浏览全部资源
扫码关注微信
网络出版日期： 2023-09-18 ，
扫 描 看 全 文
王梦雨，赵峰，庞勇，孟冉，荚文，岳超.XXXX.基于人工合成样本和随机森林回归模型的长时序中国寒温带森林类型和树种覆盖度反演研究.遥感学报，XX（XX）： 1-16
Wang Mengyu，Zhao Feng，Pang Yong，Meng Ran，Jia Wen，Yue Chao. XXXX. Mapping forest type and tree species fractions of China’s cold-temperate forest based on synthetically mixed data and random forest regression. National Remote Sensing Bulletin， XX（XX）：1-16
寒温带森林是陆地上分布面积最广的森林生态系统，具有重要的生态和社会经济价值。定量刻画长时序寒温带森林类型和树种覆盖信息对于量化其生态系统服务功能以及制定森林管理政策具有重要意义。然而受实测覆盖度数据缺乏和多光谱影像光谱信息有限的限制，现有研究较少探讨中分辨率多光谱星载数据（如Landsat卫星）对中国寒温带森林类型覆盖度和树种覆盖度进行长时序反演的可行性，并且对于遥感影像获取时间频率（单时相、多时相）对反演精度的影响仍缺乏定量评估。为此，本文利用人工合成样本和随机森林回归模型对黑龙江省孟家岗林场的森林类型和树种覆盖度分别进行了反演。并将模型应用至1986-2020年的Landsat影像，得到孟家岗林场阔叶林和针叶林35年的覆盖度结果。结果表明：（1）对于森林类型覆盖度反演，基于生长季Landsat波段和植被指数（归一化耕作指数以及缨帽变换系数）的中值特征估算的精度最高，阔叶林覆盖度估算
<math id="M1"><msup><mrow><mover accent="true"><mi>R</mi><mo>¯</mo></mover></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msup></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47835626&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47835602&type=
2.96333337
2.96333337
= 0.76，针叶林覆盖度估算
<math id="M2"><msup><mrow><mover accent="true"><mi>R</mi><mo>¯</mo></mover></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msup></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47835626&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47835602&type=
2.96333337
2.96333337
= 0.71；（2）对于树种覆盖度反演，基于多时相Landsat波段和植被指数的精度最高，落叶松覆盖度估算
<math id="M3"><msup><mrow><mover accent="true"><mi>R</mi><mo>¯</mo></mover></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msup></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47835626&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47835602&type=
2.96333337
2.96333337
= 0.40，红松覆盖度估算
<math id="M4"><msup><mrow><mover accent="true"><mi>R</mi><mo>¯</mo></mover></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msup></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47835626&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47835602&type=
2.96333337
2.96333337
= 0.23，樟子松覆盖度估算
<math id="M5"><msup><mrow><mover accent="true"><mi>R</mi><mo>¯</mo></mover></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msup></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47835759&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47835754&type=
2.96333337
2.96333337
= 0.61；（3）增加影像获取时间密度对于森林类型覆盖度反演精度的提高没有显著贡献（阔叶林∆
<math id="M6"><msup><mrow><mover accent="true"><mi>R</mi><mo>¯</mo></mover></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msup></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47835759&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47835754&type=
2.96333337
2.96333337
= 0.01，针叶林∆
<math id="M7"><msup><mrow><mover accent="true"><mi>R</mi><mo>¯</mo></mover></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msup></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47835759&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47835754&type=
2.96333337
2.96333337
= -0.03），但对提高树种覆盖度的反演精度帮助较大（落叶松
<math id="M8"><mo>∆</mo><msup><mrow><mover accent="true"><mi>R</mi><mo>¯</mo></mover></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msup></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47835799&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47835771&type=
4.74133301
2.96333337
= 0.04，红松
<math id="M9"><mo>∆</mo><msup><mrow><mover accent="true"><mi>R</mi><mo>¯</mo></mover></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msup></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47835821&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47835810&type=
4.74133301
2.96333337
= 0.07，樟子松
<math id="M10"><mo>∆</mo><msup><mrow><mover accent="true"><mi>R</mi><mo>¯</mo></mover></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msup></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47835889&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47835850&type=
4.74133301
2.96333337
= 0.27）。本文结合人工合成样本和随机森林回归模型的优势，定量评估了不同时间频率的时序光谱特征对森林类型和树种覆盖度反演的精度，为中国北方森林以及全球寒温带森林类型和树种覆盖度的大尺度长时序估算提供了思路。
Cold-temperate forests
recognized as the most extensive terrestrial ecosystems
cover vast areas around the global and hold very important ecological and social values. Accurate mapping of forest type and tree species cover fraction in these forests across space and time is crucial for quantifying ecosystem services and formulating effective forest management policies to ensure their sustainable conservation. However
despite the increasing development of remote sensing technologies
there have been limited studies exploring the feasibility of inverting forest type and tree species cover fraction using medium-resolution multispectral satellite-based data
such as Landsat
in China's cold-temperate forests. This limitation is primarily attributed to the scarcity of reference data and the restricted spectral information available in multispectral images. Moreover
the quantitative impact of the temporal frequency of data acquisitions (e.g.
single-date
multi-date) on mapping forest type and tree species cover fraction remains largely unexplored. The timing and frequency of satellite data acquisition can significantly influence the detection and characterization of dynamic changes in forests
which
in turn
affect the accuracy of mapping forest attributes. To address these gaps
our study aims to map the forest type and tree species cover fraction in Mengjiagang forest
Heilongjiang Province
by employing synthetically mixed data and a random forest regression model. Furthermore
we extend our analysis to three decades (from 1986 to 2020) of the Landsat data
thereby mapping the cover fractions of both broadleaf and needleleaf forests in Mengjiagang forest by using the optimal broadleaf and needleleaf random forest regression model. The results of our study reveal the following key findings: (1) For forest type cover fraction inversion
the random forest regression model based on the growing season median index (including spectral bands
NDTI
and TCT indices) was the optimal model (achieving an
<math id="M11"><msup><mrow><mover accent="true"><mi>R</mi><mo>¯</mo></mover></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msup></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47835940&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47835917&type=
3.30200005
2.87866688
=0.76 for broadleaf and
<math id="M12"><msup><mrow><mover accent="true"><mi>R</mi><mo>¯</mo></mover></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msup></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47836085&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47836062&type=
3.30200005
2.87866688
=0.71 for needleleaf
respectively); (2) For tree species cover fraction inversion
the random forest regression model based on the multi-date spectral features (including spectral bands
NDTI
and TCT indices of both growth season and leaf off season) was the optimal model (achieving an
<math id="M13"><msup><mrow><mover accent="true"><mi>R</mi><mo>¯</mo></mover></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msup></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47836085&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47836062&type=
3.30200005
2.87866688
=0.40 for Larch
<math id="M14"><msup><mrow><mover accent="true"><mi>R</mi><mo>¯</mo></mover></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msup></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47836085&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47836062&type=
3.30200005
2.87866688
=0.23 for Korean pine
and
<math id="M15"><msup><mrow><mover accent="true"><mi>R</mi><mo>¯</mo></mover></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msup></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47836085&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47836062&type=
3.30200005
2.87866688
=0.61 for Mongolian pine
respectively); (3) Increasing the temporal frequency of data acquisition can enhance tree species cover fraction inversion accuracy (achieving an
<math id="M16"><mo>∆</mo><msup><mrow><mover accent="true"><mi>R</mi><mo>¯</mo></mover></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msup></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47836107&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47836108&type=
5.50333309
3.13266683
= 0.04 for Larch
<math id="M17"><mo>∆</mo><msup><mrow><mover accent="true"><mi>R</mi><mo>¯</mo></mover></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msup></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47836163&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47836156&type=
5.50333309
3.13266683
= 0.07 for Korean pine
and
<math id="M18"><mo>∆</mo><msup><mrow><mover accent="true"><mi>R</mi><mo>¯</mo></mover></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msup></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47836163&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=47836156&type=
5.50333309
3.13266683
= 0.27 for Mongolian pine
respectively)
while its impact on improving forest type cover fraction inversion accuracy was limited. By effectively combining the advantages of synthetically training data and random forest regression
we have successfully mapped the forest type and tree species cover fraction of Mengjiagang forest. Moreover
our study provides a comprehensive analysis that accurately quantifies the influence of temporal data acquisition frequency on mapping forest type and tree species cover fraction. Our study offers valuable insights into the future mapping of forest type and tree species cover fraction across space and time
particularly for regions with similar species composition. The outcomes of this research will make a significant contribution to the understanding and management of cold-temperate forests
thereby supporting their conservation and sustainable use.
森林类型覆盖度树种覆盖度人工合成样本长时间序列
forest type coveragetree species coveragesynthetically training datalong time series
1Bonan G B, Pollard D, and Thompson S L. 1992. Effects of boreal forest vegetation on global climate. Nature, 359(6397):716-718
2Breiman L. 1999. RANDOM FORESTS--RANDOM FEATURES. Machine Learning.
3Brinck K, Fischer R, Groeneveld J, Lehmann S, Dantas De Paula M, Putz S, Sexton J O, Song D and Huth A. 2017. High resolution analysis of tropical forest fragmentation and its impact on the global carbon cycle. Nature Communications, 8: 14855
4Chen J, He Y, He C, and Shi P. 2001. Sub-pixel model for vegetation fraction estimation based on land cover classification. Journal of Remote Sensing, 5(6): 416-422
陈晋, 陈云浩, 何春阳, 史培军. 2001. 基于土地覆盖分类的植被覆盖率估算亚像元模型与应用. 遥感学报, 5(6): 416-422
5Chen J, Ma L, Chen X, and Rao Y. 2016. Research progess of spectral mixture analysis. Journal of Remote Sensing, 20(5):1102-1109
陈晋, 马磊, 陈学泓, 饶玉晗. 2016. 混合像元分解技术及其进展. 遥感学报, 20(5): 1102-1109
6Chen S, Shahi C, and Chen H, 2016. Economic and ecological trade-off analysis of forest ecosystems: options for boreal forests. Environmental Reviews, 24(3):348-361
7Deng C, and Zhu Z. 2020. Continuous subpixel monitoring of urban impervious surface using Landsat time series, Remote Sensing of Environment, 238: 110929
8Drever C R, Cook-Patton S C, Akhter F, Badiou P H, Chmura G L, Davidson S J, Desjardins R L, Dyk A, Fargione J E, and Fellows M. 2021. Natural climate solutions for Canada. Science Advances, 7(23): eabd6034
9FAO. 2021. Global Forest Rescources Assessment 2020: main report. Food and Agriculture Organization of the United Nations, Rome.
10Foga S, Scaramuzza P L, Guo S, Zhu Z, Dilley Jr R D, Beckmann T, Schmidt G L, Dwyer J L, Hughes M J, and Laue B. 2017. Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194: 379-390
11Girardin C A, Jenkins S, Seddon N, Allen M, Lewis S L, Wheeler C E, Griscom B W, and Malhi Y. 2021. Nature-based solutions can help cool the planet—if we act now [M]. Nature Publishing Group
12Guerschman J P, Hill M J, Renzullo L J, Barrett D J, Marks A S, and Botha E J. 2009. Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors. Remote Sensing of Environment, 113(5): 928-945
13Hansen M C, Potapov P V, Moore R, Hancher M, Turubanova S A, Tyukavina A, Thau D, Stehman S V, Goetz S J, and Loveland T R. 2013. High-resolution global maps of 21st-century forest cover change. Science, 342(6160): 850-853
14Heilmayr R, Echeverría C, and Lambin E F. 2020. Impacts of Chilean forest subsidies on forest cover, carbon and biodiversity. Nature Sustainability, 3(9): 701-709
15Hemmerling J, Pflugmacher D, and Hostert P. 2021. Mapping temperate forest tree species using dense Sentinel-2 time series. Remote Sensing of Environment, 267: 112743
16Hu S, Hu D, and Zhao W. 2010. Extract urban vegetation coverage based on LSMM and improved FCM: a case study in Haidian District. Acta Ecologica Sinica, 30(04):1018-1024
胡妹婧, 胡德勇, 赵文吉. 2010. 基于LSMM和改进的FCM提取城市植被覆盖度--以北京市海淀区为例. 生态学报, 30(04): 1018-1024
17Huang C, and Townshend J R G. 2003. A stepwise regression tree for nonlinear approximation: Applications to estimating subpixel land cover. International Journal of Remote Sensing, 24(1): 75-90
18Ji C, Jia Y, Li X, and Wang J. 2016. Research on linear and nonlinear spectral mixture models for estimating vegetation fractional cover of nitraria bushes. Journal of Remote Sensing, 20(6):1402-1412
姬翠翠, 贾永红, 李晓松, 王金英. 2016. 线性/非线性光谱混合模型估算白刺灌丛植被覆盖度. 遥感学报, 20(6): 1402-1412
19Jia W, and Pang Y. 2023. Tree species classification in an extensive forest area using airborne hyperspectral data under varying light conditions. Journal of Forestry Research
20Jiang H, Apps M J, Peng C, Zhang Y, and Liu J. 2002. Modelling the influence of harvesting on Chinese boreal forest carbon dynamics. Forest Ecology and Management, 169(1-2):65-82
21John B A, Donald E S, Valerie K, Raimundo A F, Dar A R, Milton O S, and Alan R G. 1995. Classification of multispectral images based on fractions of endmembers: Application to land-cover change in the Brazilian Amazon. Remote Sensing of Environment, 52(2): 137-154
22Kattenborn T, Eichel J, Wiser S, Burrows L, Fassnacht F E, Schmidtlein S, Horning N, and Clerici N. 2020. Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery. Remote Sensing in Ecology and Conservation, 6(4): 472-486
23Lei G, Li A, Tan J, Zhang Z, Bian J, Jin H, Zhao W, and Cao X. 2016. Forest types mapping in mountainous area using multi-source and multi-temporal satellite images and decision tree models. Remote Sensing Technology and Application, 31(1): 31-41
雷光斌, 李爱农, 谭剑波, 张正健, 边金虎, 靳华安, 赵伟, 曹小敏. 2016. 基于多源多时相遥感影像的山地森林分类决策树模型研究. 遥感技术与应用, 31(1):31-41
24Li Q, Wong F K K, and Fung T. 2021. Mapping multi-layered mangroves from multispectral, hyperspectral, and LiDAR data. Remote Sensing of Environment, 258: 112403
25Li Y, Wang H, and Li X B. 2015. Fractional vegetation cover estimation based on an improved selective endmember spectral mixture model. PLOS One, 10(4): e0124608
26Liu Y, Zeng P, Zhang R, Sun F, and Che Y. 2021. Vegetation coverage change of the demonstration area of ecologically friendly development in the Yangtze River Delta, China based on GEE and BRT during 1984-2019. Chinese Journal of Applied Ecology, 32(03):1033-1044
刘垚燚, 曾鹏, 张然, 孙凤云, 车越. 2021. 基于GEE和BRT的1984—2019年长三角生态绿色一体化发展示范区植被覆盖度变化. 应用生态学报, 32(03):1033-1044
27Long S, Guo Z, Xu L, Zhou H, Fang W, and Xu Y. 2020. Spatiotemporal variations of fractional vegetation coverage in China based on Google Earth Engine. Remote Sensing Technology and Application, 35(02):326-334
龙爽, 郭正飞, 徐粒, 周华真, 方伟华, 许映军. 2020. 基于Google Earth Engine的中国植被覆盖度时空变化特征分析. 遥感技术与应用, 35(02):326-334
28Michishita R, Jiang Z, Gong P, and Xu B. 2012. Bi-scale analysis of multitemporal land cover fractions for wetland vegetation mapping. ISPRS Journal of Photogrammetry and Remote Sensing, 72: 1-15
29Morsdorf F, Kötz B, Meier E, Itten K I, and Allgöwer B. 2006. Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction. Remote Sensing of Environment, 104(1): 50-61
30Okujeni A, Van Der Linden S, Jakimow B, Rabe A, Verrelst J, and Hostert P. 2014. A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover. Remote Sensing, 6(7): 6324-6346
31Okujeni A, Van Der Linden S, Tits L, Somers B, and Hostert P. 2013. Support vector regression and synthetically mixed training data for quantifying urban land cover. Remote Sensing of Environment, 137: 184-197
32Okujeni A, Van Der Linden S, and Hostert P. 2015. Extending the vegetation-impervious-soil model using simulated EnMAP data and machine learning. Remote Sensing of Environment, 158: 69-80
33Okujeni A, Van Der Linden S, Suess S, and Hostert P. 2017. Ensemble learning from synthetically mixed training data for quantifying urban land cover with support vector regression. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(4):1640-1650
34Okujeni A, Janicke C, Cooper S, Frantz D, Hostert P, Clark M, Segl K, and Van Der Linden S. 2021. Multi-season unmixing of vegetation class fractions across diverse Californian ecoregions using simulated spaceborne imaging spectroscopy data. Remote Sensing of Environment, 264: 112558
35Pasquarella V J, Holden C E, and Woodcock C E. 2018. Improved mapping of forest type using spectral-temporal Landsat features. Remote Sensing of Environment, 210: 193-207
36Peng S, Ding Y, Liu W, and Li Z. 2019. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth System Science Data, 11(4), 1931–1946
37Persson M, Lindberg E, and Reese H. 2018. Tree species classification with multi-temporal Sentinel-2 data. Remote Sensing, 10(11): 1794
38Pi X, Zeng Y, and He C. 2021. High-resolution urban vegetation coverage estimation based on multi-sourve remote sensing data fusion. Journal of Remote Sensing, 25(06):1216-1226
皮新宇, 曾永年, 贺城墙 2021. 融合多源遥感数据的高分辨率城市植被覆盖度估算. 遥感学报, 25(06):1216-1226
39Powell R, Roberts D, Dennison P, and Hess L. 2007. Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil. Remote Sensing of Environment, 106(2): 253-267
40Qian X, Zhang Y, Liu L, and Du S. 2019. Exploring the potential of leaf reflectance spectra for retrieving the leaf maximum carboxylation rate. International Journal of Remote Sensing, 40(14):1-18
41Savage S. L., Lawrence R. L., and Squires J R. 2015. Predicting relative species composition within mixed conifer forest pixels using zero-inflated models and Landsat imagery, Remote Sensing of Environment, 171: 326-336
42Schug F, Frantz D, Okujeni A, Van Der Linden S, and Hostert P. 2020. Mapping urban-rural gradients of settlements and vegetation at national scale using Sentinel-2 spectral-temporal metrics and regression-based unmixing with synthetic training data. Remote Sensing of Environment, 246: 111810
43Senf C, Laštovička J, Okujeni A, Heurich M, and Van Der Linden S. 2020. A generalized regression-based unmixing model for mapping forest cover fractions throughout three decades of Landsat data. Remote Sensing of Environment, 240:111691
44Srisa-An C. 2021. Guideline of Collinearity - Avoidable Regression Models on Time-series Analysis. 2021 2nd International Conference on Big Data Analytics and Practices (IBDAP), 28-32
45Venäläinen A, Lehtonen I, Laapas M, Ruosteenoja K, Tikkanen O P, Viiri H, Ikonen V P, and Peltola H. 2020. Climate change induces multiple risks to boreal forests and forestry in Finland: A literature review. Global change biology, 26(8): 4178-4196.
46Wang J, Xiao X, Liu L, Wu X, Qin Y, Steier J L, and Dong J. 2020. Mapping sugarcane plantation dynamics in Guangxi, China, by time series Sentinel-1, Sentinel-2 and Landsat images. Remote Sensing of Environment, 247: 111951
47Wang G, Wang J, Han L, Chai G, and Wang Z. 2018. Estimating fractional cover of non-photosynthetic vegetation using field spectral to simulate Landsat-8 OLI. Journal of Geo-Information Science, 20(11):1667-1678
王光镇, 王静璞, 韩柳, 柴国奇, 王周龙. 2018. 基于实测光谱模拟Landsat-8 OLI数据估算非光合植被覆盖度.地球信息科学学报, 20(11): 1667-1678
48Wang M, Zheng Y, Huang C, Meng R, Pang Y, Jia W, Zhou J, Huang Z, Fang L, and Zhao F. 2022. Assessing Landsat-8 and Sentinel-2 spectral-temporal features for mapping tree species of northern plantation forests in Heilongjiang Province, China. Forest Ecosystems, 9(3): 13
49Wang S, Zhang L, Lin W, Huang Q, Song Y, and Ye M. 2022. Study on vegetation coverage and land-use change of Guangdong Province based on MODIS-NDVI. Acta Ecologica Sinica, 42(06):2149-2163
王思, 张路路, 林伟彪, 黄秋森, 宋亦心, 叶脉. 2022. 基于MODIS-归一化植被指数的广东省植被覆盖与土地利用变化研究. 生态学报, 42(06):2149-2163
50Wang X, and Zhang Y. 2021. An analysis of change trend of fractional vegetation cover in Beijing based on Landsat imagery. Remote Sensing Technology and Application, 36(06):1388-1397
王曦, 张怡雯. 2021. 基于Landsat影像的北京植被覆盖度变化趋势分析. 遥感技术与应用, 36(06):1388-1397
51Wu Z, He G, Wang M, Fu J, and Zou D. 2016. Estimation of fractional vegetation cover and its spectral-temporal variation in hilly area of Southeastern China: Yongding County, Fujian Province. Remote Sensing Technology and Application, 31(06):1201-1208
吴志杰, 何国金, 王猛猛, 傅娇凤, 邹丹. 2016. 南方丘陵区植被覆盖度遥感估算与时空变化研究——以福建省永定县为例. 遥感技术与应用, 31(06):1201-1208
52Xiao J, and Moody A. 2005. A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA. Remote Sensing of Environment, 98(2-3): 237-250
53Zhang M, Zhang X, Zhao D, Huang C, and Zhang L. 2011. An approach to generate synthetic hyperspectral data used for evaluating linear spectral unmixing algorithms. Remote Sensing Technology and Application, 27(05):680-685
张明, 张霞, 赵东, 黄长平, 张立福. 2011. 一种用于线性光谱解混算法验证的模拟数据生成方法. 遥感技术与应用, 27(05):680-685
54Zhang P, Shao G, Zhao G, Le Master D C, Parker G R, Dunning J B, and Li Q. 2000. China's forest policy for the 21st century. Science, 288(5474): 2135-2136
55Zhao Y, Zeng Y, Zheng Z, Zheng Z, Dong W, Zhao D Wu B, and Zhao Q. 2018. Forest species diversity mapping using airborne LiDAR and hyperspectral data in a subtropical forest in China. Remote Sensing of Environment, 213: 104-114
相关作者
相关机构
京公网安备11010802024621