Despite the popularity of the term urban livability, it is often used by different groups under different circumstances. A broader understanding of urban livability is that it concerns the quality of life in any human living environment. The World Health Organization, among many others, suggests a four-dimension assessment system based on the concepts of convenience, amenity, health and safety that can be used to evaluate any cities’ potential livability. Following this proposal, the current study taps into the power of GIS and Remote Sensing technologies to generate a set of urban livability evaluating indicators via extracted land use information. Using the city proper of Changchun, Jilin Province of China as an example, the study extracts fifteen individual land use indicators from topographic maps and a remote sensing imagery. A principal component analysis-based approach was used to build an urban livability index with the fifteen indicators. Furthermore, with detailed examination of relevant studies, national documents and local fieldwork, this research also establishes potential benchmark values for all fifteen livability evaluating indicators for comparison purposes. Results suggest that slightly more than half of Changchun’s city proper is above the livability benchmark in the framework of the current study. Residents’ access to parks and open spaces is a major lagging factor for the city proper’s livability. The study provides an alternative of quantifiable and verifiable approach for sustainable urban planning, especially from a land use policy perspective.

Introduction

Urbanization in China has experienced rapid development during the past decades. While rapid urbanization brought tremendous changes on urban landscape, scholars also observe the increase of urban pollution, traffic congestion, shrinking public services and aging infrastructure in Chinese cities (Fang et al., 2016; Liu, 2018; Liu et al., 2014; Yu et al., 2014; Zhan et al., 2018). These so-called “urban diseases” (Fang and Yu, 2016) have adversely impacted on city residents’ daily lives across the entire city. Under such circumstances, both the Chinese government and scholars attempt to address urban livability issues in recent years (Zhan et al., 2018). Although used liberally, the term urban livability often lacks a consensus of what exactly it refers to. Kashef (2016) summarizes broadly three different aspects of urban livability research, namely, from the design literature, the planning literature, and the popular media and global ranking literature, and advocates for an interdisciplinary understanding that potentially considers all aspects of urban livability. Such suggestions, though conceptually appealing, often lack practical operability. Urban planners and other urban science practitioners, on the other hand, carefully consider urban livability as a balanced and harmonious mode of economic, social, cultural, land use and environmental development in cities (Asgarzadeh et al., 2012; Flores et al., 1998; Kazemi et al., 2018; Liu et al., 2014).

Various forms of machine learning (ML) methods have historically played a valuable role in environmental remote sensing research. With an increasing amount of “big data” from earth observation and rapid advances in ML, increasing opportunities for novel methods have emerged to aid in earth environmental monitoring. Over the last decade, a typical and state-of-the-art ML framework named deep learning (DL), which is developed from the traditional neural network (NN), has outperformed traditional models with considerable improvement in performance. Substantial progress in developing a DL methodology for a variety of earth science applications has been observed. Therefore, this review will concentrate on the use of the traditional NN and DL methods to advance the environmental remote sensing process. First, the potential of DL in environmental remote sensing, including land cover mapping, environmental parameter retrieval, data fusion and downscaling, and information reconstruction and prediction, will be analyzed. A typical network structure will then be introduced. Afterward, the applications of DL environmental monitoring in the atmosphere, vegetation, hydrology, air and land surface temperature, evapotranspiration, solar radiation, and ocean color are specifically reviewed. Finally, challenges and future perspectives will be comprehensively analyzed and discussed.

Introduction

The earth’s environmental deterioration, which is caused by human behavior and is continuously aggravating, has become the primary problem hindering further developments of global changes. The lack of resources and environmental deterioration are no longer exclusive phenomena in specific regions. In the last 50 years, space information technology, especially satellite remote sensing technology, has provided advanced detection and research means for the investigation of the earth’s resources, the monitoring of local and regional environmental changes, and even the study of global changes, with the advantages of being macro, comprehensive, fast, dynamic, and accurate (Overpeck et al., 2011; Yang et al., 2013). Remote sensing data are mainly used for environmental parameter monitoring based on physical models (Liang, 2005). Although physical models can effectively express the formation process from environmental parameters to remote sensing observations, these models are largely dependent on the prior knowledge of the model parameters. Such knowledge often has large uncertainty due to the high complexity of the physical process and varies in different periods and regions, which tends to result in the limited accuracy of environmental remote sensing.

The aim of this study is to assess the water quality of Lower Jhelum Canal (LJC) and its suitability for irrigation purposes. An effort has been made to develop a method by integrating water quality index with geographic information system (GIS) for an effective interpretation of LJC water quality. The pollution status of LJC was estimated by different physicochemical and biological parameters. Based on results of analysis, a spatial distribution map of selected water quality parameters was prepared using GIS. An inverse distance weighting (IDW), which is an interpolation technique, was applied to prepare a thematic layer of parameters at each station of Lower Jhelum canal. The results of individual parameters showed that the concentrations of contamination were within permissible limits of WHO and NEQS guidelines except for E. coli. Overall, most of the water falls in excellent quality category indicating the suitability of water for irrigation purpose. The results suggest that most of the water can be used for irrigation and various intended purposes except direct use of water for potable or drinking purposes without treatment.

Introduction

Water is the most significant ingredient for supporting and evolution of life (Kuutondokwa, 2008). Humans get different sorts of benefits from freshwater, which includes water for drinking, industrialization, domestic uses, irrigation, for the production of waterfowl and fisheries, use for leisure, shipping, and waste discarding (Jackson et al., 2001). Water is a source of economic gain. Almost 70% of water is used in agricultural production, which is indirectly the cause of economic growth (Brown and Matlock, 2011). Water resources (Freshwater) are becoming limited for individuals due to overpopulation, so the accessibility of freshwater for human beings decreases (Iqbal et al., 2018). Quality of freshwater deteriorates by developmental activities that contaminate the water bodies, their effects on human health, ecosystem disturbance and issues related to its management and monitoring (Iqbal et al., 2019). Water quality deterioration is the primary threat to public health at the global level (Rahman et al., 2020). Anthropogenic actions, like improper disposal of municipal, industrialized effluents, and unsystematic use of chemicals in agriculture, are vital aspects causative in the worsening of water quality (Azizullah et al., 2011; Iqbal et al., 2020; Subedi et al., 2019; Shirani et al., 2018; Li et al., 2020). The result of these activities is eutrophication, loss of water quality, loss of biodiversity, effects on human health and social security, deposition of nutrients and other inorganic pollutants, acidification and significant economic losses (Kraemer et al., 2001). Contamination of water leads to water scarcity or it may be polluted at that level where it is expensive to treat (Gupta et al., 2012; Maged et al., 2020; Imran et al., 2020). Consequences of both water treatment and water scarcity with high cost would be the reason for the increase in water prices (Kuutondokwa, 2008). According to (Rogers et al., 2002), water is an efficient good. However, the nations which have water resources could grow their economies, while on the other hand, it will be difficult for poor or developing countries, even to avail of their essential needs.

1- Introduction

2- Methodology

3- Results

4- Discussion

5- Conclusion

References

Abstract

Weather radar can provide spatially explicit precipitation grids. However interference, ground clutter and various causes of attenuation introduce uncertainty into the result. Typically, rain gauge observations, recognized as a precise measure of precipitation at point locations, are used to adjust weather radar grids to obtain more accurate precipitation maps. This adjustment involves one or more of various geostatistic techniques. Yet, since gauges are sparsely located, a geostatistic approach is sometimes limited or even not applicable.
This work adopts an alternative to radar adjustment by merging location-based variables with rain grids from weather radar. Recognizing that location-based variables: elevation, slope, aspect and distance from the coast all affect precipitation, these are applied to the original weather radar grid to produce an altered precipitation distribution.
The merging procedure presented here uses fuzzy logic, whereby all variables, as well as the original radar are assigned probabilities known as membership functions (MF), then a joint membership function (JMF) combines all MFs in the fuzzy set, each multiplied by its weight, to create a precipitation probability grid. This JMF probability grid is validated with gauge observation data. We show up to 30% higher correlation coefficients between gauges and the JMF grid than between gauges and the original radar. The improved correlation results from the flexibility of fuzzy logic in transforming location-based variables to probabilities.

Background

Estimating spatially distributed precipitation grids is a prerequisite to flood management and flood forecasting (Merz et al. (2014)). Hydrological models need basin-scale, spatially explicit precipitation data, among other inputs, to construct accurate flood forecasts (Todini et al. (2005)) for surface runoff management. Rain radar can produce such spatial precipitation distributions, however the challenges in calibrating and correcting for the various sources of error (detailed in Villarini et al. (2008)) create spatial and temporal uncertainty in the precipitation distribution (Cecinati et al. (2017), Krajewski and Smith, 2002). Nevertheless, the underlying motivation for research in improving precipitation maps rests in the needs of hydrological modeling and flood forecasting. Since weather radar became an accepted source of spatially distributed rainfall (Krajewski and Smith (2002), Morin et al. (2003)), extensive research has examined adjustment procedures to merge rain gauge observations with weather radar. Gauge data are accepted as reference observations (see for example Colli et al. (2013)), but represent point locations. Such point data can adjust weather radar grids through several geostatistic methods, reviewed and evaluated by Goovaerts (2000), Berndt et al. (2014) and McKee and Binns (2016). Kriging based methods have been examined by Kebaili Bargaoui and Chebbi (2009), Adhikary et al. (2017), and Ly et al. (2013). A comparison of various kriging methods where elevation was the secondary variable was done by Carrera-Hernández and Gaskin (2007). Another unique algorithm known as Conditional Merging, developed and evaluated by Sinclair and Pegram (2005), applies multiple kriging steps to achieve successful adjustment (Kim et al. (2007)) of weather radar grids.

1- Introduction

2- Methodology

3- Results

4- Discussion

5- Conclusions

References

Abstract

Uncertainty in capturing the effects of riparian tree shade for assessment of algal growth rates and water temperature hinders the predictive capability of models applied for river basin management. Using photogrammetry-derived tree canopy data, we quantified hourly shade along the River Thames (UK) and used it to estimate the reduction in the amount of direct radiation reaching the water surface. In addition we tested the suitability of freely-available LIDAR data to map ground elevation. Following removal of buildings and objects other than trees from the LIDAR dataset, results revealed considerable differences between photogrammetry- and LIDAR-derived methods in variables including mean canopy height (10.5 m and 4.0 m respectively), percentage occupancy of riparian zones by trees (45% and 16% respectively) and mid-summer fractional penetration of direct radiation (65% and 76% respectively). The generated data on daily direct radiation for 2010 were used as input to a river network water quality model (QUESTOR). Impacts of tree shading were assessed in terms of upper quartile levels, revealing substantial differences in indicators such as biochemical oxygen demand (BOD) (1.58–2.19 mg L−1 respectively) and water temperature (20.1 and 21.2 °C respectively) between ‘shaded’ and ‘non-shaded’ radiation inputs. Whilst the differences in canopy height and extent derived by the two methods are appreciable they only make small differences to water quality in the Thames. However such differences may prove more critical in smaller rivers. We highlight the importance of accurate estimation of shading in water quality modelling and recommend use of high resolution remotely sensed spatial data to characterise riparian canopies. Our paper illustrates how it is now possible to make better reach scale estimates of shade and make aggregations of these for use at river basin scale. This will allow provision of more effective guidance for riparian management programmes than currently possible. This is important to support adaptation to future warming and maintenance of water quality standards.

Introduction

The influence that riparian vegetation exerts on river water temperatures and light availability by intercepting incoming solar radiation has long been studied (Davies-Colley and Rutherford, 2005; Greenberg et al., 2012; Moore et al., 2005; Webb et al., 2008). Shading is a key parameter due to the control it exerts over the amount of direct radiation reaching the river surface making it an important consideration in water quality modelling and management. Solar radiation has direct effects on rates of primary production of both macrophytes and algae (Bowes et al., 2016, 2012b; Wood et al., 2012) which is important for river metabolic regime and is known to be influenced by riparian shade (Bernhardt et al., 2017). Water temperature also directly influences river fauna and dissolved oxygen concentrations. Therefore considerations of shading are of growing importance given the increasing stress on the water environment likely to arise under future climate. Effective and realistic riparian planting schemes to mitigate against these unwanted effects will become increasingly valuable and enhance water ecosystem services (Martin-Ortega et al., 2015). They will provide alternatives to traditional end-of-pipe solutions arising primarily from the EU Urban Wastewater Treatment Directive, which have been assessed through modelling (e.g. at large basin scale across Europe: Grizzetti et al., 2011).

Loss of the vegetative cover becomes the paying way of accelerated carbon emissions. It leads to increase in Land Surface Temperature (LST) and resultant resulting in global warming. This study has deployed remote sensing and GIS to observe LST variations in Islamabad to study vegetative cover loss for the time span of 25 years (1992e2017). The study’s findings confirmed 22% reduction in the vegetative cover from 1992 to 2000 with LST range 13e27
C/year. It was followed by continuous reduction of the said cover up-to 27% between 2000 and 08, confirming 16e34
C/year increase in LST. Likewise, from 2008 to 2017, alarmingly 51% vegetative cover loss contributing to 23e43
C/year rise in LST in the study area. The results confirmed correlation between loss of vegetative cover and LST that is contributing into global warming. So, attempts to halt massive urbanization so as to reduce the polluting gateways and increasing carbon sinks efforts through afforestation and reforestation remains essential to safeguard the humanity from climatic hazards.

Introduction

The 21st century’s dramatic changes in the climatic patterns became the biggest malice and severe threats to growth and development, yet alone human survival. Forests are the diverse environmental subjects, which have the capacity to adapt to the changes in the ecosystem and provide positive sustainable returns to the environment (Aljerf, 2017). But there is great threat to forest cover und the broader domain of vegetative cover on the land surface and the main source is humans. The reported adverse impacts erosion, drought and loss of biodiversity (Xie et al., 2017; Gao et al., 2017). Similarly, human-induced activities remain the main causes behind the loss of forest cover, e.g., removal of forests and/or reduction in the capacity of the forests to serve as carbon sinks (Zhang et al., 2015). The other and the most severe reason behind the loss of forest cover and there-upon massive carbon release is the rapid urbanization (Bhatt, 1990). Here, urbanization is amongst the critical factors affecting forest cover that is posing severe impacts on both regional and global environment (Lin et al., 2019). Thus, urban settlements, amongst other major threats, are posing increasing challenge as land cover change is occurring from forested land, high coverage rate grassland etc. to bare land and massive physical infrastructure development. It therefore decreases the net decrease in the rate of grass and forest land as of growing human activities (Li et al., 2017). Thus, land use change as of conversion of forest land effects the terrestrial ecosystems and thereupon the carbon storage, thus leading to anthropogenic carbon emissions. The transfer of land to build high land infrastructures in urban settings is accounted for the large scale conversion of land and associated increased carbon emissions (Chuai et al., 2015).

In the task of using deep learning semantic segmentation model to extract water from high-resolution remote sensing images, multiscale feature sensing and extraction have become critical factors that affect the accuracy of image classification tasks. A single-scale training mode will cause one-sided extraction results, which can lead to ‘‘reverse’’ errors and imprecise detail expression. Therefore, fusing multiscale features for pixel-level classification is the key to achieving accurate image segmentation. Based on this concept, this paper proposes a deep learning scheme to achieve fine extraction of image water bodies. The process includes multiscale feature perception splitting of images, a restructured deep learning network model, multiscale joint prediction, and postprocessing optimization performed by a fully connected conditional random field (CRF). According to the scale space concept of remote sensing, we apply hierarchical multiscale splitting processing to images. Then, we improve the structure of the image semantic segmentation model DeepLabV3+, an advanced image semantic segmentation model, and adjust the feature output layer of the model to multiscale features after weighted fusion. At the back end of the deep learning model, the water boundary details are optimized with the fully connected CRF. The proposed multiscale training method is well adapted to feature extraction for the different scale images in the model. In the multiscale output fusion, assigning different weights to the output features of each scale controls the influence of the various scale features on the water extraction results. We carried out a large number of water extraction experiments on GF1 remote sensing images. The results show that the method significantly improves the accuracy of water extraction and demonstrates the effectiveness of the method.

Introduction

Accurate extraction of water information from remotesensing images has always been an important research topic in the field of remote sensing image analysis because it plays a vital role in national land/water resource monitoring and environmental protection. The traditional remote sensing image water body extraction methods are divided into two main types. One is based on the spectral characteristics of water bodies and involves setting different thresholds and using a water body index to classify and extract water information [1]–[3]. The other is based on mathematical morphology [4] and segments water body edges [5], [6] to extract their features. Among the two types of methods, the index method (NDWI), which is a spectrum-photometric method, is widely used in the task of water extraction because of its simplicity and universality. In actual appliances, especially in the large-scale daily monitoring of water body nationwide, it is inevitable to process massive remote sensing images. The traditional methods with more manual intervention are not able to guarantee the quality of data products. In particular, there is substantial random interference in images, such as clouds, shadows, fog, etc. Even if some models have good generality, there will also be an extraction loss on the details of water.

Object detection is one of the crucial tasks in computer vision. In the past few years, the performance of object detection [1-14] has dramatically improved due to the success of deep convolutional neural networks (CNN). Typically, object detection and recognition involve two steps: first, deep neural networks are used to localize the potential location of each target object; then, objects are classified into appropriate classes. If the first step can effectively localize the potential object, the second step will be easier. Even though the two-step approach achieved state-of-the-art performance, the running times are usually slow [11]. Therefore, one-stage detectors have been developed to improve the speed. Small-object detection remains challenging because small objects usually have lower resolution and less context information. Finding a 20 × 20 size object located in a 5000 × 5000 image is a difficult task, even for humans. As described in the literature, state-of-the-art methods for object detection usually performed poorly on small objects [11]. Recent research has shown the importance of context information and scale for small-object recognition [8][9]. In addition, it has been reported that lower-layer features extracted from CNNs are very useful for small-object detection and segmentation [8-11]. The work presented in this paper focuses on low-resolution small-object detection by evaluating the performances of leading deep learning methods using a common dataset, which is a new dataset for bird detection, called Little Birds in Aerial Imagery (LBAI). This dataset was created from real-life aerial imagery data, provided by the Illinois Natural History Survey at the University of Illinois at Urbana-Champaign. LBAI contains images of waterfowl and other water birds in shallow lakes within the Illinois River Valley. LBAI includes different colors, shapes, poses, resolutions, and bird sizes range from 10px to 40px. The dataset contains different backgrounds of rivers, vegetation, land, and mixtures between each type of background. Overall, LBAI captures the diversity of real-life situations for bird detection in shallow lake and wet lands across the Midwest. Some of the birds have larger sizes, in higher resolutions and homogenous backgrounds, which make them easier to be identified. While others have smaller sizes, in lower resolutions with blurry contours, making them hard to be detected. LBAI is designed to identify the difficulties and improve existing methods on small object detection. Using the LBAI dataset, we compared a wide-range of representative state-of-the-art deep learning methods. The results shed a light on the strengths and weaknesses of different deep neural network architectures for small object detection. The contributions of this research include applying and adapting leading deep-learning methods to the LBAI dataset, evaluating performances of these methods on a common benchmark dataset for small-object detection and segmentation, and automating the time-consuming process of manual image processing from waterfowl surveys.

Due to rapid urbanization in developing world, city administrators and urban planners are challenged with problems in delivering basic services like green space, sanitation, water supply, transportation, and primary health center. The urban green spaces’ coverage is not adequate to meet people’s needs due to population pressure, expansion of industry, and construction in the process of urbanization (Cetin 2015a, b, c). Urban planners have to visualize such growth during planning, policy, and decision-making. Impulsive construction of unplanned informal houses in and outside the administrative boundaries of towns and cities is common in developing countries. These insightful changes cause a change in land use type which makes urban planning foreseeable (Zewdu 2011). Urban planning does not only cover matters of the built environment such as housing and transportation network, but also the integration of green spaces into the physical urban landscape. The main indicators of planned urbanization expecting physical and socioeconomic development in terms of the suitable areas for health, where bioclimatic comfort arises as one of the most significant factors (Cetin 2015a, b, c). The bioclimatic comfort situations can be established with the design criteria of landscape architecture and planning (Cetin et al. 2016). In recent years, Addis Ababa and its surroundings have observed unprecedented land degradation as a consequence of deforestation for firewood supply and human settlement. Big tracts of the city’s vegetation area are transformed to permanent structures at a rate of 6.65 km2 /year (Tamiru et al. 2005). About 10,000 ha of land initially reserved for green spaces have been allocated for other uses (Tadesse 2010). Over the years, Addis Ababa witnessed ecosystem degradation and vegetation cover is estimated at 14.6% (AAEPA 2005). In the city, 382 ha of riparian area is being cultivated by vegetable growing farmers, which cover up to 7.1% of the city’s vegetable demand (Azeb 2007). Land suitability analysis is the process to determine whether the land resource is appropriate for some specific uses and to determine the suitability level by considering different factors such as LULC (land use and land cover) type, landscape, and road infrastructure (Manlun 2003). Suitability analysis of different LULC patterns and dynamics is important to determine the most desirable site for future development. Identifying appropriate suitability parameters are the foundation of construction of suitability analysis. Green spaces are land uses that are covered with natural or man-made vegetation in the built-up areas and planning areas (Manlun 2003). The meaning of the green space system has also been continuously emerging with the development of city theory, which mainly encompasses horticultural, ecological, and spatial meanings. GIS has the main role in land-use suitability analysis for green space. In the tourism sector also, maps have vital role to protect natural and cultural value in terms of time and cost efficiency and deliver very accurate and up-to-date data (Cetin 2015a, b, c).

Urban areas are the hot spots of environmental change, since urbanization alters the land use and cover, biodiversity and hydrological systems from local to regional scales (Lambin et al., 2003; Grimm et al., 2008; Kuang, 2011; Butsch et al., 2017). Urbanization is positively correlated with economic development (Birch and Wachter 2011), but rapid and unplanned urbanization involves in breakdown of natural and social cohesion and can be regarded as a destructive process. Sri Lanka is experiencing a speedy urbanization over the last decades. According to a report (United Nations, 2012), the urban population of Sri Lanka will reach to 60% by 2030 from 14% in 2010. This rapid growth in urban population, unless handled properly, may create serious socio-economic disparities which are hard to fix. However, a well-planned and managed urbanization empow ers the nation’s economic development and provides better access to employment, education and health care to citizens. Thus, the Sri Lankan planners need new tools to monitor and project the urban growth into the near future to plan for better and more sustainable cites. In this context, remote sensing and Geographical Information Systems (GIS) can be used extensively to map and manage the rapidly growing urban areas (Bhatta, 2009; Kuang, 2012; Kantakumar et al., 2016). The urban growth models coupled with GIS can be useful to simulate the urban growth of cities to understand the pattern of urban growth (Batty et al., 1999; Stevens et al., 2007; Al-shalabi et al., 2013). Recent advancements in complexity science have given birth to a new age urban models based on Cellular Automata (CA) and Agent Based Models (ABM) (Sohl and Claggett, 2013). These models are capable of projecting the city growth into the near future both spatially and temporally. The CA models are an added advantage to ABM models, because of their simplicity and close resemblance to grids used in GIS (Su, 1998; Kantakumar et al., 2011). CA is a powerful simulation tool which assumes the land system is self-organized, heterogeneous and based on neighboring rules.

Floods are among the most common natural hazards which have caused great casualties and property losses over the past decades [1]. Hydrological modelling greatly contributes to flooding prediction and provides insights into the relationship between climate factors and hydrological cycle operating over a range of spatial and temporal scales [2]. Therefore, it is of great significance to model the hydrological features to predict the flooding events and to reduce the property losses. Effective hydrological modelling requires a deep understanding of catchment water balance. The reason for that comes from two aspects. First, the catchment water balance impacts on the water yield of catchments [3]. Second, the climatic variability leads to conflicts that correlate with freshwater supply [4]. Hydrological modelling bridges the gap between climatic factors and catchment water balance, and they can be generally classified into two types: conceptual hydrological models and distributed physicallybased models. Conceptual models deal with hydrological processes based on some simple mathematical formulas without explicit treatment of spatial heterogeneity in underlying surface [5]. Typical models are Storm Water Management Model (SWMM), Xin’an Jiang Model and Hydrologiska Byrans Vattenbalansavdelning (HBV) [6]. The distributed physically-based models represent each component of the hydrological cycle using their physical governing equations, with typical examples of Soil and Water Assessment Tool (SWAT), Variable Infiltration Capacity (VIC) and the Coupled Routing and Excess Storage (CREST) model [7]. Compared to conceptual hydrological models, the distributed models get more popularity and are more widely used in realistic applications. The distributed models simulate the spatio-temporal variations of hydrological cycle components on distributed grid cells and account for the intra-basin variability of runoff-producing mechanisms [8]. Unfortunately, distributed hydrological models rely on sufficient reliable geospatial data as the model input. That adversely hinders their potential applications in ungauged or poorly gauged regions. In contrast, the CREST has good integration capability and can be well compatible with satellite remote sensing big data, making it advantageous to be used in ungauged or poorly gauged regions. Another key point of hydrological modelling is to accurately estimate the precipitations in the region. Generally, three main approaches are available to measure precipitation, rain gauges, weather radars and satellite-based sensors [9]. Compared with conventional rain gauges and weather radars, satellites remote sensing is a more advantageous approach. Advances in remote sensing data with quasi-global coverage, continuity in time and easy access make it a cost-effective source of estimating precipitations for hydro-climatology studies in regions with sparse ground based networks [10]. One representative of satellite remote sensing big data product is the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product, which is intended to be combined with passive microwave (PMW) and infrared (IR) observations to provide the ‘‘best’’ estimate of quasiglobal precipitation [11-14]. TRMM has been becoming one of the most important and widely-used precipitation products of satellite remote sensing big data and provided tremendous information for hydrological applications [15-18].

A primary goal of urban forestry planning and management is reaching at least minimum levels of tree cover density (Nowak et al., 2010). Accurate measures of tree cover density are needed so planners know where to plant trees and how many trees to plant. Tree cover density is most often measured using remotely-sensed imagery (e.g., rectified satellite imagery). Google Earth produces high-quality remotely-sensed images of most places on earth that can be used to objectively calculate tree cover density at little or no cost to the user. This combination of no cost and easy access has made the use of remotely-sensed images for measuring tree cover density and for procuring information about a site ubiquitous in design and planning circles (Janssen & Rosu, 2012; Sheppard & Cizek, 2009). Given the advantages provided by remotely-sensed images, it is likely that many designers and planners feel that they can assess a site and make rational decisions about it by relying on Google Earth imagery rather than on visiting a site and using other eyelevel methods (Power, Neville, Devereux, Haynes, & Barnes, 2013). In recent years, tree cover density measurements derived from remotely-sensed images have been widely adopted to evaluate and compare tree cover density at the scale of states (Nowak & Greenfield, 2012a), cities (McPherson, Simpson, Xiao, & Wu, 2011; Nowak & Greenfield, 2012b), and communities (Kardan et al., 2015; Klemm, Heusinkveld, Lenzholzer, & van Hove, 2015). Eye-level tree cover density measurements are used much less often. Eye-level measures include calculating tree coverdensity fromeye-levelphotography (Jiang, Chang, & Sullivan, 2014) and asking landscape experts or ordinary people to subjectively rate tree cover density from eye-level photographs of actual landscape scenes (Jiang, Larsen, Deal, & Sullivan, 2015; Nordh, Hartig, Hagerhall, & Fry, 2009). It is plausible, however, that eye-level photography, especially panoramic photography that has a similar visual scope to human vision, may better represent people’s perceptions of landscapes than remotely-sensed imagery, and may be a better tool than remotely-sensed imagery in efforts to understand the impact of urban forestry on human health and well-being. Many studies have reported strong, positive associations between the density of vegetation in urban landscapes and the health and wellbeing of individuals (Li & Sullivan, 2016; Parsons, Tassinary, Ulrich, Hebl, & Grossman-Alexander, 1998; Ulrich, 1984; van den Berg, Koole, & van der Wulp, 2003). Previous studies suggest, moreover, that eye-level photographs are more accurate at measuring tree cover density and procuring information about a site than remotely sensed imagery (Kweon, Ellis, Lee, & Rogers, 2006; Leslie, Sugiyama, Ierodiaconou, & Kremer, 2010). To our best knowledge, no empirical studies have examined the extent of agreement among remotely-sensed and eye-level methods of measuring tree cover density at the site scale. In addition, it is unclear whether remotely-sensed or eye-level methods would be better in certain circumstances (such as when tree cover density is low or high). To what extent can designers and planners eschew traditional forms of procuring information about a site (e.g., by measuring tree cover density from eye-level photographs or walking the site) and rely instead on information conveyed by remotely-sensed images? Should municipal officials establish standards of tree cover density in cities by relying on information conveyed solely by remotely-sensed images?

حوزه مطالعاتی فعلی، در منطقه متا رابی ناحیه شوآی غربی در ایالت منطقه ای ارومیای اتیوپی واقع شده است. هدف اصلی مطالعه ی فعلی، ارزیابی ناحیه بندی خطر زمین لغزه (LHZ) با استفاده از رویکردهای “جایگذاری شبکه” و “مدلسازی GIS ” بود. از این گذشته، سعی شد اثر بخشی دو روش، شناخته شود. متدولوژی دنبال شده مبتنی بر تحلیل زمین لغزه های گذشته در ناحیه بود. برای مطالعه کنونی شش عامل سببی بویژه مواد شیب، شیب، جهت شیب، زاویه فراز، کاربری زمین و پوشش زمین و آثار سطحی آب های زیر زمینی، مد نظر قرار گرفتند. بعدها، شاخص آمادگی زمین لغزه (LSI) بر مبنای تاثیر نسبی عوامل سببی بر زمین لغزه ها، محاسبه شد. برای روش “جایگذاری شبکه ای”، یک شبکه ی دارای سلولهای m10 در m 10 روی ناحیه ی مورد مطالعه جایگذاری شد و بعد برای ترسیم دسته های فرعی هر عامل سببی، مورد پردازش زمین شناسی قرار گرفت. ارزشهای LSI به هر عامل سببی فرعی در هر خانه شبکه اختصاص داده شد و یک “شاخص آمادگی خطر زمین لغزه ی کلی” برای تولید نقشه LHZ محاسبه شد. برای مدلسازی GIS، عوامل سببی یکسان و ارزشهای LSI مشابه بکار رفت. در مورد نقشه  LHZ تهیه شده توسط روش “جایگذاری شبکه ای” در مورد 82% از زمین لغزه های گذشته، بین نواحی “بسیار پر خطر” یا نواحی ” با خطر بالا” قرار میگیرند در حالیکه مورد “مدلسازی GIS” در مورد 95% زمین لغزع ها در نواحی “بسیار پر خطر” یا “با خطر بالا” واقع میشود. نهایتا، این ارزیابی نشان داد “مدلسازی GIS” نقشه ی LHZ بهتری را ایجاد نمود. از این گذشته، روش “جایگذاری شبکه” در مقایسه با مدلسازی GIS کسل کننده تر و زمان برتر است.