key: cord-290003-pmf7aps6 authors: Avtar, Ram; Komolafe, Akinola Adesuji; Kouser, Asma; Singh, Deepak; Yunus, Ali P.; Dou, Jie; Kumar, Pankaj; Gupta, Rajarshi Das; Johnson, Brian Alan; Thu Minh, Huynh Vuong; Aggarwal, Ashwani Kumar; Kurniawan, Tonni Agustiono title: Assessing sustainable development prospects through remote sensing: A review date: 2020-09-03 journal: nan DOI: 10.1016/j.rsase.2020.100402 sha: doc_id: 290003 cord_uid: pmf7aps6 The Earth's ecosystems face severe environmental stress from unsustainable socioeconomic development linked to population growth, urbanization, and industrialization. Governments worldwide are interested in sustainability measures to address these issues. Remote sensing allows for the measurement, integration, and presentation of useful information for effective decision-making at various temporal and spatial scales. Scientists and decision-makers have endorsed extensive use of remote sensing to bridge gaps among disciplines and achieve sustainable development. This paper presents an extensive review of remote sensing technology used to support sustainable development efforts, with a focus on natural resource management and assessment of natural hazards. We further explore how remote sensing can be used in a cross-cutting, interdisciplinary manner to support decision-making aimed at addressing sustainable development challenges. Remote sensing technology has improved significantly in terms of sensor resolution, data acquisition time, and accessibility over the past several years. This technology has also been widely applied to address key issues and challenges in sustainability. Furthermore, an evaluation of the suitability and limitations of various satellite-derived indices proposed in the literature for assessing sustainable development goals showed that these older indices still perform reasonably well. Nevertheless, with advancements in sensor radiometry and resolution, they were less exploited and new indices are less explored. The success of sustainable development in any region depends upon what is known regarding 49 resource management and hazards in the area (Tabor and Hutchinson, 1994) . Although 50 several approaches and techniques are available to monitor natural resources and hazards, 51 remote sensing (RS) technology has been particularly popular since the 1970s because of its 52 low acquisition costs and high utility for data collection, interpretation, and management. 53 Over the past few decades, RS tools and techniques have been deployed for several purposes 54 at various time scales (Jensen, 1996) . RS provides both archived and near-real-time 55 information on Earth systems (Jensen, 1996; Jensen and Cowen, 1999) . RS is applied to 56 obtain spatial information in various fields in Earth system science. The ability of RS to 57 monitor Earth systems at various spatial and temporal scales makes it suitable for addressing 58 global environmental, ecological, and socioeconomic challenges. RS can provide a synoptic 59 view of spatial information at local, regional, and global scales, thus facilitating swift 60 decision-making and action (Jensen and Cowen, 1999) . As information can be obtained 61 directly through RS, it is the main surveying technology employed for collecting data in 62 inaccessible and remote locations. Based on these RS data, forest fragmentation, land use and cover, and species distributions 211 have been mapped and monitored over time (Kerr et al., 2001; Menon and Bawa, 1997) . 212 LULC data are especially useful for detecting the distributions of individual species, species 213 assemblages, and species richness over broad areas (Kerr and Ostrovsky, 2003 RS can also be used to derive environmental parameters or indices indirectly, to in turn 223 map species patterns and diversity (Turner et al., 2003) . Such parameters are thought to be 224 drivers of biodiversity, and those that are frequently estimated for determining species 225 richness and distribution patterns include (i) primary productivity, (ii) climate variables, and 226 (iii) habitat structure (Abdalla, 2012). These three types of parameters facilitate assessment 227 of the diversity of various species at any given location and time (Turner et al., 2003) . 228 Parameters can first be estimated from data obtained by advanced RS sensors; then, both 229 local and global species availability, richness, and diversity can be inferred. 230 The capability of remote sensing application for mineral exploration was started from 369 the passive satellite sensors to active sensors. In the past decades, several studies have been 370 done towards (1) The mapping of geology and structures (the faults and fractures) that hosts 371 ore deposits; (2) Identifying hydrothermally altered rocks based on their spectral signatures; 372 (3) Mapping surface distribution of rocks and its mineral constituents (Sabins, 1999) . Sabins, 373 (1999) In some areas such as mountainous areas in developing countries with a lack of in-situ 468 measurement devices. It is difficult to simulate hydrodynamic models due to a lack of data. The principle behind the usage of the InSAR is the acquisition and processing of phase shift 635 information obtained from a series of complex SAR images. In this case, every pixel element 636 from each image is processed and the elevation at its centroid is established based on signal 637 phase response and the satellite altitude information (Rosen et al., 2000) . 638 The Table 4 shows 670 summary of the studies relevant to applications of remote sensing in transportation. 671 672 An important aspect of sustainable development is the understanding of the dynamics of 674 the population within a community and across national boundaries. This information assists industrialization, and socio-economic development. The population also plays a substantial 677 role in our ability to measure the extent of human influence on the environment. 678 Demographic data is measurable and quantifiable, which lends itself to applications in 679 remote sensing. From an economic perspective, the population is one of the determinants of 680 demand. An increase in the population invariably increases the aggregate demand within a 681 country. There are a few ways to go about using remote sensing techniques to count the 682 population. Jensen and Cowen, (1999) imagery against population census data. They found that high-resolution satellite images do 690 not correlate strongly enough with the population data to serve as a proxy for population data. 691 They also only found a weak correlation between landscape textures and population density. 692 As census data is already being collected through surveying methods on the ground, there is 693 less necessity for remote sensing applications in population estimation. There is a clear 694 distinction in the literature between allocation and estimation. Despite the prevalence of 695 population census data, this type of information does not give significant insight into how 696 these people are spatially arranged. The population has been recognized as an indirect driver 697 of land-use change though its effect cannot be explicitly stated (Meyer and Turner, 1992 principle that people tend to cluster, and in the model, the population density is greatest at the Indianapolis, Indiana. They found that remote sensing-based models that stratified the 711 population according to density levels increased the accuracy of the model. They cited the 712 issue that the census data is of a lower resolution than the remotely sensed data. They also 713 found that the inclusion of textures, temperatures, and spectral responses greatly increased 714 the accuracy of estimation. 715 The remotely sensed data must be combined with in-situ data to ensure accuracy. 716 However, literature seems to agree that, measurements of population density using remote 717 sensing have not been carried out consistently due to the large degree of variation between 718 communities. More technologically developed countries can remotely sense population 719 allocations. Japan, for example, has access to positioning data obtained from smartphones. 720 This knowledge was applied when the 2011 Tohoku Earthquake struck, providing insight as 721 to where the highest concentrations of people were in real-time in the midst of the disaster. 722 However, the use of this type of remotely sensed data has raised a lot of concerns if it is to be 723 used in the field of research because for many people it represents a privacy breach. Table 4 vulnerability against poverty rates. One of the issues that they experienced is that poverty identified, and it is likely that they differ from place-to-place. 746 Extending the studies on poverty, in recent years, scientists have seen the need to shift 747 away from a static poverty mapping model and move towards a more dynamic one. Rogers et 748 al., (2006) Although there is some evidence that about the spatial trend as shown by high levels of 755 contiguity in three clusters: the Peloponnesian region, the islands of the Dodecanese and 756 Crete. This study was not conclusive, yet it highlights one of the main issues of 757 socio-economic applications of remote sensing. Still, accurately attributing a precise number 758 of people to a small spatial designation has been observed consistently and needs further 759 investigation. 760 In some literature, environmental quality has been used interchangeably with the quality 761 of life. This refers to the perception of the quality of the natural environment is integrated into 762 the human environment such that the human population actively interacts with and perceives. 763 Lo and Faber (1997) were able to show that there is a linkage between income, population 764 density, and forest amenities measured by leaf area. They found that higher levels of 765 greenness were positively correlated with income and median home data and negatively 766 correlated with population density. Pozzi and Small (2001) suggested that using greenness to 767 determine levels of affluence can lead to ambiguous results because greenness can be 768 indicative of either high or low levels of affluence. At this point, they agree that the 769 stratification between urban, rural, and suburban locals greatly increases the accuracy of 770 results. 771 It can be seen from Table 5 that most of the indices applied in sustainable developmental 798 studies were developed a long time ago when the sensor radiometric resolution and spatial 799 resolution was lower than the present ones. Despite that, these indices performed well such as 800 NDVI, EVI, and LAI, etc. Today a large number of satellites orbiting outer space today with 801 a narrower range of radiometric resolution. It provides an improved spatial resolution and 802 increased availability of SAR data in multiple frequencies. However, lesser attempts have 803 been made for developing new spectral indices capable of retrieving more accurate 804 information from remote sensing products. Therefore, there is a need to explore new indices 805 for continued development in attaining sustainable development through remote sensing 806 area from the latest sensors to improve Earth's monitoring. 808 • The biggest challenges associated with the remote sensing itself is the 810 availability and distribution of data. Lack of freely available high-resolution 811 remote sensing data makes the remote sensing research community debilitated 812 despite the fact that advanced remote sensing tools have become available for 813 processing and analyzing the data. In cases, where the high-resolution data is 814 available commercially, their cost is not affordable to many researchers, 815 especially those from economically weaker countries. 816 • Lack of effective national spatial data infrastructures (SDI) in developing 817 countries prevent access to data and information for analysis and validation. 818 • Due to the inherent shortcomings of remote sensing devices in measuring the 819 underground conditions directly, inferential methods are sometimes adopted, 820 however, such methods suffer from limited accuracy in many cases, especially in 821 groundwater exploration. 822 • Mapping of lake bodies in glaciated areas using various indices are still difficult 823 because of the similar behavior of reflectance from adjoining areas. 824 • Since turbidity varies largely between the aquatic systems, generic algorithms for 825 water quality mapping introduces error value of more than 10% in low to 826 moderately turbid waters. The error in highly turbid water is much more. 827 • Use of hyperspectral data for mineral mapping has high potential, however the 828 availability of hyperspectral sensor data is limited. 829 • Mapping of surface mineralogy with remote sensing under forest canopy in 830 tropical rainforests region of the world remains difficult. 831 • Although there are advanced algorithms for mapping snow cover, remote sensing 832 of snow can be extremely difficult due to mixed pixels arising from cloud cover. 833 • Availability of clouds free satellite data during the event of floods is still 834 challenging in tropical region. High-temporal resolution SAR remote sensing is 835 the viable solution. 836 • Apart from mapping the flood extent and water depths, derivation of flood water 837 characteristics such as flow velocity, sediments load, warning time and 838 awareness, winds and duration of inundation from the integration of satellites space and time (Merz et al., 2010) . 841 • More accurate and open-access precipitation, discharge, boundary data and 842 topography at the global level are needed to increase dependability of flood 843 hazard modeling. 844 • Lack of ground data for validation in data scarce regions often affect the 845 reliability of satellite-based rainfall data. 846 • Satellites that currently employ rainfall measurements are available only at 847 coarser resolution, which limits the rainfall threshold -landslide initiation 848 mapping in the ungauged catchments. 849 • Separating the landslide initiation and deposition areas are challenging even with 850 3 m resolution Planet images (Wang et al., 2019) . 851 • Estimation of income distribution from remote sensing data still remain a 852 challenge in understanding the quality of life. 853 • Population estimation using remote sensing data without ground measurement 854 remain a difficult task. 855 • Sustainable transportation mapping and analysis in developing countries is 856 greatly affected by the availability, cost, licensing and access to high resolution 857 real-time imageries and image processing software. 858 • Effective communication between remote sensing experts and decision makers 859 on the effective use of remote sensing for human welfare issues is lacking in most 860 developing countries. 861 This paper reviewed how RS technologies have been used to support several aspects of 863 sustainable development, including (1) natural resource monitoring, development, and 864 management; (2) environmental assessments and hazard monitoring; and (3) socioeconomic 865 development. RS has several advantages, including the ability to provide global-scale 866 coverage, high-resolution data, and multi spatio-temporal coverage with optical, SAR, 867 thermal and LiDAR sensors. It provides large volume of data and recent development in ML 868 algorithms can handle large volume of geospatial data to extract beneficial information. 869 Here, we discussed the use of RS for sustaining the Earth and human life. With the 870 development of new and improved satellite and airborne sensors, data with increasingly 871 higher spatial, spectral, and/or temporal resolution will become available for researchers, decision-making in many areas of sustainable development. Accordingly, the United Nations 874 highlighted RS as an indispensable tool for achieving its Sustainable Development Goals 875 (SDGs). RS can be used not only to develop comprehensive policies promoting sustainable 876 development, but also for effective implementation, monitoring, and decision-making. 877 However, for RS to be effective and reliable, adequate information has to be obtained from 878 other sources. In particular, the development of new spectral indices based on improved 879 sensor technology is key for achieving sustainable development goals. 880 Spatial data from RS and other sources can be integrated using GIS, among other 881 spatial-integration tools, to analyze global environmental processes and change. During the 882 COVID-19 global crisis, the contribution of remote sensing data has been widely discussed 883 in a wide variety of applications including monitoring water and air pollution, management 884 of the threat, monitoring traffic patterns, measuring human and economic activities, and 885 socio-economic restriction. Several new studies and applications of remote sensing are 886 emerged during the pandemic and are becoming significant case studies for sustainability 887 applications. 888 For developing countries, however, obtaining RS data for research and development 889 purposes is difficult; thus, it is not efficient to use RS technology to support sustainable 890 development in such countries. As counters strive to achieve the SDGs, more data acquisition 891 platforms should be created and made available to researchers in developing nations to 892 enable them to actively use RS data to support national, regional, and global sustainable 893 development. RS techniques are still not widely employed in developing countries, which are 894 more vulnerable to natural hazards. There may also be conflicts of interests in terms of 895 security and privacy between governments and other entities associated with RS use. 896 Additional collaborations between policy think tanks, decision-making bodies in developing 897 countries, and countries or organizations with ready access to GIS resources are needed. 898 Remote Sensing for Sustainable Forest Management Detecting areas of high-potential gold 1059 mineralization using ASTER data A narrow-waveband spectral index that 1061 tracks diurnal changes in photosynthetic efficiency. 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