In this study, we measured and characterized the relative dielectric constant of mineral soils over the 0.3–3.0 frequency range, and compared our measurements with values of three dielectric constant simulation models (the Wang, Dobson, and Mironov models). The interrelationship between land cover and soil texture with respect to the dielectric constant was also investigated. Topsoil samples (0–10 cm) were collected from homogenous areas based on a land unit map of the study site, located in the Gamsar Plain in northern Iran.

This study evaluated the effect of Land Use and Land Cover (LULC) dynamics on the value of ecosystem services in Abaya-Chamo basin over 1985–2050. The main objectives of the study were to estimate the value of ecosystem services of Abaya-Chamo basin using local and global ecosystem service value coefficients, assess how it changes over time, and develop tools to inform policy and public decision-making to protect lands and waters in the region.

The Land Sector Non State Actors (LSNSA) is a network of civil society organizations working together to promote secure and equitable access to land and natural resource for all through advocacy, dialogue and capacity building. We petition parliament on issues we hold to be of fundamental importance in the context and content of the two bills before the National Assembly.

The first set of the land laws were enacted in 2012 in line with the timelines outlined in the Constitution of Kenya 2010. In keeping with the spirit of the constitution, the Land Act, Land Registration Act and the national Land Commission Act respond to the requirements of Articles 60, 61, 62, 67 & 68 of the Constitution.

The Role Of Wetlands In Poverty Reduction- Extreme poverty among rural poor people living around wetlands remains a daily reality for more than 56% of Kenya’s population, who subsist on less than one dollar a day. Seventy percent of extremely poor households, a majority of who live in rural areas where hunger and poverty prevails, are now being caught up in a new web of lack of access to wetlands as safety-net during hard times due to appropriation of wetlands by private developers.

The wide availability of multispectral satellite imagery through projects such as Landsat and Sentinel, combined with the introduction of deep learning in general and Convolutional Neural Networks (CNNs) in particular, has allowed for the rapid and effective analysis of multiple classes of problems pertaining to land coverage. Taking advantage of the two phenomena, we propose a machine learning model for the classification of land abandonment.

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.

Land use and land cover change (LULCC) is a critical factor for enhancing the soil erosion risk and land degradation process in the Wabi Shebelle Basin. Up-to-date spatial and statistical data on basin-wide erosion rates can provide an important basis for planning and conservation of soil and water ecosystems. The objectives of this study were to examine the magnitude of LULCC and consequent changes in the spatial extent of soil erosion risk, and identify priority areas for Soil and Water Conservation (SWC) in the Erer Sub-Basin, Wabi Shebelle Basin, Ethiopia.

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.

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.