 15-year period to gain a better understanding of the causes of the main types of LUCC. Using a depth transition matrix and redundancy analysis (RDA), the major types and causes of LUCC for each LULC type over the past 15 years were identified. LUCC exhibited obvious net change, relatively low persistence, and high swap change.

A new locally-adaptive image classification method LAGMA (Locally-Adaptive Global Mapping Algorithm) has been developed to meet requirements of land cover mapping over large areas using remote-sensing data. The LAGMA involves the grid-based supervised image classification using classes’ features estimated locally in classified pixels’ surrounding from spatially distributed reference data.

Change detection with remotely sensed imagery plays an important role in land cover mapping, process analysis and dynamic information services. Euclidean distance, correlation and other mathematic metrics between spectral curves have been used to calculate change magnitude in most change detection methods. However, many pseudo changes would also be detected because of inter-class spectral variance, which remains a significant challenge for operational remote sensing applications.

Potential data sets for landcover classification, such as Landsat (or pre-processed data such as the National Land Cover Dataset (NLCD)), are often too coarse for fine-scale research needs or are cost-prohibitive (Quickbird, Ikonos and Geoeye). Repeated attempts at classifying high spatial resolution data, National Agricultural Imagery Program (NAIP) imagery, based on traditional techniques, such as a maximum likelihood supervised classification, have failed to produce a product with sufficient accuracy.

Accurate and timely land cover change detection at regional and global scales is necessary for both natural resource management and global environmental change studies. Satellite remote sensing has been widely used in land cover change detection over the past three decades. The variety of satellites which have been launched for Earth Observation (EO) and the large volume of remotely sensed data archives acquired by different sensors provide a unique opportunity for land cover change detection.

Quantifying the amount of forest and change in the amount of forest are key to ensure that appropriate management practices and policies are in place to maintain the array of ecosystem services provided by forests. There are a range of analytical techniques and data available to estimate these forest parameters, however, not all ‘forest’ is the same and various components of change have been presented. Forest as defined by use and forest as defined by cover are different, although it is common for scientists and policy makers to infer one from the other.

CONTEXT: With rapidly expanding urban regions, the effects of land cover changes on urban surface temperatures and the consequences of these changes for human health are becoming progressively larger problems. OBJECTIVES: We investigated residential parcel and neighborhood scale variations in urban land surface temperature, land cover, and residents’ perceptions of landscapes and heat illnesses in the subtropical desert city of Phoenix, AZ USA.

The effects of land use policy and socioeconomic changes on urban landscape dynamics have been increasingly investigated around the world, but our knowledge of the underlying processes of these effects is still inadequate for sustainably managing urban ecosystems.

Assessing the potential impact of future land-cover changes on habitat quality requires projections with a fine spatial and thematic resolution. The former is usually addressed by downscaling methods, often at the expense of the latter. We present a new, rule-based method to downscale land-use change scenarios to the landscape level while keeping a large number of land-cover classes (CORINE level 3). The method relies on the interpretation of European scenario storylines, the observation of past land-use changes, high-resolution regional data and expert knowledge.