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Modeling coastal land use/cover (LULC) change is one of the most accurate methods for understanding temporal change in the coastal system. In this study, we first time combined Land Change Modeler, Habitat Quality Model, and Digital Shoreline Analysis System to quantify temporal coastal LULC change and driving forces between 2000-2020; combined CA-Markov Model with Sea Level Affecting Marshes Model (SLAMM) and merged the local study area’s SLR data with future representative concentration pathway (RCP8.5) scenarios to predict future coastal LULC change and associated sea-level rise (SLR) impact on the coastal land use and habitat quality in short-, medium- and long-term. Between 2000-2020, the study area experienced considerable coastal LULC change, including tidal flats, whose change was driven mainly by sea level with a total net gain of 57.93 km2, and mixed forest with a net gain of 25.90 km2. We also observed the significant loss of developed land whose change was influenced by mixed-forest and tidal flat with a total loss of -75.58 km2. From 2020 to 2060, the tidal flat will observe a remarkable net gain of 80.55 km2; these gains will make developed land the most impacted land in the study area. The study concluded that, the conversion of tidal flat, mixed-forest and mangroves into agricultural and infrastructural areas were main drivers influenced the change. The other responsible of the changes were the 450 m of coastal erosion and 400 m of accretion observed over 20 years. The study concluded that, low coastal elevation of -1m and flat slope of less than 2 degrees have also contributed to coastal change. These unprecedented changes will inevitably threaten the coastal ecosystem services, socio-economic development, and food security. Therefore, timely actions should be taken by enabling sustainable mitigation and development programs to prevent the future impacts.

Non-point source pollution (NSP) and runoff intensities and distribution are primarily affected by landscape structure and composition. Multiple causalities hinder our ability to determine significant variables that influence NSP. Therefore, we developed an approach that integrates the Soil and Water Assessment Tool (SWAT), random forest regression model, redundancy analysis, and correlation coefficient to assess the role of landscape structure on runoff and NSP in the Dongsheng basin. We used R to calculate landscape metrics and the SWAT to simulate NSP loads from 1990 to 2019. redundancy analysis (RDA), random forest, and Pearson correlation were used to analyze the relationships among landscape metrics and NSP variables. The largest patch index (LPI) shows a significant negative correlation with NSP, with an R2 of −0.58 for TP and TN and −0.62 for sediment load. The findings indicate that landscapes with larger patch sizes, a high number of patches, and aggregation of patches largely influence pollution distribution. Overall, the results suggest that the role of landscape patterns on NSP outweighs that of runoff. Moreover, the findings infer that the aggregation and connectivity of forest patches contribute to the decline in NSP load and vice versa for cropland cover. Thus, for sustainable watershed management, it is crucial to encourage unfragmented landscapes, especially pollutant-intercepting landcovers such as forests.

This paper seeks to analyse the present and future land-use change transition intensity (using Pontius intensity model) and landscape pattern using landscape metrics to investigate the relationship between the land cover regions, landscape pattern and the changes in the size of Bojiang lake in Ordos basin from 1987-2017. The accelerated changes in each land use region from 1987 and 2017 subsequently led to changes in landscape pattern. The changes experienced within 1987 and 2017 including the future changes indicated that the grassland and sparsely vegetated region increased significantly, dominating the study region which however indicated an increase in anthropogenic activities like agricultural activities in the study region. Based on our observation in this study, the change in each land use region although showed a significant relationship with the change in water region including Bojiang lake, other external factors also contributed to the changes in the size of the lake.

Rainwater harvesting (RWH) has emerged as one of the efficient strategies to augment surface and groundwater sources to meet water resource demands. The aim of this study is to present a methodology for siting RWH areas and selecting appropriate RWH structures in a watershed using Geographic Information Systems (GIS) and Remote Sensing (RS) based multi-criteria analysis. Derived thematic layers such as drainage density, slope, road network, runoff potential, distance to streams, and distance to built-up areas were used to identify suitable areas for RWH. The Weighted Linear Combination (WLC) technique was used in combination with the Boolean approach in a GIS environment. Normalized weights assigned to thematic layers and features were tested for consistency using the consistency ratio in Analytical Hierarchy Process. The study area can be divided into three categories of suitability, i.e.: (i) Most suitable with 26% coverage (167 km2); (ii) moderately suitable with 30% coverage; and (iii) least suitable with 12.6% coverage (80 km2) of the study area. Suitable RWH and artificial recharge structures were identified based on identified zones. It was found that 11 check dams could be implemented in the study area and 3 percolation tanks along streams and on the ground. This cost-effective and low data-intensive methodology presented can be replicated by relevant stakeholders in arid regions to adopt RWH as an effective strategy to address growing water shortages.

The application of Species Distribution Models (SDM) in the management of species habitat and environmental impact changes has been widely used by scientists and ecologists globally, however this study proposes a novel analytical technique involving the combination of a statistical ranking algorithm and Remote sensing GIS data (habitat and threat) to analyze the suitability hierarchy of the habitat of a signature water-bird (Larus relictus). The results indicate that about 47.63% of the study region was suitable to accommodate L. Relictus during their breeding/resting season, while 27.08% proved to be highly unsuitable for this species. Based on the spatial distribution of the statistical data already incorporated into the GIS environment, it was observed that regions surrounding the Bojiang Lake (especially polygons 38 and 42) dominated by low vegetation and increased amount of moisture proved to be the best region for this species. On the contrary, polygon 23, located in the southern part of the study region proved to be the worst region for this species being dominated by significantly high amount of threat factors having the highest mean normalized hierarchical value and ranking 60th out of 61 in terms of standard deviation. To distinctively capture different suitability level of the study region, we could say that the application of this technique is quite effective and beneficial. This is because, compared to other decision making tools, this technique which solely relies on remote sensing and vector data gives decision makers an option of weight application if so desired.

Guinea-Bissau coastlines are found highly vulnerable to coastal hazards, and this vulnerability will likely increase under future climate changes scenarios. In addition, the multi-hazardous assessment studies have not yet been conducted to clarify the status of vulnerability index to coastal hazards. Therefore, we integrated eight bio-geophysical parameters and elaborate a comprehensive Coastal Vulnerability Index to coastal hazards, stablish the rate of sea-level rise and determine the role of coastal habitats in protecting the shorelines in the Northwestern Coastline of Guinea-Bissau, by using the GIS and Coastal Vulnerability Index of InVEST Model. The study found that, out of 87 km of the studied coastlines, nearly 45 km lie in high to very-high vulnerability index. 17 km are found in a moderate vulnerability index and 25 km are found at low to very-low vulnerability index. The main responsible for high vulnerability registered in Zone-B were the wind and wave exposure, as this coastline is highly exposed to sea. The other reason was the storm surge and sea-level that rises 8.79/year, motivated by low coastal elevation. Mangrove ecosystem that are largely found in Zone-A, play very important role in protecting shoreline from coastal hazards with value 0.61, followed by forest and sand dune that are found mostly in Zone-C with 0.49 and 0.4 respectively, and saltmarsh that are relatively found in Zone-B with value 0.32. These findings can assist coastal managers in cost-effective adaptation plans, provide a scientific basis for sustainable coastal management and guidance for ecological conservation in coastal regions.

Coastal zones are transitional and inter-connected areas between marine and terrestrial ecosystems. Changes in coastal zones will inevitably have implications on coastal habitat quality. Moreover, the quantitative assessment studies on coastal zones have rarely been implemented to clarify the impact of coastal land cover change on habitat quality due to lack of long-term available data. In this research, we first time combined Habitat Quality (HQ) of InVEST Model with Digital Shoreline Analysis System (DSAS), to access the anthropogenic and natural hazards influence on coastal habitat quality change and improved the Habitat Quality Model (HQM) and estimated the habitat degradation index in area nearly 377 km2 in the Northwestern Coastline of Guinea-Bissau (NC-GB) from 1979 to 2020. The results showed that from 1979 to 2020, cultivated lands changed from 46.26 to 60.90 km2, residential lands changed from 33.71 to 41.19 km2 and coastal dune changed from 15.41 to 37.36 km2, respectively. All the coastal land cover displayed geomorphological alteration of 12.5% erosion, 39.6% accretion and 47.7% displayed moderate changes. The conversion of wetland, forest and mangrove land to cultivated, residential and coastal dune were the main responsible for habitat degradation. From 1979 to 2020, the wetland habitat was the most degraded with 38.00 to 51.00%, followed by forest 25.00 to 37.00%. Mangrove habitat showed a significant improvement of 12.00 to 5.00% of degradation, respectively. These changes largely occurred in the zone-B of our study area due to high humans’ occupation. Our findings can provide a scientific basis and practical reference for sustainable coastal management and guidance for ecological conservation.

Purpose This paper aims to analyze the context of community opinions and participation in the natural gas sector in developing countries, a case study of Tanzania. To achieve this purpose, the study pointed out six facts, namely, information on the natural gas sector; awareness of the natural gas-related policies; laws and regulations and the creation of employment opportunities; local experts in the natural gas sector; the use of natural gas revenues; and natural gas for poverty reduction and improvement of social well-being. Design/methodology/approach The study is a systematic review of the literature on community participation based on the relevant studies published between 2010 and 2018. A comprehensive literature review was carried out following the seven-step model whereby relevant themes from different potential bibliographic databases such as Google Scholar were systematically selected, compiled and analyzed using descriptive methods. Findings The study revealed that despite the various efforts made by the governments and other stakeholders to promote community participation, there is an inadequate level of community participation in the natural gas sector in developing countries. There are limited local experts for natural gas operations and low transparency on natural gas contracts, agreements and revenues. Therefore, there is the need to raise awareness for a highly informed society with a clear sense of ownership of the natural gas wealth among the local communities. Moreover, transparency and accountability are recommended for the sustainable natural gas sector development. Originality/value This paper offers new and current cross-sectoral inclusion, opinions, hopes and concerns of the community on the natural gas sector management in developing countries.

The carbon sequestration potential of different crops in their mature period in Northern China was analyzed in order to promote the selection of green species. Carbon dioxide infrared gas detection, three-dimensional fluorescence, fixed carbon enzyme activity analysis and other methods were used to test the carbon sequestration capacity of nine representative mature crops in the Beijing area. Results show that the carbon sequestration capacity of the nine crops was: corn > sorghum > wheat > tomato > cucumber > cabbage > celery > eggplant > pepper. Among them, the carbon sequestration capacity of corn and sorghum which are C4 crops was stronger than that of other crops. Meanwhile, the three-dimensional fluorescence spectra of corn were closer to sorghum, and there were significant differences in the characteristics of the three-dimensional fluorescence spectra of C3 crops. Based on the analysis of the activity of the fixed carbon enzyme—Phosphoenolpyruvate Carboxylase (PEP) carboxylase and Ribulose-1, 5-Bisphosphate (RuBP) Carboxylase of these plants show that the carbon sequestration capacity of C4 crops was much larger than C3 crops because of its higher PEP carboxylase activity. The carbon sequestration capacity of C3 crops was positively correlated with the activity of RuBP carboxylase in addition to cabbage. Therefore, the carbon sequestration capacity of crops can be measured by using the portable carbon dioxide infrared analyzer.

A proper understanding of watershed spatio-temporal hydrological characteristics is critical to the management of a watershed and its natural resources such as water and vegetation. Rainfall runoff estimation plays an important role as an integral part of watershed management. Runoff volume and distribution data provides valuable information for water management strategies such as selection of artificial water abstraction sites, water storage facilities, and soil erosion control strategies. In the present study Bojiang lake watershed was used to indicate the application of Soil Conservation Service Curve Number method (SCS-CN) coupled with Geographic Information System (GIS) and Remote Sensing (RS) techniques. The watershed falls within Erdos Larus Relictus National Nature Reserve (ELRNNR) which was listed under the wetlands of international importance in 2002. Rainfall runoff is influenced by a variety of factors within a watershed such as soil and land use/cover types, soil moisture content, rainfall, drainage density, and shape and size of the watershed. The SCS Curve number is the most popular and widely applied method for runoff estimation. GIS and Remote Sensing play an important role in estimating surface runoff by SCS-CN method. ArcGIS 10.2 software was used to overlay different thematic layers and develop an attribute table and calculate a weighted curve number. The weighted curve number was applied to the SCS-CN equations to estimate daily, monthly, and yearly runoff. Correlation coefficient (r) was used to test for the relationship between rainfall and runoff, and verify the computation of the method. The results show an average runoff of 17.78 mm which is about 7.18% of the annual average rainfall for the years 2001-2016. The derived output maps can assist in identifying suitable areas for water recharge/abstraction. The study demonstrates that SCS-CN in conjunction with GIS and RS can be used to calculate runoff for ungagged watersheds and assist in watershed management strategies.

This study involves the initiation of a novel data driven habitat suitability hierarchical approach used for the identification and prediction of suitable regions for Larus relictus a signature waterbird specie of Erdos Larus relictus National Nature Reserve (ELRNNR). This approach was proposed using GIS to incorporate a readily available environmental parameters (NDVI, NDWI, Slope, DEM, Land use, NDBI) used as the Remote sensing (RS) information and the threat factors (road networks, developed buildings and vegetation) used as the vector information to allow us quantitatively describe the habitat suitability for L. relictus. The RS and Vector information were statistically analyzed and ranked before incorporation into thiessen polygon using ArcGIS to produce the final habitat suitability output which was validated using a conventional habitat suitability model (weight applied). A sensitivity analysis conducted to assess the contribution of each parameter to this approach revealed that threat factors (Vector dataset) was the most significant factor. It was also revealed that this approach compared with HSM showed some statistical relationships indicating similar predictions in most region specifically the high and the low suitable regions. From the results, about 8% of the study region was predicted to be highly suitable for this species most of which was situated around the Lake in the region. The southern and northwestern regions covering about 45% of the study region proved to be unsuitable due to the significantly high density of threat factors in those regions. Compared to other decision making models, this approach possesses some unique qualities like flexibility and autonomy giving its users the options of weight application and reliance on expert opinion when necessary. It is also anticipated towards supporting conservationist and resource managers with the development of strategies or recommendations for sustainably managing the ecosystem to enhance the protection of L. relictus or other waterbird species in the region.