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.

Most of the plantation impact studies reported in literature normally use either one of the sensitivity-based approach or a hydrological model with few actually comparing the impact results from these different approaches. This paper investigates the impacts of increase or decrease in plantations and climate variability on streamflow using two approaches: the sensitivity-based approach (including a non-parametric model and six Budyko framework based models) and the hydrological modelling approach (using Xinanjiang and SIMHYD models) for three medium sized catchments in Australia.

After the industrial revolution, urban growth has been increasing, especially with technological advances. Urbanization is accelerating environmental pollution and also affects climate significantly because of land use or land cover changes. In this study, the Hydrological Simulation Program-Fortran (HSPF) model developed by the United States Environment Protection Agency (USEPA) is used for modeling the impervious areas of Eskişehir which is located in the Porsuk Stream Watershed in Inner Anatolia, Turkey.

Identifying the relative importance of climatic and other environmental controls on the interannual variability and trends in global land surface phenology and greenness is challenging. Firstly, quantifications of land surface phenology and greenness dynamics are impaired by differences between satellite data sets and phenology detection methods. Secondly, dynamic global vegetation models (DGVMs) that can be used to diagnose controls still reveal structural limitations and contrasting sensitivities to environmental drivers.

Large remote sensing datasets, that either cover large areas or have high spatial resolution, are often a burden of information mining for scientific studies. Here, we present an approach that conducts clustering after gray-level vector reduction. In this manner, the speed of clustering can be considerably improved. The approach features applying eigenspace transformation to the dataset followed by compressing the data in the eigenspace and storing them in coded matrices and vectors.

As part of the development of the 2011 National Land Cover Database (NLCD) tree canopy cover layer, a pilot project was launched to test the use of high-resolution photography coupled with extensive ancillary data to map the distribution of tree canopy cover over four study regions in the conterminous US. Two stochastic modeling techniques, random forests (RF) and stochastic gradient boosting (SGB), are compared.

In this study, a multi-scale approach was used for classifying land cover in a high resolution image of an urban area. Pixels and image segments were assigned the spectral, texture, size, and shape information of their super-objects (i.e. the segments that they are located within) from coarser segmentations of the same scene, and this set of super-object information was used as additional input data for image classification.

The integrated remote sensing (RS) and geographic information system (GIS) approach was utilized in this study to classify land use and land cover (LULC), detect changes based over time, and identify transition trends in the Marikina sub-watershed, Laguna de Bay watershed, Philippines. Landsat 5 Thematic Mapper (TM) imageries acquired in 1999 and 2006 were pre-processed and classified using a supervised classification technique with maximum likelihood classifier algorithm in RS and were used to develop maps of the sub-watershed and sub-subwatershed levels in a GIS platform.

The impacts of deforestation and land cover change upon underlying soils were examined on one hillside in central Armenia. Soil characteristics in three land cover areas—forest, coppice, and pasture—were recorded and soil samples were analyzed. Deforestation and land cover change were found to increase erosion rates. From soil horizon and structural characteristics, it can be estimated that 40 cm of soil have been lost in the pasture and 20 cm have been lost in the coppice compared to the forest. Soil organic carbon was also affected by deforestation and land cover change.

The Pampean region covers a large surface in central Argentina, but despite the extensive agricultural activities and the high nutrient levels recorded in streams of the region, few authors have analysed the influence of land use on water quality. Here, we evaluated the relationships among catchment attributes (size, morphometry and land cover) and water chemistry in 23 Pampean streams in different seasons (autumn, spring and summer) and at three spatial scales: whole catchment and two scales of riparian buffers (200 and 500 m adjacent to both stream margins).

Shallow landslides induced by heavy rainfall events represent one of the most disastrous hazards in mountainous regions because of their high frequency and rapid mobility. Recent advancements in the availability and accessibility of remote sensing data, including topography, land cover and precipitation products, allow landslide hazard assessment to be considered at larger spatial scales. A theoretical framework for a landslide forecasting system was prototyped in this study using several remotely sensed and surface parameters.