کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4972782 | 1451243 | 2017 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Improving the prediction of African savanna vegetation variables using time series of MODIS products
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کلمات کلیدی
EVIrRMSERelative root mean square errorFPARAdvanced Very High Resolution RadiometerPCQdpmSPOTSWIRNPPAVHRRRMSEPPLSRNIRPartial Least Square Regression (PLSR)Moderate Resolution Imaging Spectroradiometer - Spectroradiometer تصویربرداری با وضوح تصویر متوسطNet primary productivity - بهره وری اولیهMODIS - تابشسنج طیفی تصویربرداری با وضوح متوسط یا MODIS Partial least square regression - حداقل رگرسیون حداقل مربعRoot Mean Squared Error of Prediction - ریشه متوسط مربع خطا پیش بینیGIS - سامانه اطلاعات جغرافیاییGeographic information systems - سیستم های اطلاعات جغرافیاییnormalized difference vegetation index - شاخص تنوع گیاه شناسی نرمال شدهEnhanced Vegetation Index - شاخص رشد گیاهیEnhanced vegetation index (EVI) - شاخص رشد گیاهی (EVI)Leaf area index - شاخص سطح برگLeaf area index (LAI) - شاخص سطح برگ (LAI)NDVI - شاخص نرمالشده تفاوت پوشش گیاهی Vegetation index - شاخص گیاهیLAI - شبیهModel transferability - قابلیت انتقال مدلShortwave infrared - مادون قرمز مادون قرمزNear infrared - مادون قرمز نزدیکVegetation cover - پوشش گیاهیquality control - کنترل کیفیت
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
سیستم های اطلاعاتی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
African savanna vegetation is subject to extensive degradation as a result of rapid climate and land use change. To better understand these changes detailed assessment of vegetation structure is needed across an extensive spatial scale and at a fine temporal resolution. Applying remote sensing techniques to savanna vegetation is challenging due to sparse cover, high background soil signal, and difficulty to differentiate between spectral signals of bare soil and dry vegetation. In this paper, we attempt to resolve these challenges by analyzing time series of four MODIS Vegetation Products (VPs): Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) for Etosha National Park, a semiarid savanna in north-central Namibia. We create models to predict the density, cover, and biomass of the main savanna vegetation forms: grass, shrubs, and trees. To calibrate remote sensing data we developed an extensive and relatively rapid field methodology and measured herbaceous and woody vegetation during both the dry and wet seasons. We compared the efficacy of the four MODIS-derived VPs in predicting vegetation field measured variables. We then compared the optimal time span of VP time series to predict ground-measured vegetation. We found that Multiyear Partial Least Square Regression (PLSR) models were superior to single year or single date models. Our results show that NDVI-based PLSR models yield robust prediction of tree density (R2Â =Â 0.79, relative Root Mean Square Error, rRMSEÂ =Â 1.9%) and tree cover (R2Â =Â 0.78, rRMSEÂ =Â 0.3%). EVI provided the best model for shrub density (R2Â =Â 0.82) and shrub cover (R2Â =Â 0.83), but was only marginally superior over models based on other VPs. FPAR was the best predictor of vegetation biomass of trees (R2Â =Â 0.76), shrubs (R2Â =Â 0.83), and grass (R2Â =Â 0.91). Finally, we addressed an enduring challenge in the remote sensing of semiarid vegetation by examining the transferability of predictive models through space and time. Our results show that models created in the wetter part of Etosha could accurately predict trees' and shrubs' variables in the drier part of the reserve and vice versa. Moreover, our results demonstrate that models created for vegetation variables in the dry season of 2011 could be successfully applied to predict vegetation in the wet season of 2012. We conclude that extensive field data combined with multiyear time series of MODIS vegetation products can produce robust predictive models for multiple vegetation forms in the African savanna. These methods advance the monitoring of savanna vegetation dynamics and contribute to improved management and conservation of these valuable ecosystems.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: ISPRS Journal of Photogrammetry and Remote Sensing - Volume 131, September 2017, Pages 77-91
Journal: ISPRS Journal of Photogrammetry and Remote Sensing - Volume 131, September 2017, Pages 77-91
نویسندگان
Miriam Tsalyuk, Maggi Kelly, Wayne M. Getz,