曹霸, 杨小梅, 肖玲, 等, 2016. 基于极化干涉SAR数据森林树高反演算法比较[J]. 林业资源管理(6): 100-105. 〔CAO B, YANG X M, XIAO L, et al, 2016. Study on forest tree height inversion based on polarization interference SAR data[J]. Forest Resources Management(6): 100-105.〕
陈鹏琦, 龙四春, 蒋宗立, 等, 2015. 基于TanDEMX和ERS数据的InSAR提取DEM的实验研究[J]. 湖南科技大学学报(自然科学版), 30(4): 21-26. 〔CHEN P Q, LONG S C, JIANG Z L, et al, 2015. Study of InSAR-derived DEMs generated with Tan DEM-X and ERS tandem data[J]. Journal of Hunan University of Science & Technology(Natural Science Edition), 30(4): 21-26.〕
戴前石, 谭宽祥, 郑红, 2000. 卫星遥感技术在林地变化监测中的应用[J]. 林业资源管理(2): 57-59. 〔DAI Q S, TAN K X, ZHENG H, 2000. Application of remote sensing technology on forest land variation monitoring[J]. Forest Resources Management(2): 57-59.〕
邓书斌, 2007. 林冠状态变化遥感监测方法研究[D]. 青岛: 山东科技大学. 〔DENG S B, 2007. A study on the approach of remote sensing dynamic monitoring of canopy state changes[D]. Qingdao: Shandong University of Science and Technology〕
郭胜龙, 李洋, 尹嫱, 等, 2016. 基于简缩极化干涉SAR数据的森林垂直参数反演[J]. 电子与信息学报, 38(1): 71-79. 〔GUO S L, LI Y, YIN Q, et al, 2016. Vertical parameters estimation of forest with compact polarimetric SAR interferometry data[J]. Journal of Electronics & Information Technology, 38(1): 71-79.〕
郭泽呈, 魏伟, 庞素菲, 等, 2019. 基于SPCA和遥感指数的干旱内陆河流域生态脆弱性时空演变及动因分析: 以石羊河流域为例[J]. 生态学报, 39(7): 2558-2572. 〔GUO Z C, WEI W, PANG S F, et al, 2019. Spatio-Temporal evolution and motivation analysis of ecological vulnerability in arid inland river basin based on SPCA and remote sensing index: a case study on the Shiyang River Basin[J]. Acta Ecologica Sinica, 39(7): 2558-2572.〕
贺鹏, 贺东北, 陈振雄, 等, 2020. 基于树高和树冠因子的立木材积与地上生物量相容模型研究[J]. 中南林业科技大学学报, 40(4): 28-33. 〔HE P, HE D B, CHEN Z X, et al, 2020. Compatibility model of stand volume and above-ground biomass based on tree height and crown characteristics[J]. Journal of Central South University of Forestry & Technology, 40(4): 28-33.〕
何迎东, 马瑞峰, 2019. 基于Landsat-8 TIRS数据的兰州市地表温度反演[J]. 测绘与空间地理信息, 42(9): 43-46. 〔HE Y D, MA R F, 2019. Surface temperature inversion and urban heat island research of Lanzhou based on Landsat-8 TIRS[J]. Geomatics & Spatial Information Technology, 42(9): 43-46.〕
李彪, 王耀强, 2015. 土壤盐渍化雷达反演模拟研究[J]. 干旱区资源与环境, 29(8): 180-184. 〔LI B, WANG Y Q, 2015. Radar inversion and simulation of salty soil salinization[J]. Journal of Arid Land Resources and Environment, 29(8): 180-184.〕
李依, 王世奥, 王丹, 等, 2015. 基于遥感影像的平朔矿区植被碳库变化分析[J]. 资源与产业, 17(1): 84-91. 〔LI Y, WANG S A, WANG D, et al, 2015. Vegetation carbon pool change of Pingshuo mine based on remote sensing images[J]. Resources & Industries, 17(1): 84-91.〕
李哲, 陈尔学, 王建, 2009. 几种极化干涉SAR森林平均高反演算法的比较评价[J]. 遥感技术与应用, 24(5): 611-616. 〔LI Z, CHEN E X, WANG J, 2009. Forest height retrieval methods by polarimetric SAR interferometry and their validation against ground truth[J]. Remote Sensing Technology & Application, 24(5): 611-616.〕
梁守真, 施平, 马万栋, 等, 2010. 植被叶片光谱及红边特征与叶片生化组分关系的分析[J]. 中国生态农业学报, 18(4): 804-809, 6. 〔LIANG S Z, SHI P, MA W D, et al, 2010. Relational analysis of spectra and red-edge characteristics of plant leaf and leaf biochemical constituent[J]. Chinese Journal of Eco-Agriculture, 18(4): 804-809, 6.〕
林中立, 徐涵秋, 2019. 基于遥感的海岛型城市发展生态效应分析: 以厦门岛为例[J]. 福州大学学报(自然科学版), 47(5): 610-616. 〔LIN Z L, XUE H Q, 2019. Ecological response analysis for urban development in an island city based on remote sensing: a case study in Xiamen island, southeastern China[J]. Journal of Fuzhou University(Natural Science Edition), 47(5): 610-616.〕
凌飞龙, 2010. 面向植被识别的SAR图像分类方法研究[D]. 北京: 中国林业科学研究院. 〔LING F L, 2010. Study on classification method using SAR images for vegetated area identification[D]. Beijing: Chinese Academy of Forestry.〕
刘琦, 岳彩荣, 章皖秋, 等, 2017. 极化干涉SAR森林冠层高反演的地形坡度改正[J]. 东北林业大学学报, 45(1): 55-60, 70. 〔LIU Q, YUE C R, ZHANG W Q, et al, 2017. Terrain slope correction on PolInSAR forest canopy height inversion[J]. Journal of Northeast Forestry University, 45(1): 55-60, 70.〕
刘索玄, 袁艳斌, 赵皞, 等, 2019. 基于遥感生态指数(RSEI)的水电开发区生态环境变化分析: 以清江中下游地区为例[J]. 生态与农村环境学报, 35(11): 1361-1368. 〔LIU S X, YUAN Y B, ZHAO H, et al, 2019. Analysis of ecological environment changes in hydropower development zone based on RSEI: a case study in the middle and lower reaches of the Qingjiang River, China[J]. Journal of Ecology and Rural Environment, 35(11): 1361-1368.〕
刘艳明, 李莉, 2019. 基于RS/GIS遥感监测的资源型城市建成区扩张与驱动因素分析: 以克拉玛依市为例[J]. 资源与产业, 21(3): 14-21. 〔LIU Y M, LI L, 2019. A case study on Karamay city: expansion and driving factors of resourcebased cities based on RS/GIS[J]. Resources & Industries, 21(3): 14-21.〕
罗雪莲, 2017. 基于合成孔径雷达的地表参数及森林树高反演[D]. 成都: 电子科技大学. 〔LUO X L, 2017. Inversion of surface parameters and tree heights using SAR data[D].Chengdu: University of Electronic Science and Technology of China.〕
牟怀义, 2016. 多源高分辨率卫星遥感影像监测林地动态变化研究[J]. 林业资源管理(4): 107-113. 〔MOU H Y, 2016. Monitoring of forestland dynamic changes by using multi source and high resolution satellite remote sensing images[J].Forest Resources Management(4): 107-113.〕
潘磊, 孙玉军, 2018. 应用Sentinel-1影像纹理信息模型估测杉木林生物量[J]. 东北林业大学学报, 46(1): 58-62. 〔PAN L, SUN Y J, 2018. Estimation of cunninghamia lanceolata forest biomass based on Sentinel-1 image texture information[J].Journal of Northeast Forestry University, 46(1): 58-62.〕
潘正荣, 2010. 几种常用森林蓄积量调查方法对比分析[J]. 林业调查规划, 35(2): 9-10. 〔PAN Z R, 2010. Comparative analysis of several frequently-used methods of forest stock volume inventory[J]. Forest Inventory and Planning, 35(2): 9-10.〕
覃先林, 李晓彤, 刘树超, 等, 2020. 我国林火卫星遥感预警监测技术研究进展[J]. 遥感学报, 24(5): 511-520. 〔QIN X L, LI X T, LIU S C, et al, 2020. Forest fire early warning and monitoring techniques using satellite remote sensing in China[J]. National Remote Sensing Bulletin, 24(5): 511-520.〕
沈夏炯, 韩道军, 侯柏成, 等, 2016. 浅谈植被指数的分类与应用[J]. 计算机时代(12): 17-20. 〔SHEN X J, HAN D J, HOU B C, et al, 2016. Discussion on classification and application of vegetation indices[J]. Computer Era (12): 17-20.〕
宋美杰, 罗艳云, 段利民, 2019. 基于改进遥感生态指数模型的锡林郭勒草原生态环境评价[J]. 干旱区研究, 36(6): 1521-1527. 〔SONG M J, LUO Y Y, DUAN L M, 2019. Evaluation of ecological environment in the Xilin Gol Steppe based on modified remote sensing ecological index model[J].Arid Zone Research, 36(6): 1521-1527.〕
王丹, 2006. 材积表材积对林分蓄积量调查的影响分析[J]. 林业勘查设计(4): 39-42. 〔WANG D, 2006. Effect of volume tables on surveying stand volume[J].Forest Investigation Design(4): 39-42.〕
王臣立, 牛铮, 郭治兴, 等, 2005. Radarsat SAR的森林生物物理参数信号响应及其蓄积量估测[J]. 国土资源遥感, 17(2): 24-28. 〔WANG C L, NIU Z, GUO Z X, et al, 2005. A study on forest biophysical parameter impact on radar signature and extraction of forest stock volum by means of Radarsat-SAR[J]. Remote Sensing for Land & Resources, 17(2): 24-28.〕
温若橙, 苏军明, 伍雅晴, 2017. 全极化干涉SAR反演树高的几种算法研究[J]. 测绘与空间地理信息, 40(2): 212-214. 〔WEN R C, SU J M, WU Y Q, 2017. Research on several vegetation height inversion algorithms from polarimetric interferometric SAR[J]. Geomatics & Spatial Information Technology, 40(2): 212-214.〕
吴楠, 李增元, 廖声熙, 等, 2017. 国内外林业遥感应用研究概况与展望[J]. 世界林业研究, 30(6): 34-40. 〔WU N, LI Z Y, LIAO S X, et al, 2017. Current situation and prospect of research on application of remote sensing to forestry[J].World Forestry Research, 30(6): 34-40.〕
岳彩荣, 肖虹雁, 曹霸, 2016. 基于PolInSAR森林高度反演研究[J]. 西南林业大学学报, 36(3): 137-143. 〔YUE C R, XIAO H Y, CAO B, 2016. Forest height inversion based on polarimetric interferometry SAR[J]. Journal of Southwest Forestry University, 36(3): 137-143.〕
张露, 李新武, 杜鹤娟, 等, 2010. 玉米作物极化SAR数据模拟[J]. 遥感学报, 14(4): 621-636, 2, 9-10. 〔ZHANG L, LI X W, DU H J, et al, 2010. Coherent polarimetric SAR simulation of maize[J]. National Remote Sensing Bulletin, 14(4): 621-636, 2, 9-10.〕
张庆云, 刘国林, 陶秋香, 等, 2014. 基于极化干涉SAR的植被高度反演算法对比分析[J]. 山东科技大学学报(自然科学版), 33(3): 77-83. 〔ZHANG Q Y, LIU G L, TAO Q X, et al, 2014. Algorithm comparative analysis of vegetation height inversion based on polarimetric SAR interferometric[J]. Journal of Shandong University of Science and Technology(Natural Science), 33(3): 77-83.〕
张王菲, 陈尔学, 李增元, 等, 2017. 干涉、极化干涉SAR技术森林高度估测算法研究进展[J]. 遥感技术与应用, 32(6): 983-997. 〔ZHANG W F, CHEN E X, LI Z Y, et al, 2017. Development of forest height estimation using InSAR/PolInSAR technology[J]. Remote Sensing Technology and Application, 32(6): 983-997.〕
章皖秋, 岳彩荣, 颜培东, 2017. Tan DEM-X极化干涉SAR森林冠层高度反演[J]. 东北林业大学学报, 45(1): 47-54. 〔ZHANG W Q, YUE C R, YAN P D, 2017. Forest canopy height retrieval by Pol in SAR with Tan DEM-X data[J]. Journal of Northeast Forestry University, 45(1): 47-54.〕
张雨, 郭鑫, 段佳豪, 2017. 基于遥感的1979—2014年中国填海造地变化特征分析[J]. 资源与产业, 19(1): 35-40. 〔ZHANG Y, GUO X, DUAN J H, 2017. China's land reclamation changes from 1979—2014 based on remote sensing data[J].Resources & Industries, 19(1): 35-40.〕
张颖, 李晓格, 2022. 碳达峰碳中和目标下北京市森林碳汇潜力分析[J]. 资源与产业, 24(1): 15-25. 〔ZHANG Y, LI X G, 2022. Carbon sink potential of Beijing's forest under carbon peak and carbon neutrality[J]. Resources & Industries, 24(1): 15-25.〕
赵明瑶, 刘会云, 张晓丽, 等, 2015. 基于林分结构响应的PALSAR森林结构参数估测[J]. 北京林业大学学报, 37(6): 61-69. 〔ZHAO M Y, LIU H Y, ZHANG X L, et al, 2015. Estimation of forest structural parameters based on stand structure response and PALSAR data[J]. Journal of Beijing Forestry University, 37(6): 61-69.〕
赵雅楠, 郭鹏程, 2021. 晋城市煤矿开采对植被覆盖度的影响研究[J]. 资源与产业, 23(3): 79-87. 〔ZHAO Y N, GUO P C, 2021. Impacts of coal mining on vegetation coverage in Jincheng city[J]. Resources & Industries, 23(3): 79-87.〕
周晓虎, 2020. 基于多源遥感数据的中国东北地区森林信息提取研究[D]. 长春: 吉林大学. 〔ZHOU X H, 2020. Research on forest information extraction in Northeast China based on multi-source remote sensing data[D]. Changchun: Jilin University.〕
朱建军, 李志伟, 胡俊, 2017. InSAR变形监测方法与研究进展[J]. 测绘学报, 46(10): 1717-1733.〔ZHU J J, LI Z W, HU J, 2017. Research progress and methods of InSAR for deformation monitoring[J].Acta Geodaetica et Cartographica Sinica, 46(10): 1717-1733.〕
ABER J D, CARD D H, MATSON P A, et al, 1988. Prediction of leaf chemistry by the use of visible and near infrared reflectance spectroscopy[J].Remote Sensing of Environment, 26(2): 123-147.
ALONSO-GONZALEZ A, HAJNESK I, 2018. Radar remote sensing of land surface parameters[M]//LI X, VEREECKEN H. Observation and measurement of ecohydrological processes. Berlin: Springer: 1-38.
BALZTER H, ROWLAND C S, SAICH P, 2007. Forest canopy height and carbon estimation at Monks Wood National Nature Reserve, UK, using dual-wavelength SAR interferometry[J]. Remote Sensing of Environment, 108(3): 224-239.
BELLON B, BLANCO J, DE VOS A, et al, 2020. Integrated landscape change analysis of protected areas and their surrounding landscapes: application in the Brazilian Cerrado[J]. Remote Sensing, 12(9): 1413.
BIAN Z F, LU Q Q, 2013. Ecological effects analysis of land use change in coal mining area based on ecosystem service valuing: a case study in Jiawang[J]. Environmental Earth Sciences, 68(6): 1619-1630.
BLASCH G, SPENGLER D, HOHMANN C, et al, 2015. Multitemporal soil pattern analysis with multispectral remote sensing data at the field-scale[J]. Computers & Electronics in Agriculture, 113(C): 1-13.
CHEN P Y, ZHANG Y C, JIA Z H, et al, 2017. Remote sensing image change detection based on NSCT-HMT model and its application[J]. Sensors, 17(6): 1295.
DAWSON T P, CRUUAN P J, 1998. Technical note a new technique for interpolating the reflectance red edge position[J].International Journal of Remote Sensing, 19(11): 2133-2139.
DOBSON M C, ULABY F T, LE TOAN T, 1992. Dependence of radar backscatter on coniferous forest biomass[J]. IEEE Transactions on Geoscience and Remote Sensing, 30(2): 412-415.
HERMITTE J L, LE TOAN T, GRIPPA M, et al, 2004. Monitoring the Siberian boreal forest using ENVISAT/ASAR data: first analysis results[C]. Anchorage: 2004 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2004).
HOWARD J A, 1991. Remote sensing of forest resources: theory and application[M]. London: Chapman & Hall.
KAHLE A B, 1987. Surface emittance, temperature, and thermal inertia derived from thermal infrared multispectral scanner(TIMS)data for Death Valley, California[J]. Geophysics, 52(7): 858-874.
KUGLER F, LEE S K, HAJNSEK I, et al, 2015. Forest height estimation by means of PolInSAR data inversion: the role of the vertical wavenumber[J]. IEEE Transactions on Geoscience and Remote Sensing, 53(10): 5294-5311.
LI H M, WANG Y, LUO X L, 2016. Tree height estimation at plateau mountains, northwestern Sichuan, China using dual Pol-InSAR data[C]. Beijing: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
LU S, 2013. Effects of leaf surface wax on leaf spectrum and hyperspectral vegetation indices[C].Melbourne: 2013 IEEE International Geoscience And Remote Sensing Symposium (IGARSS).
MATSUSHITA B, YANG W, CHEN J, et al, 2007. Sensitivity of the enhanced vegetation index(EVI)and normalized difference vegetation index(NDVI)to topographic effects: a case study in high-density cypress forest[J]. Sensors, 7(11): 2636-2651.
MUTANGA O, SKIDMORE A K, 2007. Red edge shift and biochemical content in grass canopies[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 62(1): 34-42.
NAGENDRA H, LUCAS R, HONRADO J , et al, 2013. Remote sensing for conservation monitoring: assessing protected areas, habitat extent, habitat condition, species diversity, and threats[J]. Ecological Indicators, 33(Special SI): 45-59.
PUGH L A, RAO K N, 1973. Spectrum of water-vapor in the 1-9 and 2-7mμ regions[J]. Journal of Molecular Spectroscopy, 47(3): 403-408.
RAHMAN M R, SHI Z H, CAI C F, 2014. Assessing regional environmental quality by integrated use of remote sensing, GIS, and spatial multi-criteria evaluation for prioritization of environmental restoration[J]. Environmental Monitoring & Assessment, 186(11): 6993-7009.
SAKAMOTO M, TANI M, MORIYAMA M, 2018. Remote sensing characterization of changes in forest resources and betel leaf cultivation through time[M]// TANI M, RAHMAN M A. Deforestation in the Teknaf Peninsula of Bangladesh. Singapore: Springer: 77-84.
SANTORO M, BEAUDOIN A, BEER C, et al, 2015. Forest growing stock volume of the northern hemisphere: spatially explicit estimates for 2010 derived from Envisat ASAR[J].Remote Sensing of Environment, 168: 316-334.
SIMS D A, GAMON J A, 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages[J].Remote Sensing of Environment, 81(2/3): 337-354.
SUZUKI R, KOBAYASHI H, DELBART N, et al, 2011. NDVI responses to the forest canopy and floor from spring to summer observed by airborne spectrometer in eastern Siberia[J]. Remote Sensing of Environment, 115(12): 3615-3624.
TANG P Q, YAO Y M, WEI N, 2009. Advance of rice recognition and monitoring by SAR[J]. Agricultural Science & Technology, 10(6): 184-188.
ULANDER L M H, DAMMERT P B G, HAGBERG J O, 1995. Measuring tree height using ERS-1 SAR interferometry[C]. Firenze: 1995 International Geoscience and Remote Sensing Symposium, IGARSS'95.
WANG J, LI J Y, HAN P, et al, 2022. Inversion for parameters of Tamarix Chinensis Forest from SAR and InSAR[J]. Polish Journal of Environmental Studies, 31(2): 1837-1845.
WANG S Q, WANG J B, ZHANG L M, et al, 2019. A national key R&D program: technologies and guidelines for monitoring ecological quality of terrestrial ecosystems in China[J]. Journal of Resources and Ecology, 10(2): 105-111.
WANG Y, HESS L L, FILOSO S, et al, 1995. Understanding the radar backscattering from flooded and nonflooded Amazonian forests: results from canopy backscatter modeling[J]. Remote Sensing of Environment, 54(3): 324-332.
XU Y L, LI C, SUN Z C, et al, 2019. Tree height explains stand volume of closed-canopy stands: evidence from forest inventory data of China[J]. Forest Ecology and Management, 438: 51-56.
YAMADA H, YAMAGUCHI Y, KIM Y J, et al, 2001. Polarimetric SAR interferometry for forest analysis based on the ESPRIT algorithm[J]. IEICE Transactions on Electronics, 84(12): 1917-1924.
|