Accurate and timely information about the geographic distribution of irrigated cropland is important for a range of applications including crop assessments, water resources management, drought monitoring, and environmental modeling.

The breakdown of socialism caused massive socio-economic and institutional changes that led to substantial agricultural land abandonment. The goal of our study was to identify the determinants of agricultural land abandonment in post-Soviet Russia during the first decade of transition from a state-controlled economy to a market-driven economy (1990–2000). We analyzed the determinants of agricultural land abandonment for approximately 150,550km² of land area in the provinces (oblasts) of Kaluga, Rjazan, Smolensk, Tula and Vladimir in European Russia.

This study used linear programming (LP) to analyse land-use alternatives in the traditional Umbundu farming system in the Angolan central highlands. Farmers of the region have traditionally produced maize and pulses for subsistence and vegetables and timber as cash crops. Different pasture and forest fallow rotations are used along catena production sites. The system is labour-intensive and uses animal traction. LP problems were formulated and solved for a baseline land-use alternative, improved diet alternative and maximal timber production alternative.

Land areas in collective ownership or use are traditionally referred to as commons. Through history, the common use and ownership has been a widespread means of regulating the use of natural resources. Changing economic conditions and technology spawned a process however where land use rights and landowner rights aggregated into the modern form of private ownership of individual farms with full management and owner rights. This process had obvious rationales in terms of production of marketable agricultural products.

This paper examines environmental disturbances related to energy development in the Orenburg region through a two-pronged approach. First, it ranks environmental performance per 25 administrative districts based on multidimensional clustering of 10 diagnostic indicators. Second, it examines land-use and land-cover (LULC) patterns in nine western administrative districts by classifying Landsat imagery and quantifying surface disturbance for the year 2001. Spatial analysis and descriptive statistics help identify the most disturbed administrative units.

The purpose of this study was to characterize the land use, vegetation structure, and diversity in the Barnowpara Sanctuary, Raipur district, Chhattisgarh, India through the use of satellite remote sensing and GIS. Land cover and vegetation were spatially analyzed by digitally classifying IRS 1D LISS III satellite data using a maximum likelihood algorithm. Later, the variations in structure and diversity in different forest types and classes were quantified by adopting quadratic sampling procedures.

The NitroEurope project aims to improve understanding of the nitrogen (N) cycle at the continental scale and quantify the major fluxes of reactive N by a combination of reactive N measurements and modelling activities. As part of the overall measurement strategy, a network of 13 flux 'super sites' (Level-3) has been established, covering European forest, arable, grassland and wetland sites, with the objective of quantifying the N budget at a high spatial resolution and temporal frequency for 4.5 years, and to estimate greenhouse gas budgets (N₂O, CH₄ and CO₂).

Impact assessment of watershed development activity assumes greater importance in present day agriculture. Considering the ability of remote sensing technology in watershed monitoring and impact assessment, a study was carried out to investigate the Impact Assessment of Karnataka Watershed Development Project (DANIDA) in Koralahallihalla Sub watershed in Sindagi taluk of Bijapur district in Northern Karnataka using satellite data of two periods i.e., IRS 1 C, LISS-III data of 30 December, 1997 (pre-treatment) and IRS P6, LISS-III data of 17 December, 2004 (post-treatment).

Consistent, repeatable and broadly applicable land use, land cover data is needed across the Lake Superior basin to facilitate ecosystem condition assessment and trend analysis. Such a data set collected regularly through time could inform and focus field monitoring efforts, and help prioritize restoration and mitigation efforts. Unfortunately, few data sets exist that are bi-nationally consistent in time, classification method, or resolution.

Coastal wetlands are important contributors to large-lake productivity and biodiversity and mediators of lake—watershed interactions. This study explores whether the size of the watershed in which coastal wetlands are embedded (a measure of strength of connection to the terrestrial landscape) influences their background condition and response to anthropogenic landuse.

An optimized artificial immune network-based classification model, namely OPTINC, was developed for remote sensing-based land use/land cover (LULC) classification.