[1] 李佳佳. 小麦生物物理与生物化学参数的高光谱遥感监测[D]. 南京: 南京信息工程大学, 2015.
LI J J. Monitoring bio-physical and bio-chemical parameters of wheat by hyper-spectral remote sensing[D]. Nanjing: Nanjing University of Information Science and Technology, 2015. (in Chinese)
[2] 张凯, 王润元, 王小平, 赵鸿, 韩海涛. 黄土高原春小麦地上鲜生物量高光谱遥感估算模型. 生态学杂志, 2009, 28(6): 1155-1161.
ZHANG K, WANG R Y, WANG X P, ZHAO H, HAN H T. Hyperspectral remote sensing estimation models for aboveground fresh biomass of spring wheat on Loess Plateau. Chinese Journal of Ecology, 2009, 28(6): 1155-1161. (in Chinese)
[3] 尚艳. 不同氮水平下小麦冠层光谱特征及其与农学参数关系研究[D]. 杨凌: 西北农林科技大学, 2015.
SHANG Y. Wheat canopy spectral features and its research relationship with agronomy parameter under different nitrogen levels[D]. Yangling: Northwest Agriculture and Forestry University, 2015. (in Chinese)
[4] 范云豹, 宫兆宁, 赵文吉, 张敏. 基于高光谱遥感的植被生物量反演方法研究. 河北师范大学学报(自然科学版), 2016, 40(3): 267-271.
FAN Y B, GONG Z N, ZHAO W J, ZHANG M. Study on vegetation biomass inversion method based on hyperspectral remote sensing. Journal of Hebei Normal University (Natural Science Edition), 2016, 40(3): 267-271. (in Chinese)
[5] 谭昌伟, 杨昕, 罗明, 马昌, 严翔, 陈亭亭. 以HJ-CCD影像为基础的冬小麦孕穗期关键苗情参数遥感定量反演. 中国农业科学, 2015, 48(13): 2518-2527.
TAN C W, YANG X, LUO M, MA C, YAN X, CHEN T T. Quantitative inversion of key seedling condition parameters in winter wheat at booting stage using remote sensing based on HJ-CCD images.
Scientia Agricultura Sinica, 2015, 48(13): 2518-2527. (in Chinese)
[6] 陈鹏飞, 王卷乐, 廖秀英, 尹芳, 陈宝瑞, 刘睿. 基于环境减灾卫星遥感数据的呼伦贝尔草地地上生物量反演研究. 自然资源学报, 2010, 25(7): 1122-1131.
CHEN P F, WANG J L, LIAO X Y, YIN F, CHEN B R, LIU R. Using data of HJ-1A/B for hulunbeier grassland aboveground biomass estimation. Journal of Nature Resources, 2010, 25(7): 1122-1131. (in Chinese)
[7] 高明亮, 宫兆宁, 赵文吉, 高阳, 胡东. 基于植被指数的北京军都山荆条灌丛生物量反演研究. 生态学报, 2014, 34(5): 1178-1188.
GAO M L, GONG Z N, ZHAO W J, GAO Y, HU D. The study of Vitex negundo shrubs canopy biomass inversion in Beijing Jundu mountains area based on vegetation indice. Acta Ecologica Sinica, 2014, 34(5): 1178-1188. (in Chinese)
[8] 赵天舸, 于瑞宏, 张志磊, 白雪松, 曾庆奥. 湿地植被地上生物量遥感估算方法研究进展. 生态学杂志, 2016, 35(7): 1936-1946.
ZHAO T G, YU R H, ZHANG Z L, BAI X S, ZENG Q A. Estimation of wetland vegetation aboveground biomass based on remote sensing data: A review. Chinese Journal of Ecology, 2016, 35(7): 1936-1946. (in Chinese)
[9] SHIBAYAMA M, AKIYAMA T. Seasonal visible, near-infrared and mid-infrared spectra of rice canopies in relation to LAI and above-ground dry phytomass. Remote Sensing of Environment, 1989, 27(2): 119-127.
[10] 侯学会, 牛铮, 黄妮, 许时光. 小麦生物量和真实叶面积指数的高光谱遥感估算模型. 国土资源遥感, 2012, 24(4): 30-35.
HOU X H, NIU Z, HUANG N, XU S G. The hyperspectral remote sensing estimation models of total biomass and true LAI of wheat. Remote Sensing for Land & Resources, 2012, 24(4): 30-35. (in Chinese)
[11] 刘琼阁, 彭道黎, 涂云燕, 李艳丽, 高东启. 基于偏最小二乘的森林生物量遥感估测. 东北林业大学学报, 2014, 42(7): 44-47.
LIU Q G, PENG D L, TU Y Y, LI Y Y, GAO D Q. Estimating forest biomass by partial least squares regression. Journal of Northeast Forestry University, 2014, 42(7): 44-47. (in Chinese)
[12] 陈鹏飞, Nicolas T, 王纪华, PHILIPPE V, 黄文江, 李保国. 估测作物冠层生物量的新植被指数的研究. 光谱学与光谱分析, 2010, 30(2): 512-517.
CHEN P F, NICOLAS T, WANG J H, PHILIPPE V, HUANG W J, LI B G. New index for crop canopy fresh biomass estimation. Spectroscopy and Spectral Analysis, 2010, 30(2): 512-517. (in Chinese)
[13] 刘俊, 毕华兴, 朱沛林, 孙菁, 朱金兆, 陈涛. 基于 ALOS 遥感数据纹理及纹理指数的柞树蓄积量估测. 农业机械学报, 2014, 45(7): 245-254.
LIU J, BI H X, ZHU P L, SUN J, ZHU J Z, CHEN T. Estimating stand volume of xylosma racemosum forest based on texture parameters and derivative texture indices of ALOS imagery. Transactions of the Chinese Society for Agricultural Machinery, 2014, 45(7): 245-254. (in Chinese)
[14] GU Z J, JU W M, LI L, LI D Q, LIU Y B, FAN W L. Using vegetation indices and texture measures to estimate vegetation fractional coverage (VFC) of planted and natural forests in Nanjing city, China. Advances in Space Research, 2013, 51(7): 1186-1194.
[15] SARKER L R, NICHOL J E. Improved forest biomass estimates using ALOS AVNIR-2 texture indices. Remote Sensing of Environment, 2011, 115(4): 968-977.
16] 曹庆先, 徐大平, 鞠洪波. 基于TM影像纹理与光谱特征的红树林生物量估算. 林业资源管理, 2010, 12(6): 102-107.
CAO Q X, XU D P, JU H B. The biomass estimation of mangrove community based on the textural features and spectral information of TM images. Forest Resources Management, 2010, 12(6): 102-107. (in Chinese)
[17] 牧其尔, 高志海, 包玉海, 王琫瑜, 白黎娜. 植被指数纹理特征信息估测稀疏植被生物量. 遥感信息, 2016, 31(1): 58-63.
MU Q E, GAO Z H, BAO Y H, WANG B Y, BAI L N. Estimation of sparse vegetation biomass based on grea-level co-occurrence matrix of vegetation indices. Remote Sensing Information, 2016, 31(1): 58-63. (in Chinese)
[18] YUE J B, YANG G J, LI C C, LI Z H, WANG Y J, FENG H K, XU B. Estimation of winter wheat above-ground biomass using unmanned aerial vehicle-based snapshot hyperspectral sensor and crop height improved models. Remote Sensing, 2017, 9(70): 801-819.
[19] 邓书斌. ENVI遥感图像处理方法. 北京: 科学出版社, 2010.
DENG S B. ENVI Remote Sensing Image Processing Method. Beijing: Science Press, 2010. (in Chinese)
[20] HARALICK R M, SHANMUGAM K, DINSTEIN I. Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics, 1973, 3(6): 768-780.
[21] 刘广东. 基于TM影像植被指数和纹理特征的对比关系研究[D]. 重庆: 重庆师范大学, 2010.
LIU G D. Study on the contrast relationships between NDVI and texture features based on TM image [D]. Chongqing: Chongqing Normal University, 2010. (in Chinese)
[22] FRANKLIN S E, HALL R J, MOSKAL L M. Incorporating texture into classification of forest species composition from airborne multispectral images. International Journal of Remote Sensing, 2000, 21(1): 61-79.
[23] TREITZ P, HOWARTH P. Integrating spectral, spatial, and terrain variables for forest ecosystem classification. Photogrammetric Engineering & Remote Sensing, 2000, 66(3): 305-318.
[24] JOHANSEN K, COOPS N C, GERGEL S E. Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification. Remote Sensing of Environment, 2007, 110(1): 29-44.
[25] FRANKLIN S E, WULDER M A, GERYLO G R. Texture analysis of IKONOS panchromatic data for Douglas-fir forest age class separability in British Columbia. International Journal of Remote Sensing, 2001, 22(13): 2627-2632.
[26] KAYITAKIRE F, HAMEL C, DEFOURNY P. Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery. Remote Sensing of Environment, 2006, 102(3/4): 390-401.
[27] OZDEMIR I, KARNIELI A. Predicting forest structural parameters using the image texture derived from WorldView-2 multispectral imagery in a dryland forest, Israel. International Journal of Applied Earth Observation & Geoinformation, 2011, 13(5): 701-710.
[28] 李明诗, 谭莹, 潘洁, 彭世揆. 结合光谱、纹理及地形特征的森林生物量建模研究. 遥感信息, 2006(6): 6-9.
LI M S, TAN Y, PAN J, PENG S K. Modeling forest aboveground biomass by combining the spectrum, textures with topographic features. Remote Sensing Information, 2006, 2006(6): 6-9. (in Chinese)
[29] SARKER M L, NICHOL J, IZ H B, AHMAD B B, RAHMAN A A. Forest biomass estimation using texture measurements of high- resolution dual-polarization C-band SAR data. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(6): 3371-3384.
[30] MIGUEL A C, MARTIN R, BERNARDUS H J. Estimation of tropical forest structure from SPOT-5 satellite images. International Journal of Remote Sensing, 2010, 31(10): 2767-2782. |