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林珲，张鸿生.2021.热带与亚热带遥感：需求、挑战与机遇.遥感学报，25（1）： 276-291
Lin H and Zhang H S. 2021. Tropical and subtropical remote sensing： Needs， challenges， and opportunities. National Remote Sensing Bulletin， 25（1）：276-291
热带与亚热带拥有大量丰富的自然资源，同时也正在经历着快速的城市化进程，其资源、生态、环境等都面临着前所未有的挑战。同时，热带与亚热带地区存在着大量的自然灾害（如台风、干旱、地震等），威胁着该地区人类经济社会的可持续发展。应用遥感技术对热带与亚热带区域进行全面的监测，对于热带与亚热带区域甚至全球的可持续发展具有重要的意义。然而，由于热带与亚热带特殊的地理条件（如多云多雨等），遥感监测需要克服特殊的技术挑战。本文通过Web of Science核心数据库的7594篇研究论文进行分析，综述了热带与亚热带遥感的研究现状，分别从热带与亚热带遥感的需求、现状、挑战和机遇，通过共被引文献分析和主题词频率分析，建立共被引文献网络和主题词网络，并通过非监督机器学习进行聚类，分别识别出22个共被引文献聚类和6个主题词聚类。通过对这些共被引文献类别和主题词类别的深入分析，本文总结了：（1）热带与亚热带遥感研究的主要监测对象，包括城市地表、热带雨林、红树林、珊瑚、热带草原、生物多样性和自然灾害；（2）热带与亚热带遥感主要采用的遥感技术，包括：遥感数据的选择和使用、遥感数据分析的方法、多云多雨的问题应对以及多源遥感技术。最后，从现代遥感技术的快速发展，本文从8个方面讨论热带与亚热带遥感面临的挑战和未来发展的机遇。
Tropical areas are located between the Tropic of Cancer where the sun can directly shine
while subtropical areas are roughly between 40° North and South and the Tropic of Cancer (23° 26 min North and South). The tropical and subtropical regions have rich natural resources while undergoing rapid urbanization. Thus
the ecology and environment in these regions are facing unprecedented challenges. Meanwhile
a large number of natural disasters (e.g.
typhoons
droughts
and earthquakes) occur in tropical and subtropical regions
and they threaten the sustainable development of human society and the economy in these areas. The application of remote sensing technologies to comprehensively monitor the tropical and subtropical regions is important to the sustainable development of these areas and even the world. However
remote sensing monitoring needs to overcome special challenges due to the complex geographic conditions of tropical and subtropical regions (e.g.
cloudy and rainy throughout the year).
This review analyzed 7594 research papers from the Web of Science Core Database and summarizes the state of the art of tropical and subtropical remote sensing
including the demand
status
challenges
and opportunities of tropical and subtropical remote sensing. The techniques of co-citation analysis and term frequency analysis were applied by investigating the clustering characteristics and patterns of the 7594 papers through their lists of references and the frequent terms in the title
abstracts
and keywords. A co-citation relationship network and a term frequency network were established to identify the clusters of research by unsupervised machine learning methods.
A total of 22 co-citation clusters and 6 subject clusters were identified. Through an in-depth analysis of these categories
this study summarized (1) the major application topics of tropical and subtropical remote sensing
including urban land surface
tropical rain forest
mangrove
coral
tropical grassland
biodiversity
and natural disasters; and (2) the main remote sensing technologies used in tropical and subtropical regions
including selection of remote sensing data
remote sensing data analysis techniques
solutions to overcome cloud contaminations
and multi-source remote sensing technologies.
From the rapid development of modern remote sensing technologies
this study discusses the challenges and future opportunities of tropical and subtropical remote sensing from eight different aspects. (1) Cloud detection
cloud restoration
and sub-pixel unmixing technologies promote the better applications of optical remote sensing in tropical and subtropical areas. (2) Multi-source and -modal fusion technology will bring new opportunities to overcome the problem of cloud contaminations in tropical and subtropical regions with the increasing availability of SAR remote sensing data. (3) Long time-series fusion technology can be used to monitor the tropical and subtropical regions with high temporal resolution and medium-to-high spatial resolution images. (4) New-generation observation satellites from China
Europe
Japan
and United States
as well as aerial remote sensing and UAV platforms
have provided great opportunities. Cloud computing platforms facilitate a long-term comprehensive analysis of full-coverage datasets in tropical and subtropical regions. (5) The in-depth application of tropical and subtropical remote sensing promotes the formation of a more comprehensive interdisciplinary method in Earth system science and sustainable development.
遥感热带亚热带热带雨林红树林珊瑚大草原自然灾害
tropicalsubtropicalrainforestmangrovescoralssavannanatural disasters
Ajmar A, Boccardo P and Tonolo F G. 2011. Earthquake damage assessment based on remote sensing data. The Haiti case study. Italian Journal of Remote Sensing, 43(2): 123-128 [DOI: 10.5721/ItJRS20114329http://dx.doi.org/10.5721/ItJRS20114329]
Alam K, Trautmann T, Blaschke T and Subhan F. 2014. Changes in aerosol optical properties due to dust storms in the Middle East and Southwest Asia. Remote Sensing of Environment, 143: 216-227 [DOI: 10.1016/j.rse.2013.12.021http://dx.doi.org/10.1016/j.rse.2013.12.021]
Asner G P, Carlson K M and Martin R E. 2005. Substrate age and precipitation effects on Hawaiian forest canopies from spaceborne imaging spectroscopy. Remote Sensing of Environment, 98(4): 457-467 [DOI: 10.1016/j.rse.2005.08.010http://dx.doi.org/10.1016/j.rse.2005.08.010]
Asner G P and Warner A S. 2003. Canopy shadow in IKONOS satellite observations of tropical forests and savannas. Remote Sensing of Environment, 87(4): 521-533 [DOI: 10.1016/j.rse.2003.08.006http://dx.doi.org/10.1016/j.rse.2003.08.006]
Avtar R, Kumar P, Oono A, Saraswat C, Dorji S and Hlaing Z. 2017. Potential application of remote sensing in monitoring ecosystem services of forests, mangroves and urban areas. Geocarto International, 32(8): 874-885 [DOI: 10.1080/10106049.2016.1206974http://dx.doi.org/10.1080/10106049.2016.1206974]
Bagaram M B, Giuliarelli D, Chirici G, Giannetti F and Barbati A. 2018. UAV Remote Sensing for Biodiversity Monitoring: Are Forest Canopy Gaps Good Covariates?. Remote Sensing, 10: 1397 [DOI: 10.20944/preprints201807.0209.v1http://dx.doi.org/10.20944/preprints201807.0209.v1]
Beloconi A, Kamarianakis Y and Chrysoulakis N. 2016. Estimating urban PM10 and PM2.5 concentrations, based on synergistic MERIS/AATSR aerosol observations, land cover and morphology data. Remote Sensing of Environment, 172: 148-164 [DOI: 10.1016/j.rse.2015.10.017http://dx.doi.org/10.1016/j.rse.2015.10.017]
Blaschke T, Hay G J, Weng Q H and Resch B. 2011. Collective sensing: integrating geospatial technologies to understand urban systems-an overview. Remote Sensing, 3(8): 1743-1776 [DOI: 10.3390/rs3081743http://dx.doi.org/10.3390/rs3081743]
Bouvet A, Le Toan T and Lam-Dao N. 2009. Monitoring of the rice cropping system in the Mekong delta using ENVISAT/ASAR dual polarization data. IEEE Transactions on Geoscience and Remote Sensing, 47(2): 517-526 [DOI: 10.1109/TGRS.2008.200 7963http://dx.doi.org/10.1109/TGRS.2008.2007963]
Buitre M J C, Zhang H S and Lin H. 2019. The mangrove forests change and impacts from tropical cyclones in the Philippines using time series satellite imagery. Remote Sensing, 11(6): 688 [DOI: 10.3390/rs11060688http://dx.doi.org/10.3390/rs11060688]
Cárdenas N Y, Joyce K E and Maier S W. 2017. Monitoring mangrove forests: are we taking full advantage of technology?. International Journal of Applied Earth Observation and Geoinformation, 63: 1-14 [DOI: 10.1016/j.jag.2017.07.004http://dx.doi.org/10.1016/j.jag.2017.07.004]
Chai D F, Newsam S, Zhang H K K, Qiu Y F and Huang J F. 2019. Cloud and cloud shadow detection in Landsat imagery based on deep convolutional neural networks. Remote Sensing of Environment, 225: 307-316 [DOI: 10.1016/j.rse.2019.03.007http://dx.doi.org/10.1016/j.rse.2019.03.007]
Chen B Q, Xiao X M, Li X P, Pan L H, Doughty R, Ma J, Dong J W, Qin Y W, Zhao B, Wu Z X, Sun R, Lan G Y, Xie G S, Clinton N and Giri C. 2017. A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform. ISPRS Journal of Photogrammetry and Remote Sensing, 131: 104-120 [DOI: 10.1016/j.isprsjprs.2017.07.011http://dx.doi.org/10.1016/j.isprsjprs.2017.07.011]
Chen H N, Chandrasekar V, Cifelli R and Xie P P. 2020. A machine learning system for precipitation estimation using satellite and ground radar network observations. IEEE Transactions on Geoscience and Remote Sensing, 58(2): 982-994 [DOI: 10.1109/TGRS.2019.2942280http://dx.doi.org/10.1109/TGRS.2019.2942280]
Chen S P, Zhou C H and Lu F. 2005. Several remote sensing experiments in monsoon tropical and subtropical regions. Geo-Information Science, 7(4): 2-4
陈述彭, 周成虎, 陆锋. 2005. 季风热带与亚热带地区的遥感应用实验. 地球信息科学, 7(4): 2-4
Choi W, Lee H, Kim J, Ryu J Y, Park S S, Park J and Kang H. 2019. Effects of spatiotemporal O4 column densities and temperature-dependent O4 absorption cross-section on an aerosol effective height retrieval algorithm using the O4 air mass factor from the ozone monitoring instrument. Remote Sensing of Environment, 229: 223-233 [DOI: 10.1016/j.rse.2019.05.001http://dx.doi.org/10.1016/j.rse.2019.05.001]
Choi Y and Souri A H. 2015. Chemical condition and surface ozone in large cities of Texas during the last decade: observational evidence from OMI, CAMS, and model analysis. Remote Sensing of Environment, 168: 90-101 [DOI: 10.1016/j.rse.2015.06.026http://dx.doi.org/10.1016/j.rse.2015.06.026]
Collin A, Nadaoka K and Bernardo L. 2015. Mapping the socio-economic and ecological resilience of Japanese coral reefscapes across a decade. ISPRS International Journal of Geo-Information, 4(2): 900-927 [DOI: 10.3390/ijgi4020900http://dx.doi.org/10.3390/ijgi4020900]
Dimitrov P, Dong Q H, Eerens H, Gikov A, Filchev L, Roumenina E and Jelev G. 2019. Sub-pixel crop type classification using PROBA-V 100 m NDVI time series and reference data from sentinel-2 classifications. Remote Sensing, 11: 1370 [DOI: 10.3390/rs11111370http://dx.doi.org/10.3390/rs11111370]
Dong J W, Xiao X M, Sheldon S, Biradar C and Xie G S. 2012. Mapping tropical forests and rubber plantations in complex landscapes by integrating PALSAR and MODIS imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 74: 20-33 [DOI: 10.1016/j.isprsjprs.2012.07.004http://dx.doi.org/10.1016/j.isprsjprs.2012.07.004]
Dong L G and Shan J. 2013. A comprehensive review of earthquake-induced building damage detection with remote sensing techniques. ISPRS Journal of Photogrammetry and Remote Sensing, 84: 85-99 [DOI: 10.1016/j.isprsjprs.2013.06.011http://dx.doi.org/10.1016/j.isprsjprs.2013.06.011]
Doren R F, Rutchey K and Welch R. 1999. The Everglades: a perspective on the requirements and applications for vegetation map and database products. Photogrammetric Engineering and Remote Sensing, 65(2): 155-161
Dupuis C, Lejeune P, Michez A and Fayolle A. 2020. How can remote sensing help monitor tropical moist forest degradation?-A systematic review. Remote Sensing, 12(7): 1087 [DOI: 10.3390/rs12071087http://dx.doi.org/10.3390/rs12071087]
Fonseka H P U, Zhang H S, Sun Y, Su H, Lin H and Lin Y Y. 2019. Urbanization and its impacts on land surface temperature in Colombo metropolitan area, Sri Lanka, from 1988 to 2016. Remote Sensing, 11(8):957 [DOI: 10.3390/rs11080957http://dx.doi.org/10.3390/rs11080957]
Friess D A and Webb E L. 2014. Variability in mangrove change estimates and implications for the assessment of ecosystem service provision. Global Ecology and Biogeography, 23: 715-725 [DOI: 10.1111/geb.12140http://dx.doi.org/10.1111/geb.12140]
Gagliardini D A, Clemente-Colón P, Bava J, Milovich J A and Frulla L A. 2001. Complementary use of SAR and thermal IR observations in the Brazil-Malvinas confluence region. Canadian Journal of Remote Sensing, 27(6): 643-650 [DOI: 10.1080/07038992.2001.10854906http://dx.doi.org/10.1080/07038992.2001.10854906]
Gastellu-Etchegorry J P. 1988. Cloud cover distribution in Indonesia. International Journal of Remote Sensing, 9(7): 1267-1276 [DOI: 10.1080/01431168808954934http://dx.doi.org/10.1080/01431168808954934]
Geng G N, Zhang Q, Martin R V, van Donkelaar A, Huo H, Che H Z, Lin J T and He K B. 2015. Estimating long-term PM2.5 concentrations in China using satellite-based aerosol optical depth and a chemical transport model. Remote Sensing of Environment, 166: 262-270
Giri C, Ochieng E, Tieszen L L, Zhu Z, Singh A, Loveland T, Masek J and Duke N. 2011. Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography, 20: 154-159 [DOI: 10.1111/j.1466-8238.2010.00584.xhttp://dx.doi.org/10.1111/j.1466-8238.2010.00584.x]
Giuliani G and Peduzzi P. 2011. The PREVIEW Global Risk Data Platform: a geoportal to serve and share global data on risk to natural hazards. Natural Hazards and Earth System Sciences, 11: 53-66 [DOI: 10.5194/nhess-11-53-2011http://dx.doi.org/10.5194/nhess-11-53-2011]
Goodwin N R, Collett L J, Denham R J, Flood N and Tindall D. 2013. Cloud and cloud shadow screening across Queensland, Australia: an automated method for Landsat TM/ETM + time series. Remote Sensing of Environment, 134: 50-65 [DOI: 10.1016/j.rse.2013.02.019http://dx.doi.org/10.1016/j.rse.2013.02.019]
Green E P, Clark C D, Mumby P J, Edwards A J and Ellis A C. 1998. Remote sensing techniques for mangrove mapping. International Journal of Remote Sensing, 19(5): 935-956 [DOI: 10.1080/014311698215801http://dx.doi.org/10.1080/014311698215801]
Grishin G A and Savoskin, V M. 1995. Simulation of the influence of clouds on the outgoing long-wave-radiation from Noaa-11 satellite data. Earth Observation and Remote Sensing, 12: 737-743
Guo H D, Goodchild M F and Annoni A. 2020. Manual of Digital Earth. Singapore: Springer [DOI: 10.1007/978-981-32-9915-3http://dx.doi.org/10.1007/978-981-32-9915-3]
Guo M, Li J, Sheng C L, Xu J W and Wu L. 2017. A review of wetland remote sensing. Sensors, 17(4): 777 [DOI: 10.3390/s17040777http://dx.doi.org/10.3390/s17040777]
Hansen M C and Loveland T R. 2012. A review of large area monitoring of land cover change using Landsat data. Remote Sensing of Environment, 122: 66-74 [DOI: 10.1016/j.rse.2011.08.024http://dx.doi.org/10.1016/j.rse.2011.08.024]
Hedley J D, Roelfsema C, Brando V, Giardino C, Kutser T, Phinn S, Mumby P J, Barrilero O, Laporte J and Koetz B. 2018. Coral reef applications of Sentinel-2: coverage, characteristics, bathymetry and benthic mapping with comparison to Landsat 8. Remote Sensing of Environment, 216: 598-614 [DOI: 10.1016/j.rse.2018.07.014http://dx.doi.org/10.1016/j.rse.2018.07.014]
Hedley J D, Roelfsema C M, Chollett I, Harborne A R, Heron S F, Weeks S, Skirving W J, Strong A E, Eakin C M, Christensen T R L, Ticzon V, Bejarano S and Mumby P J. 2016. Remote sensing of coral reefs for monitoring and management: a review. Remote Sensing, 8(2): 118 [DOI: 10.3390/rs8020118http://dx.doi.org/10.3390/rs8020118]
Hedley J D, Roelfsema C M, Phinn S R and Mumby P J. 2012. Environmental and sensor limitations in optical remote sensing of coral reefs: implications for monitoring and sensor design. Remote Sensing, 4(1): 271-302 [DOI: 10.3390/rs4010271http://dx.doi.org/10.3390/rs4010271]
Hethcoat M G, Edwards D P, Carreiras J M B, Bryant R G, Franca F M and Quegan S. 2019. A machine learning approach to map tropical selective logging. Remote Sensing of Environment, 221: 569-582 [DOI: 10.1016/j.rse.2018.11.044http://dx.doi.org/10.1016/j.rse.2018.11.044]
Higginbottom T P, Symeonakis E, Meyer H and van der Linden S. 2018. Mapping fractional woody cover in semi-arid savannahs using multi-seasonal composites from Landsat data. ISPRS Journal of Photogrammetry and Remote Sensing, 139: 88-102 [DOI: 10.1016/j.isprsjprs.2018.02.010http://dx.doi.org/10.1016/j.isprsjprs.2018.02.010]
Honda K and Nagai M. 2002. Real-time volcano activity mapping using ground-based digital imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 57(1/2): 159-168 [DOI: 10.1016/S0924-2716(02)00112-0http://dx.doi.org/10.1016/S0924-2716(02)00112-0]
Hu L J, Li W Y and Xu B. 2018. The role of remote sensing on studying mangrove forest extent change. International Journal of Remote Sensing, 39(19): 6440-6462 [DOI: 10.1080/01431161.2018.1455239http://dx.doi.org/10.1080/01431161.2018.1455239]
Jensen J R, Qiu F and Ji M H. 1999. Predictive modelling of coniferous forest age using statistical and artificial neural network approaches applied to remote sensor data. International Journal of Remote Sensing, 20(14): 2805-2822 [DOI: 10.1080/0143116 99211804http://dx.doi.org/10.1080/014311699211804]
Jia M M, Wang Z M, Wang C, Mao D H and Zhang Y Z. 2019. A new vegetation index to detect periodically submerged mangrove forest using single-tide sentinel-2 imagery. Remote Sensing, 11(17): 2043 [DOI: 10.3390/rs11172043http://dx.doi.org/10.3390/rs11172043]
Jiang L M, Liao M S, Lin H and Yang L M. 2009. Synergistic use of optical and InSAR data for urban impervious surface mapping: a case study in Hong Kong. International Journal of Remote Sensing, 30(11): 2781-2796 [DOI: 10.1080/01431160802555838http://dx.doi.org/10.1080/01431160802555838]
Jiang X, Liu Y, Yu B and Jiang M. 2007. Comparison of MISR aerosol optical thickness with AERONET measurements in Beijing metropolitan area. Remote Sensing of Environment, 107(1/2): 45-53 [DOI: 10.1016/j.rse.2006.06.022http://dx.doi.org/10.1016/j.rse.2006.06.022]
Johannessen J A. 1999. Marine coastal ocean monitoring: SAR in synergy with other remote sensing observations//Proceedings of the Euro-Asian Space Week on Cooperation in Space. Singapore, 430: 311-319
Kaufman Y J and Gao B C. 1992. Remote sensing of water vapor in the near IR from EOS/MODIS. IEEE Transactions on Geoscience and Remote Sensing, 30(5): 871-884 [DOI: 10.1109/36.175321http://dx.doi.org/10.1109/36.175321]
Kaufman Y J, Tanré D and Boucher O. 2002. A satellite view of aerosols in the climate system. Nature, 419(6903): 215-223 [DOI: 10.1038/nature01091http://dx.doi.org/10.1038/nature01091]
Kazansky A V. 1997. Dual-path method and revised radiative model for retrieving cloud parameters from pairs of AVHRR images. Remote Sensing of Environment, 59(3): 417-427 [DOI: 10.1016/0034-4257(95)00101-8http://dx.doi.org/10.1016/0034-4257(95)00101-8]
Kim W, Kim J, Jung Y, Boesch H, Lee H, Lee S, Goo T Y, Jeong U, Kim M, Cho C H and Ou M L. 2016. Retrieving XCO2 from GOSAT FTS over East Asia using simultaneous aerosol information from CAI. Remote Sensing, 8(12): 994 [DOI: 10.3390/rs8120994http://dx.doi.org/10.3390/rs8120994]
Kimes D S, Nelson R F, Salas W A and Skole D L. 1999. Mapping secondary tropical forest and forest age from SPOT HRV data. International Journal of Remote Sensing, 20(18): 3625-3640 [DOI: 10.1080/014311699211246http://dx.doi.org/10.1080/014311699211246]
Knudby A, LeDrew E and Newman C. 2007. Progress in the use of remote sensing for coral reef biodiversity studies. Progress in Physical Geography, 31(4): 421-434 [DOI: 10.1177/030913330708 1292http://dx.doi.org/10.1177/0309133307081292]
Kottek M, Grieser J, Beck C, Rudolf B and Rubel F. 2006. World map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift, 15(3): 259-263 [DOI: 10.1127/0941-2948/2006/0130http://dx.doi.org/10.1127/0941-2948/2006/0130]
Kovacs J M, Lu X X, Flores-Verdugo F, Zhang C, de Santiago F F and Jiao X. 2013. Applications of ALOS PALSAR for monitoring biophysical parameters of a degraded black mangrove (Avicennia germinans) forest. ISPRS Journal of Photogrammetry and Remote Sensing, 82: 102-111 [DOI: 10.1016/j.isprsjprs.2013.05.004http://dx.doi.org/10.1016/j.isprsjprs.2013.05.004]
Kuenzer C, Bluemel A, Gebhardt S, Quoc T V and Dech S. 2011. Remote sensing of mangrove ecosystems: a review. Remote Sensing, 3(5): 878-928 [DOI: 10.3390/rs3050878http://dx.doi.org/10.3390/rs3050878]
Lee J, Kim J, Song C H, Ryu J H, Ahn Y H and Song C K. 2010. Algorithm for retrieval of aerosol optical properties over the ocean from the Geostationary Ocean Color Imager. Remote Sensing of Environment, 114(5): 1077-1088 [DOI: 10.1016/j.rse.2009.12.021http://dx.doi.org/10.1016/j.rse.2009.12.021]
Li X, Yeh A G O, Wang S, Liu K, Liu X, Qian J and Chen X. 2007. Regression and analytical models for estimating mangrove wetland biomass in South China using Radarsat images. International Journal of Remote Sensing, 28(24): 5567-5582 [DOI: 10.1080/01431160701227638http://dx.doi.org/10.1080/01431160701227638]
Li Z L and Becker F. 1993. Feasibility of land surface temperature and emissivity determination from AVHRR data. Remote Sensing of Environment, 43(1): 67-85 [DOI: 10.1016/0034-4257(93)90065-6http://dx.doi.org/10.1016/0034-4257(93)90065-6]
Liew S C, Kam S P, Tuong T P, Chen P, Minh V Q and Lim H. 1998. Application of multitemporal ERS-2 synthetic aperture radar in delineating rice cropping systems in the Mekong River Delta, Vietnam. IEEE Transactions on Geoscience and Remote Sensing, 36(5): 1412-1420 [DOI: 10.1109/36.718845http://dx.doi.org/10.1109/36.718845]
Lin C A, Chen Y C, Liu C Y, Chen W T, Seinfeld J H and Chou C C K. 2019. Satellite-derived correlation of SO2, NO2, and aerosol optical depth with meteorological conditions over East Asia from 2005 to 2015. Remote Sensing, 11(15): 1738 [DOI: 10.3390/rs11151738http://dx.doi.org/10.3390/rs11151738]
Lin Y Y, Zhang H S, Lin H, Gamba P E and Liu X P. 2020. Incorporating synthetic aperture radar and optical images to investigate the annual dynamics of anthropogenic impervious surface at large scale. Remote Sensing of Environment, 242: 111757 [DOI: 10.1016/j.rse.2020.111757http://dx.doi.org/10.1016/j.rse.2020.111757]
Liu M F, Zhang H S, Lin G H, Lin H and Tang D L. 2018. Zonation and directional dynamics of mangrove forests derived from time-series satellite imagery in Mai Po, Hong Kong. Sustainability, 10(6): 1913 [DOI: 10.3390/su10061913http://dx.doi.org/10.3390/su10061913]
Liu Y, Franklin M, Kahn R and Koutrakis P. 2007. Using aerosol optical thickness to predict ground-level PM2.5 concentrations in the St. Louis area: a comparison between MISR and MODIS. Remote Sensing of Environment, 107(1/2): 33-44 [DOI: 10.1016/j.rse.2006.05.022http://dx.doi.org/10.1016/j.rse.2006.05.022]
Luo L and Mountrakis G. 2010. Integrating intermediate inputs from partially classified images within a hybrid classification framework: an impervious surface estimation example. Remote Sensing of Environment, 114(6): 1220-1229 [DOI: 10.1016/j.rse.2010.01.008http://dx.doi.org/10.1016/j.rse.2010.01.008]
Luo L and Mountrakis G. 2011. Converting local spectral and spatial information from a priori classifiers into contextual knowledge for impervious surface classification. ISPRS Journal of Photogrammetry and Remote Sensing, 66(5): 579-587 [DOI: 10.1016/j.isprsjprs.2011.03.002http://dx.doi.org/10.1016/j.isprsjprs.2011.03.002]
Mahrooghy M, Aanstoos J V, Nobrega R A A, Hasan K, Prasad S and Younan N H. 2015. A machine learning framework for detecting landslides on earthen levees using spaceborne SAR imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(8): 3791-3801 [DOI: 10.1109/JSTARS.2015.2427337http://dx.doi.org/10.1109/JSTARS.2015.2427337]
Malingreau J P and Belward A S. 1994. Recent activities in the European Community for the creation and analysis of global AVHRR data sets. International Journal of Remote Sensing, 15(17): 3397-3416 [DOI: 10.1080/01431169408954337http://dx.doi.org/10.1080/01431169408954337]
Malingreau J P and Tucker C J. 1990. Ranching in the Amazon Basin - Large-scale changes observed by AVHRR. International Journal of Remote Sensing, 11(2): 187-189 [DOI: 10.1080/014311690 08955012http://dx.doi.org/10.1080/01431169008955012]
Manavalan R. 2018. Review of synthetic aperture radar frequency, polarization, and incidence angle data for mapping the inundated regions. Journal of Applied Remote Sensing, 12(2): 021501 [DOI: 10.1117/1.JRS.12.021501http://dx.doi.org/10.1117/1.JRS.12.021501]
Martins V S, Novo E M L M, Lyapustin A, Aragão L E O C, Freitas S R and Barbosa C C F. 2018. Seasonal and interannual assessment of cloud cover and atmospheric constituents across the Amazon (2000-2015): insights for remote sensing and climate analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 145: 309-327 [DOI: 10.1016/j.isprsjprs.2018.05.013http://dx.doi.org/10.1016/j.isprsjprs.2018.05.013]
Maselli F, Papale D, Chiesi M, Matteucci G, Angeli L, Raschi A and Seufert G. 2014. Operational monitoring of daily evapotranspiration by the combination of MODIS NDVI and ground meteorological data: application and evaluation in Central Italy. Remote Sensing of Environment, 152: 279-290 [DOI: 10.1016/j.rse.2014.06.021http://dx.doi.org/10.1016/j.rse.2014.06.021]
Mason R A B, Skirving W J and Dove S G. 2020. Integrating physiology with remote sensing to advance the prediction of coral bleaching events. Remote Sensing of Environment, 246: 111794 [DOI: 10.1016/j.rse.2020.111794http://dx.doi.org/10.1016/j.rse.2020.111794]
Mathieu R, Naidoo L, Cho M A, Leblon B, Main R, Wessels K, Asner G P, Buckley J, Van Aardt J, Erasmus B F N and Smit I P J. 2013. Toward structural assessment of semi-arid African savannahs and woodlands: the potential of multitemporal polarimetric RADARSAT-2 fine beam images. Remote Sensing of Environment, 138: 215-231 [DOI: 10.1016/j.rse.2013.07.011http://dx.doi.org/10.1016/j.rse.2013.07.011]
Miller M E, Lefsky M and Pang Y. 2011. Optimization of Geoscience Laser Altimeter System waveform metrics to support vegetation measurements. Remote Sensing of Environment, 115(2): 298-305 [DOI: 10.1016/j.rse.2010.09.002http://dx.doi.org/10.1016/j.rse.2010.09.002]
Minnis P, Sun-Mack S, Young D F, Heck P W, Garber D P, Chen Y, Spangenberg D A, Arduini R F, Trepte Q Z, Smith W L, Ayers J K, Gibson S C, Miller W F, Hong G, Chakrapani V, Takano Y, Liou K N, Xie Y and Yang P. 2011. CERES edition-2 cloud property retrievals using TRMM VIRS and terra and aqua MODIS data-Part I: algorithms. IEEE Transactions on Geoscience and Remote Sensing, 49(11): 4374-4400 [DOI: 10.1109/TGRS.2011.2144601http://dx.doi.org/10.1109/TGRS.2011.2144601]
Mohamad I N, Hayashi T, Uyeda H, Terao T and Kikuchi K. 2004. Diurnal variations of cloud activity in Bangladesh and north of the Bay of Bengal in 2000. Remote Sensing of Environment, 90(3): 378-388 [DOI: 10.1016/j.rse.2004.01.011http://dx.doi.org/10.1016/j.rse.2004.01.011]
Myint S W, Giri C P, Le W, Zhu Z L and Gillette S C. 2008. Identifying mangrove species and their surrounding land use and land cover classes using an object-oriented approach with a lacunarity spatial measure. Giscience and Remote Sensing, 45(2): 188-208 [DOI: 10.2747/1548-1603.45.2.188http://dx.doi.org/10.2747/1548-1603.45.2.188]
Okoro E and Okeke F. 2017. Effects of zonal wind on stratospheric ozone variations over Nigeria. International Journal of Remote Sensing, 38(6): 1665-1681 [DOI: 10.1080/01431161.2017.1286053http://dx.doi.org/10.1080/01431161.2017.1286053]
Otukei J R and Blaschke T. 2010. Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12(S1): S27-S31 [DOI: 10.1016/j.jag.2009.11.002http://dx.doi.org/10.1016/j.jag.2009.11.002]
Palandro D A, Andréfouët S, Hu C M, Hallock P, Müller-Karger F E, Dustan P, Callahan M K, Kranenburg C and Beaver C R. 2008. Quantification of two decades of shallow-water coral reef habitat decline in the Florida Keys National Marine Sanctuary using Landsat data (1984-2002). Remote Sensing of Environment, 112(8): 3388-3399 [DOI: 10.1016/j.rse.2008.02.015http://dx.doi.org/10.1016/j.rse.2008.02.015]
Pettorelli N, Vik J O, Mysterud A, Gaillard J M, Tucker C J and Stenseth N C. 2005. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology and Evolution, 20(9): 503-510 [DOI: 10.1016/j.tree.2005.05.011http://dx.doi.org/10.1016/j.tree.2005.05.011]
Pham T D, Yokoya N, Bui D T, Yoshino K and Friess D A. 2019. Remote sensing approaches for monitoring mangrove species, structure, and biomass: opportunities and challenges. Remote Sensing, 11(3): 230 [DOI: 10.3390/rs11030230http://dx.doi.org/10.3390/rs11030230]
Pham T D, Yokoya N, Xia J S, Ha N T, Le N N, Nguyen T T T, Dao T H, Vu T T P, Pham T D and Takeuchi W. 2020. Comparison of machine learning methods for estimating mangrove above-ground biomass using multiple source remote sensing data in the Red River Delta Biosphere Reserve, Vietnam. Remote Sensing, 12(8): 1334 (OI: 10.3390/rs12081334)
Popov M A, Kussul N N, Stankevich S A, Kozlova A A, Shelestov A Y, Kravchenko O M, Korbakov M B and Skakun S V. 2008. Web service for biodiversity estimation using remote sensing data. International Journal of Digital Earth, 1(4): 367-376 [DOI: 10.1080/17538940802483745http://dx.doi.org/10.1080/17538940802483745]
Prasad A K and Singh R P. 2007. Comparison of MISR-MODIS aerosol optical depth over the Indo-Gangetic basin during the winter and summer seasons (2000-2005). Remote Sensing of Environment, 107(1/2): 109-119 [DOI: 10.1016/j.rse.2006.09.026http://dx.doi.org/10.1016/j.rse.2006.09.026]
Racault M F, Raitsos D E, Berumen M L, Brewin R J W, Platt T, Sathyendranath S and Hoteit I. 2015. Phytoplankton phenology indices in coral reef ecosystems: application to ocean-color observations in the Red Sea. Remote Sensing of Environment, 160: 222-234 [DOI: 10.1016/j.rse.2015.01.019http://dx.doi.org/10.1016/j.rse.2015.01.019]
Ramsey E W III and Jensen J R. 1996. Remote sensing of mangrove wetlands: relating canopy spectra to site-specific data. Photogrammetric Engineering and Remote Sensing, 62(8): 939-948
Renó V, Novo E and Escada M. 2016. Forest fragmentation in the Lower Amazon Floodplain: implications for biodiversity and ecosystem service provision to riverine populations. Remote Sensing, 8(11): 886 [DOI: 10.3390/rs8110886http://dx.doi.org/10.3390/rs8110886]
Reygadas Y, Jensen J L R and Moisen G G. 2019. Forest degradation assessment based on trend analysis of MODIS-leaf area index: a case study in Mexico. Remote Sensing, 11(21): 2503 [DOI: 10.3390/rs11212503http://dx.doi.org/10.3390/rs11212503]
Rignot E, Salas W A and Skole D L. 1997. Mapping deforestation and secondary growth in Rondonia, Brazil, using imaging radar and thematic mapper data. Remote Sensing of Environment, 59(2): 167-179 [DOI: 10.1016/S0034-4257(96)00150-2http://dx.doi.org/10.1016/S0034-4257(96)00150-2]
Rudorff N, Rudorff C M, Kampel M and Ortiz G. 2018. Remote sensing monitoring of the impact of a major mining wastewater disaster on the turbidity of the Doce River plume off the eastern Brazilian coast. ISPRS Journal of Photogrammetry and Remote Sensing, 145: 349-361 [DOI: 10.1016/j.isprsjprs.2018.02.013http://dx.doi.org/10.1016/j.isprsjprs.2018.02.013]
Russell A, Milford J, Bergin M S, McBride S, McNair L, Yang Y, Stockwell W R and Croes B. 1995. Urban ozone control and atmospheric reactivity of organic gases. Science, 269(5223): 491-495 [DOI: 10.1126/science.269.5223.491http://dx.doi.org/10.1126/science.269.5223.491]
Rutchey K and Vilchek L. 1999. Air photointerpretation and satellite imagery analysis techniques for mapping cattail coverage in a northern Everglades impoundment. Photogrammetric Engineering and Remote Sensing, 65(2): 185-191
Santos M J, Disney M and Chave J. 2018. Detecting human presence and influence on neotropical forests with remote sensing. Remote Sensing, 10(10): 1593 [DOI: 10.3390/rs10101593http://dx.doi.org/10.3390/rs10101593]
Segal-Rozenhaimer M, Li A, Das K and Chirayath V. 2020. Cloud detection algorithm for multi-modal satellite imagery using convolutional neural-networks (CNN). Remote Sensing of Environment, 237: 111446 [DOI: 10.1016/j.rse.2019.111446http://dx.doi.org/10.1016/j.rse.2019.111446]
Sinyuk A, Dubovik O, Holben B, Eck T F, Breon F M, Martonchik J, Kahn R, Diner D J, Vermote E F, Roger J C, Lapyonok T and Slutsker I. 2007. Simultaneous retrieval of aerosol and surface properties from a combination of AERONET and satellite data. Remote Sensing of Environment, 107(1/2): 90-108 [DOI: 10.1016/j.rse.2006.07.022http://dx.doi.org/10.1016/j.rse.2006.07.022]
Smith L B and Wright J W. 1972. Sporadic-E and wind-profile interrelation over Hawaii. Radio Science, 7(3): 363-366 [DOI: 10.1029/RS007i003p00363http://dx.doi.org/10.1029/RS007i003p00363]
Sobrino J A, Coll C and Caselles V. 1991. Atmospheric correction for land surface temperature using NOAA-11 AVHRR channels 4 and 5. Remote Sensing of Environment, 38(1): 19-34 [DOI: 10.1016/0034-4257(91)90069-Ihttp://dx.doi.org/10.1016/0034-4257(91)90069-I]
Stampoulis D, Haddad Z S and Anagnostou E N. 2014. Assessing the drivers of biodiversity in Madagascar by quantifying its hydrologic properties at the watershed scale. Remote Sensing of Environment, 148: 1-15 [DOI: 10.1016/j.rse.2014.03.005http://dx.doi.org/10.1016/j.rse.2014.03.005]
Thenkabail P S, Enclona E A, Ashton M S, Legg C and De Dieu M J. 2004. Hyperion, IKONOS, ALI, and ETM + sensors in the study of African rainforests. Remote Sensing of Environment, 90(1): 23-43 [DOI: 10.1016/j.rse.2003.11.018http://dx.doi.org/10.1016/j.rse.2003.11.018]
Trigg S N, Roy D P and Flasse S P. 2005. An in situ study of the effects of surface anisotropy on the remote sensing of burned savannah. International Journal of Remote Sensing, 26(21): 4869-4876 [DOI: 10.1080/01431160500141923http://dx.doi.org/10.1080/01431160500141923]
United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP). 2015. Disasters without borders: Regional resilience for sustainable development. Asia-Pacific Disaster Report 2015
Wan Z M. 2014. New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sensing of Environment, 140: 36-45 [DOI: 10.1016/j.rse.2013.08.027http://dx.doi.org/10.1016/j.rse.2013.08.027]
Wan Z M, Zhang Y L, Zhang Q C and Li Z L. 2002. Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data. Remote Sensing of Environment, 83(1/2): 163-180 [DOI: 10.1016/S0034-4257(02)00093-7http://dx.doi.org/10.1016/S0034-4257(02)00093-7]
Wang D, Lin H, Chen J S, Zhang Y Z and Zeng Q W. 2010. Application of multi-temporal ENVISAT ASAR data to agricultural area mapping in the Pearl River Delta. International Journal of Remote Sensing, 31(6): 1555-1572 [DOI: 10.1080/01431160903475258http://dx.doi.org/10.1080/01431160903475258]
Wang L, Jia M M, Yin D M and Tian J Y. 2019. A review of remote sensing for mangrove forests: 1956-2018. Remote Sensing of Environment, 231: 111223 [DOI: 10.1016/j.rse.2019.111223http://dx.doi.org/10.1016/j.rse.2019.111223]
Whittle M, Quegan S, Uryu Y, Stüewe M and Yulianto K. 2012. Detection of tropical deforestation using ALOS-PALSAR: a Sumatran case study. Remote Sensing of Environment, 124: 83-98 [DOI: 10.1016/j.rse.2012.04.027http://dx.doi.org/10.1016/j.rse.2012.04.027]
Wiens J, Sutter R, Anderson M, Blanchard J, Barnett A, Aguilar-Amuchastegui N, Avery C and Laine S. 2009. Selecting and conserving lands for biodiversity: the role of remote sensing. Remote Sensing of Environment, 113(7): 1370-1381 [DOI: 10.1016/j.rse.2008.06.020http://dx.doi.org/10.1016/j.rse.2008.06.020]
Xu R, Zhang H S and Lin H. 2017a. Urban impervious surfaces estimation from optical and SAR imagery: a comprehensive comparison. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(9): 4010-4021 [DOI: 10.1109/JSTARS.2017.2706747http://dx.doi.org/10.1109/JSTARS.2017.2706747]
Xu R, Zhang H S, Wang T and Lin H. 2017b. Using pan-sharpened high resolution satellite data to improve impervious surfaces estimation. International Journal of Applied Earth Observation and Geoinformation, 57: 177-189 [DOI: 10.1016/j.jag.2016.12.020http://dx.doi.org/10.1016/j.jag.2016.12.020]
Yan X, Li Z Q, Luo N N, Shi W Z, Zhao W J, Yang X C, Liang C, Zhang F and Cribb M. 2019. An improved algorithm for retrieving the fine-mode fraction of aerosol optical thickness. Part 2: application and validation in Asia. Remote Sensing of Environment, 222: 90-103 [DOI: 10.1016/j.rse.2018.12.012http://dx.doi.org/10.1016/j.rse.2018.12.012]
Zhang H S, Li J, Wang T, Lin H, Zheng Z Z, Li Y and Lu Y F. 2018a. A manifold learning approach to urban land cover classification with optical and radar data. Landscape and Urban Planning, 172: 11-24 [DOI: 10.1016/j.landurbplan.2017.12.009http://dx.doi.org/10.1016/j.landurbplan.2017.12.009]
Zhang H S, Lin H and Wang Y P. 2018c. A new scheme for urban impervious surface classification from SAR images. ISPRS Journal of Photogrammetry and Remote Sensing, 139: 103-118 [DOI: 10.1016/j.isprsjprs.2018.03.007http://dx.doi.org/10.1016/j.isprsjprs.2018.03.007]
Zhang H S, Lin H, Zhang Y Z and Weng Q H. 2015. Remote Sensing of Impervious Surfaces in Tropical and Subtropical Areas. Boca Raton, FL: CRC Press
Zhang H S, Wang T, Liu M F, Jia M M, Lin H, Chu L M and Devlin A T. 2018b. Potential of combining optical and dual polarimetric SAR data for improving mangrove species discrimination using rotation forest. Remote Sensing, 10(3): 467 [DOI: 10.3390/rs10030467http://dx.doi.org/10.3390/rs10030467]
Zhang H S, Zhang Y Z and Lin H. 2012. A comparison study of impervious surfaces estimation using optical and SAR remote sensing images. International Journal of Applied Earth Observation and Geoinformation, 18: 148-156 [DOI: 10.1016/j.jag.2011.12.015http://dx.doi.org/10.1016/j.jag.2011.12.015]
Zhang H S, Zhang Y Z and Lin H. 2014a. Seasonal effects of impervious surface estimation in subtropical monsoon regions. International Journal of Digital Earth, 7(9): 746-760 [DOI: 10.1080/17538947.2013.781241http://dx.doi.org/10.1080/17538947.2013.781241]
Zhang Y Z, Zhang H S and Lin H. 2014b. Improving the impervious surface estimation with combined use of optical and SAR remote sensing images. Remote Sensing of Environment, 141: 155-167 [DOI: 10.1016/j.rse.2013.10.028http://dx.doi.org/10.1016/j.rse.2013.10.028]
Zhong C, Li H, Xiang W, Su A J and Huang X F. 2012. Comprehensive study of landslides through the integration of multi remote sensing techniques: framework and latest advances. Journal of Earth Science, 23(2): 243-252 [DOI: 10.1007/S12583-012-0245-6http://dx.doi.org/10.1007/S12583-012-0245-6]
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