Remotely sensed land cover data can be a tremendous resource to land use decision makers, yet there is often a disconnect between the worlds of remote sensing and local government. The Connecticut’s Changing Landscape project is focused on bridging this gap. The project analyzes changes to the state’s landscape using Landsat-derived 30-m land cover and cross-correlation analysis. It includes seven dates spanning 30 years, from 1985 to 2015.

Long-term anthropogenic land use and land cover changes (LULCCs) are regarded as an important component of past global change. The past 300 years have witnessed dramatic changes in LULCC in China, and this has resulted in the large-scale conversion of natural vegetation to agricultural landscapes. Studies of past LULCC in China have mainly focused on cropland and forest; however, estimates of grassland cover remain rare due to the scarcity of grassland-related historical documents.

Land cover is one of key indicators for modeling ecological, environmental, and climatic processes, which changes frequently due to natural factors and anthropogenic activities. The changes demand various samples for updating land cover maps, although in reality the number of samples is always insufficient. Sample augment methods can fill this gap, but these methods still face difficulties, especially for high-resolution remote sensing data.

Understanding the effects of socio-ecological shocks on land use/land cover (LULC) change is essential for developing land management strategies and for reducing adverse environmental pressures. Our study examines the impacts of the armed conflict in Syria, which began in mid-2011, and the related social and economic crisis on LULC between 2010 and 2018. We used remote sensing for change detection by applying a supervised maximum likelihood classification to Landsat images of the three target years 2010, 2014, and 2018.

In fast-developing regions, like Southeast-Asia, monitoring urban areas presents a challenge given the lack of publicly available data. This is an issue that precludes the nuances of a city’s growth and undermines the way land-use is considered with respect to planning. The issue of data availability is very much present in the small nation of Brunei. Little is still known about the spatiotemporal evolution of its urban realm; in particular, with regard to its national development planning.

Landscape provides many services for human wellbeing through its mosaic of ecosystems. Although different landscape spatial structures limit some access to these services for local residents, their demand for landscape benefits creates a crucial component in landscape planning. Herein, we evaluate the ecosystem service supply from landscape structures in four different areas of Slovakia and we identify the public preferences for these services. This evaluation was assisted by expert-based ecosystem services (ES) matrix assessment and feedback from experts and key local stakeholders.

Land as an essential resource is becoming increasingly scarce due to population growth. In the case of the Kenyan coast, population pressure causes land cover changes in the Arabuko Sokoke Forest, which is an important habitat for endangered species. Forest and bushland have been changed to agricultural land in order to provide livelihood for the rural population who are highly dependent on small-scale farming. Unclear land rights and misbalanced access to land cause uncontrolled expansion and insecure livelihoods.

This paper presents a new approach for rapidly assessing the extent of land use and land cover (LULC) areas in Mato Grosso state, Brazil. The novel idea is the use of an annual time series of fraction images derived from the linear spectral mixing model (LSMM) instead of original bands. The LSMM was applied to the Project for On-Board Autonomy-Vegetation (PROBA-V) 100-m data composites from 2015 (~73 scenes/year, cloud-free images, in theory), generating vegetation, soil, and shade fraction images. These fraction images highlight the LULC components inside the pixels.

Landscape research involves a large number of scientific disciplines. Different disciplinary and scale approaches have led to the creation of numerous land use/land cover databases with different classification nomenclature. It is very important for end-users of databases to know the capabilities and limits of land use/land cover data to avoid potential mistakes resulting from inappropriate combinations and interpretations.

Measuring, monitoring, and managing biodiversity across agricultural regions depends on methods that can combine high-resolution mapping of landscape patterns with local biodiversity observations. This study explores the potential to monitor biodiversity in agricultural landscapes by linking high-resolution remote sensing with passive acoustic monitoring.

The Prairie Pothole Region (PPR) is a biotically important region of grassland, wetland, and cropland that traverses the Canada-US border. Significant amounts of grasslands and wetlands within the PPR have been converted to croplands in recent years due to increasing demand for biofuels. We characterized land dynamics across the US portion of the PPR (US–PPR) using the USDA Crop Data Layer (CDL) for 2006–2018. We also conducted a comparative analysis between two epochs (1998–2007 & 2008–2017) of the CDL data time series in the North Dakotan portion of the US–PPR.

Understanding the trajectories and extents of land use/land cover change (LULCC) is important to generate and provide helpful information to policymakers and development practitioners about the magnitude and trends of LULCC. This study presents the contributing factors of LULCC, the extent and implications of these changes for sustainable land use in the Finchaa catchment. Data from Landsat images 1987, 2002, and 2017 were used to develop the land use maps and quantify the changes. A supervised classification with the maximum likelihood classifier was used to classify the images.