Forest restoration and support for sustainable ecosystems in the Gandaki Basin, Nepal.

Restoring degraded forest is essential if we are to reduce human pressure on natural ecosystems and their biodiversity. Forests were nationalized in 1957 in Nepal and as a consequence, forest cover declined from 45% in 1964 to just 29% in 1994. However, as its response, sectoral plans and policies, particularly introduction of community-based forest management programs since the 1980s and conservation activities resulted in large scale forest cover restoration. Here, we examined the forest cover change in the Gandaki River Basin (GRB), the catchment with the largest altitudinal variation (ranging from ± 93 to 8167 m) and environmental and ecological significance. To see how forests have changed since then, we analyzed snapshots of spatiotemporal, ecological and physiographic changes in forest cover, and forest type at decadal intervals from 1996 to 2016 using Landsat 5 and 8 satellite images. We observed an overall gain in forest cover of 207 km2, from 7571 km2 (34.4% of the total area) in 1996 to 7778 km2 (35.3%) in 2016. Of the 21 forest cover types identified, the greatest forest coverage during 2016 was of Schima-Castanopsis forest (25.9%) and hill sal forests (16.4%). In terms of physiographic zones, land below 500 m (Tarai) where most people live, witnessed gradual declines in forest cover, in contrast to large increases in forests above 500 m. Historical examination of forest cover at ecological and physiographic scales helps to identify the elevation-wise distribution of forest resources, vegetation composition, ecosystem characteristics, anthropogenic pressure upon vegetation, and hence the overall influence of LULC upon the environment. These outputs will assist planners, policy makers, and researchers in their formulation of effective basin wide plans and policies to ensure the protection of basin level biodiversity and ecosystem function.

Comparison of three hybrid models to simulate land use changes: a case study in Qeshm Island, Iran.

Land use change simulation is an important issue for its role in predicting future trends and providing implications for sustainable land management. Hybrid models have become a recognized strategy to inform decision-makers, but further attempts are needed to warrant the reliability of their projected results. In view of this, three hybrid models, including the cellular automata-Markov chain-artificial neural network, cellular automata-Markov chain-logistic regression, and Markov chain-artificial neural network, were applied to simulate land use change on the largest island in Iran, Qeshm Island. The Figure of Merit (FOM) was used to measure the modeling accuracy of the simulations, with the FOMs for the three models 6.7, 5.1, and 4.5, respectively. Consequently, the cellular automata-Markov chain-artificial neural network most precisely simulates land use change on Qeshm Island and is, thus, used to simulate land use change until 2026. The simulation shows that the incremental trend of the built-up class will continue in the coming years. Meanwhile, the areas of valuable ecosystems, such as mangroves, tend to decrease. Despite the protection plans for mangroves, these areas require more attention and conservation planning. This study demonstrates a referential example to select the proper land use models for informing planning and management in similar coastal zones.

Simulating urban expansion in a rapidly changing landscape in eastern Tarai, Nepal.

Understanding the spatiotemporal dynamics of urbanization and predicting future growth is now essential for sustainable urban planning and policy making. This study explores future urban expansion in the rapidly growing region of eastern lowland Nepal. We used the hybrid cellular automata-Markov (CA-Markov) model, which utilizes historical land use and land cover (LULC) maps and several biophysical change driver variables to predict urban expansion for the years 2026 and 2036. Transitional area matrices were generated based on historical LULC data from 1996 to 2006, from 2006 to 2016, and from 1996 to 2016. The approach was validated by cross comparing the actual and simulated maps for 2016. Evaluation gave satisfactory values of Kno (0.89), Kstandard (0.84), and Klocation (0.89) which verifies the accuracy of the model. Hence, the CA-Markov model was utilized to simulate the LULC map for the years 2026 and 2036. The study area experienced rapid peri/urban expansion and sharp decline in area of cultivated land during 1989-2016. Built-up area increased by 110.90 km2 over a period of 27 years at the loss of 87.59 km2 cultivated land. Simulation analysis indicates that urban expansion will continue with urban cover increasing to 230 km2 (8.95%) and 318.51 km2 (12.45%) by 2026 and 2036, respectively, with corresponding declines in cultivated land to 1453.83 km2 (56.86%) and 1374.93 km2 (53.77%) for the same years. The alarming increase in urban areas coupled with loss of cultivated land will have negative implications for food security and environmental equilibrium in the region.

A cost-effective and efficient framework to determine water quality monitoring network locations.

A crucial part in designing a robust water quality monitoring network is the selection of appropriate water quality sampling locations. Due to cost and time constraints, it is essential to identify and select these locations in an accurate and efficient manner. The main contribution of the present article is the development of a practical methodology for allocating critical sampling points in present and future conditions of the non-point sources under a case study of the Khoy watershed in northwest Iran, where financial resources and water quality data are limited. To achieve this purpose, the river mixing length method (RML) was applied to propose potential sampling points. A new non-point source potential pollution score (NPPS) was then proposed by the analytic network process (ANP) to classify the importance of each sampling point prior to selecting the most appropriate locations for a river system. In addition, an integrated cellular automata-Markov chain model (CA-Markov) was applied to simulate future change in non-point sources during the period 2026-2036. Finally, by considering anthropogenic activities through land-use mapping, the hierarchy value, the non-point source potential pollution score values and budget deficiency in the study area, the seven sampling points were identified for the present and the future. It is not expected, however, that the present location of the proposed sampling points will change in the future due to the forthcoming changes in non-point sources. The current study provides important insights into the design of a reliable water quality monitoring network with a high level of assurance under certain changes in non-point sources. Furthermore, the results of this study should be valuable for water quality monitoring agencies looking for a cost-effective approach for selecting sampling locations.