Environmental monitoring and assessment of landscape dynamics in southern coast of the Caspian Sea through intensity analysis and imprecise land-use data

Nitrogen and metal pollution in the southern Caspian Sea: a multiple approach to bioassessment

The Caspian Sea hosts areas of high ecological value as well as industrial, leisure, and agricultural activities that dump into the water body different kinds of pollutants. In this complex context, a proper description of the origin and potential sources of pollution is necessary to address management and mitigation actions aimed at preserving the quality of the water resource and the integrity of the ecosystems. Here, we aimed at detecting sources of both nitrogen inputs, by N stable isotope analysis of macroalgae, and metals in macroalgae and sediments in two highly anthropized coastal stretches at the Iranian side of the Caspian Sea. Sampling was done near the mouth of rivers and canals draining agricultural and urbanized areas. In the westernmost waters, facing a port city, low macroalgal δ¹⁵N signatures indicated industrial fertilizers as the principal source of pollution. By contrast, in the central coastal waters, facing touristic areas, the high macroalgal δ¹⁵N indicated N inputs from wastewaters. Here the lowest dissolved oxygen concentrations in waters were associated with excess dissolved inorganic nitrogen. Metal concentrations varied largely in the study areas and were lower in macroalgae than in sediments. Localized peaks of Pb and Zn in sediments were observed in the central coastal sites as probable byproducts of mining activity transported downstream. By contrast, Cr and Ni concentrations were high in all sampling sites, thus potentially representing hazardous elements for marine biota. Overall, macroalgal δ¹⁵N coupled with metal analysis in macroalgae and sediments was useful for identifying the main sources of pollution in these highly anthropized coastal areas. This double approach in comprehensive monitoring programs could thus effectively inform stakeholders on major environmental threats, allowing targeted management measures.

Spatially-explicit modeling and intensity analysis of China's land use change 2000-2050

Land use change affected by wide ranges of human activities is a key driver of global climate change. In the last three decades, China has experienced unprecedented land use change accompanied by increasing environmental problems. There is a pressing need to project and analyze long-term land use scenarios that are critical for land use planning and policymaking. Using GlobeLand30 data, we examined China's land use change from 2000 to 2010, and developed a novel LandCA model for scenario projections from 2020 to 2050. The observed and projected land use change (2000-2050) was analyzed in terms of the interval, category, and transition levels. Our findings show that land Exchange intensity is more than 3 times greater than land Quantity intensity from 2000 to 2050, and the overall rate of land use change will decelerate from 2010 to 2050. During 2000-2010, the loss of built-up land to other categories was 12.7% while the gain was 32.5%, with a growth rate 3.4 times larger than that during 2010-2050. The total amount of cultivated land continuously decreases but will not violate the Chinese "Cultivated Land Red-Line Restriction" by 2050. We speculate that the government's goal of 26% forest cover by 2050 may not be achieved, as a result of strict land use policies preventing the transformation from cultivated land to forests. This study contributes to new evaluations of long-term land use change in China for the government to adjust policies and regulations for sustainable development.

Spatiotemporal Characteristic of Land Use/Land Cover Changes in the Middle and Lower Reaches of Shule River Basin Based on an Intensity Analysis

The vegetation response to climatic factors is a hot topic in global change research. With the Support of ArcGIS and ENVI software, six sets of Landsat remote sensing images of the middle and lower reaches of the Shule River Basin were interpreted. Eight types of land use and land covers were obtained and the spatiotemporal characteristics of the land use/land cover changes (LUCCs) were analyzed using an intensity analysis to provide a basis for decision-making on the sustainable development of the basin. In the past 29 years, the area of cropland, construction land and shrubland had a net increase, while high-coverage grassland (HCG), medium-coverage grassland (MCG), low-coverage grassland (LCG), wetland and non-vegetation land all presented a net decrease. The area of artificial vegetation (cropland) presented an expanding trend and increased by 1105.56 km2 in total, while the natural vegetation (grassland, shrubland, wetland) showed a shrinking tendency and decreased by 917.69 km2. The intensity analysis revealed that the rate of LUCC in the period of 2000~2006 and 2006~2010 was relatively higher, although the rate of LUCC in other periods was much lower. The change intensities of MCG and HCG were greatest, followed by LCG, shrubland and wetland. Construction land and cropland were in third place, while non-vegetation land was in last place. The pattern of regional LUCC was generally stable except for cropland loss and the gain/loss change of other land-use/land-cover types was always in an active state. For spatial distribution, few changes were observed in the old irrigated area within the oasis. The LUCC was mainly concentrated in the oasis fringe area, natural vegetation cover area and emigrant arrangement regions.

Land surface temperature estimating in urbanized landscapes using artificial neural networks

Scenario-based land surface temperature (LST) modeling is a powerful tool for adopting proper urban land use planning policies. In this study, using greater Isfahan as a case study, the artificial neural network (ANN) algorithm was utilized to explore the non-linear relationships between urban LST and green cover spatial patterns derived from Landsat 8 OLI imagery. The model was calibrated using two sets of variables: Normalized Difference Built Index (NDBI) and Normalized Difference Vegetation Index (NDVI). Furthermore, Compact Development Scenario (CDS) and Green Development Scenario (GDS) were defined. The results showed that GDS is more successful in mitigating urban LST (mean LST = 40.93) compared to CDS (mean LST = 44.88). In addition, urban LST retrieved from the CDS was more accurate in terms of ANOVA significance (sig = 0.043) than the GDS (sig = 0.010). The findings of this study suggest that developing green spaces is a key strategy to combat against the risk of LST concerns in urban areas.