Article Remote Sensing of Agro-droughts in Guangdong Province of China Using MODIS Satellite Data

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Impacts of Landscape Change on Water Resources

Changes in land use and land cover can have many drivers, including population growth, urbanization, agriculture, demand for food, evolution of socio-economic structure, policy regulations, and climate variability. The impacts of these changes on water resources range from changes in water availability (due to changes in losses of water to evapotranspiration and recharge) to degradation of water quality (increased erosion, salinity, chemical loadings, and pathogens). The impacts are manifested through complex hydro-bio-geo-climate characteristics, which underscore the need for integrated scientific approaches to understand the impacts of landscape change on water resources. Several techniques, such as field studies, long-term monitoring, remote sensing technologies, and advanced modeling studies, have contributed to better understanding the modes and mechanisms by which landscape changes impact water resources. Such research studies can help unlock the complex interconnected influences of landscape on water resources in terms of quantity and quality at multiple spatial and temporal scales. In this Special Issue, we published a set of eight peer-reviewed articles elaborating on some of the specific topics of landscape changes and associated impacts on water resources

Agroclimatic analysis for mainland East Asia by a GIS approach

Climate has been long recognised as an important constraint to crop production. Many agroclimatic analyses have been developed in Mainland East Asian countries to assist their agricultural development and resource management. These analyses were all restricted to using a limited number of data points and static summations of climatic variables. The complex climatic patterns and the non-linear responses of crops to climate cannot be captured by such agroclimatic analyses. This thesis addressed the agroclimatic environment and its impact on crop production using a different philosophy and methodology in order to overcome these shortcomings. There are four related components in this study. They are: 1). regular grid climatic data sets; 2). crop responses to the environmental elements in Mainland East Asia; 3). agroclimatic classification; and 4). crop modeling at selected representative stations from various agroclimatic zones. The regular grid climatic data sets consist of climatic surfaces and a digital elevation model (DEM). In this study they were developed at a resolution of l/20th degree. While this study focused on agroclimatic analysis these data sets can be applied to any other fields that relate to climate such as forestry, ecology and conservation. These climatic surfaces express climatic variables as functions of multi-dimensional thin plate smoothing splines in term of longitude, latitude and elevation. They were developed using the ANUSPLIN package, and are based on a network of up to 3800 stations across Mainland East Asia. Estimates for climatic variables at any location in the Mainland East Asian countries can be calculated from these surfaces with input of the appropriate independent variables. A DEM at a resolution of l/20th degree, calculated using the ANUDEM package and based on terrain data digitised from topographic maps, was used to construct data sets in this study. These data sets consist of 434,484 grid cells across the studying area. Based on such data sets, crop responses to the climatic environment were simulated using a general plant growth model GROWEST. This model transforms the non-linear responses of key plant groups to linear dimensionless scalars. These include a light index (LI), a thermal index (Tl), a moisture index (MI) and an integrated multi-factor growth index (GI). The spatial and seasonal variations of these indices were analysed for each of the 434,484 grid cells across Mainland East Asia. With 39 selected GROWEST attributes, Mainland East Asia was classified into 66 groups and further aggregated to 14 agroclimatic zones using the ALOC and FUSE modules of a numerical taxonomic package PATN. These agroclimatic zones have been given descriptive labels, thus; 1.Cold high plateau zone 2.Hot dry desert 3.Grassland zone 4.Single-crop 5.Double crop/wheat and 6.Double crop/rice 7.Warm hills 8.Warm highlands 9.Tropical mountain tops 10.Tropical forest 11.Triple-crop 12.Humid tropical lowlands 13.Perhumid tropical highlands 14.Perhumid tropical lowlands Finally, 14 representative stations were selected from the major cropping zones of Mainland East Asia for more detailed crop modeling using the DSSAT v3 package. The Seasonal Analysis module was used to model wheat, maize and rice production for for a period of 15 years, and has further demonstrated the major climatic constraints on crop production for various agroclimatic zones

Feature Papers of Water Resources Management, Policy and Governance

Water resource management includes the consideration of all disciplines of hydrology and water sources. Water supplies are allocated and diverted to cover the water needs of a range of agricultural, municipal, industrial, hydro-electrical, and ecological water uses. These water uses are, usually, very competitive, as the available water resources are limited and it is not possible to cover the total water needs in a basin, requiring the setting of water use priorities to best serve societal and ecological needs. To manage the water resources and waterworks may, sometimes, lead to confrontational deliberations and negotiations. As a result, water resource management is one of the world’s greatest challenges due to competition for limited resources, regional disparities in water supply and affluence, mounting global water demand, aquifer depletion, and pollution- and climate-change-induced water stress. Proper policy and governance for sustainable water resource management is essential and require new fresh ideas, innovation, and international cooperation. This book includes seven papers by invited renowned researchers and engineers to cover issues of water resource management, governance, and policy. These issues include the following topics: Integrated water resource management; Water resource systems and water availability; National and international water policy, institutional arrangements, and water law; Water conflict resolution, public participation, and decision making; Water resource management, policy and governance in socially and environmentally sensitive areas and regions

Sustainable Use of Soils and Water: The Role of Environmental Land Use Conflicts

This book on the sustainable use of soils and water addressed a variety of issues related to the utopian desire for environmental sustainability and the deviations from this scene observed in the real world. Competing interests for land are frequently a factor in land degradation, especially where the adopted land uses do not conform with the land capability (the natural use of soil). The concerns of researchers about these matters are presented in the articles comprising this Special Issue book. Various approaches were used to assess the (im)balance between economic profit and environmental conservation in various regions, in addition to potential routes to bring landscapes back to a sustainable status being disclosed

Coupled effects of climate variability and land use pattern on surface water quality: An elasticity perspective and watershed health indicators.

Understanding the coupled effects of climate variability and land use on riverine nitrogen is essential for watershed management. The climate-water relationships for ammonium (NH4-N) and nitrate (NO3-N) were determined by an elasticity approach and then the watershed health index was estimated using the reliability, resilience, and vulnerability framework. These methods were applied to an in-situ monitoring dataset of N concentrations measured during 2010-2017 from nine sub-watersheds in the Jiulong River Watershed, China. The results showed that temperature and precipitation elasticity of NH4-N and NO3-N changed substantially among various land use patterns. The N concentrations were highly sensitive to extreme climate conditions, particularly at urban and agricultural sub-watersheds. The measure of risk indicators revealed that the watershed health index varied from good health to unhealthy status. Linear regression analysis was used to analyze the interactions among watershed characteristics, climate elasticity, and watershed health. Cropland and population had strong positive correlations with climate elasticity of NO3-N. Forest and elevation had strong negative associations with climate elasticity of NO3-N. Watershed health significantly declined with increasing proportion of cropland and population density. This study demonstrated that human-impacted watersheds were less healthy to unhealthy and tend to be more sensitive to climate variability than natural watersheds, which is useful for efforts aimed at improving watershed management

Developing a Suitability Index for Residential Land Use: A case study in Dianchi Drainage Area

The conflict between residential land and agriculture land in China is increasingly sharpened, especially when some urban development began to sprawl to the suburban and rural areas. In order to plan land resources properly, land suitability assessment is often conducted to determine which type of land use is most appropriate for a particular location. The main objective of this study is to examine how land suitability assessment methods could be used in land planning processes in the Dianchi Drainage Area (DDA) in Southwest China to identify where future residential development should be located. The 1991 Toronto Waterfront Plan and the more recent 2005 Ontario Greenbelt Plan are examined and used to develop a framework which describes the potential for land suitability assessment in the DDA. Data limitations did not permit a suitability analysis to be completed for the DDA, however a description of methodologies for conducting residential land suitability analysis and required data are presented based on a review of relevant literature. The paper concludes with a discussion of the feasibility of land suitability in the DDA and other areas in China and also suggests opportunities for future research

PREDICTING TROPICAL RAINFOREST DEFORESTATION USING MACHINE LEARNING, REMOTE SENSING & GIS: CASE STUDY OF THE CROSS RIVER NATIONAL PARK, NIGERIA

Population growth, urban sprawl, agricultural expansion, and illegal logging has led to losses in forested land in most parts of the world, especially in a highly populated country like Nigeria. The Cross River National Park (CRNP) in southeastern Nigeria with an area just above 4000km2 is designated a biodiverse hotspot and one of the oldest rainforests in Africa. As with all other tropical forests spread across the globe the CRNP is not immune to these factors that threaten its existence. The focus of this study is to analyze the change of forest cover at the Oban division of the Cross River National Park using multi-temporal remotely sensed data to predict and model the future probability of deforestation within the area of interest. This study made use of the Landsat West Africa Land Use/Land Cover Time Series dataset for the years 1975, 2000 and 2013 and Landsat 8 operational land imager (OLI) imagery for the year 2020 in a post classification change detection model to determine the extent of change in forest cover classes. Random forest decision tree machine learning algorithm was used to predict the future risk of forest cover loss using the datasets produced from the post classification change detection. The model related deforestation probabilities with several physical and anthropogenic factors such as elevation, slope angle, solar radiation, aspect, topographic roughness, soil type, distance from roads, distance from towns, distance from rivers, distance from plantations and population density. The results from the change detection analysis showed that from 1975 to 2020 the forest cover declined by 1909km2 a rate of 42km2 per year. The random forest regression analysis predicted areas of the forest with modest to high deforestation probabilities and indicated that socio-economic factors are major drivers of deforestation in the region rather than physical factors