MULTITEMPORAL REMOTELY SENSED INDICES AND A PROPOSED INTEGRATED FUNCTIONAL VEGETATION INDEX (IFVI) TO MONITOR HOLM-OAK WOODS ALONG A SPATIAL GRADIENT

2002 ◽  
Author(s):  
FAUSTO MANES ◽  
MONICA GIANNINI ◽  
SILVIA ANSELMI ◽  
ALESSIA ALLEGRINI ◽  
SUSANNA MELINI
Keyword(s):  
Vegetation Index ◽  
Remotely Sensed ◽  
Holm Oak ◽  
Spatial Gradient

Assessment of changes in the state of the rangelands of Inner Mongolia, China between 1998 and 2007 using remotely sensed data

2012 ◽  
Vol 34 (1) ◽  
pp. 103 ◽  
Author(s):  
Z. M. Hu ◽  
S. G. Li ◽  
J. W. Dong ◽  
J. W. Fan
Keyword(s):  
Inner Mongolia ◽  
Vegetation Index ◽  
Net Primary Productivity ◽  
Temporal Dynamics ◽  
Remotely Sensed ◽  
The State ◽  
Mean Annual Precipitation ◽  
Aboveground Net Primary Productivity ◽  
Remotely Sensed Data ◽  
Normalised Difference Vegetation Index

The spatial annual patterns of aboveground net primary productivity (ANPP) and precipitation-use efficiency (PUE) of the rangelands of the Inner Mongolia Autonomous Region of China, a region in which several projects for ecosystem restoration had been implemented, are described for the years 1998–2007. Remotely sensed normalised difference vegetation index and ANPP data, measured in situ, were integrated to allow the prediction of ANPP and PUE in each 1 km2 of the 12 prefectures of Inner Mongolia. Furthermore, the temporal dynamics of PUE and ANPP residuals, as indicators of ecosystem deterioration and recovery, were investigated for the region and each prefecture. In general, both ANPP and PUE were positively correlated with mean annual precipitation, i.e. ANPP and PUE were higher in wet regions than in arid regions. Both PUE and ANPP residuals indicated that the state of the rangelands of the region were generally improving during the period of 2000–05, but declined by 2007 to that found in 1999. Among the four main grassland-dominated prefectures, the recovery in the state of the grasslands in the Erdos and Chifeng prefectures was highest, and Xilin Gol and Chifeng prefectures was 2 years earlier than Erdos and Hunlu Buir prefectures. The study demonstrated that the use of PUE or ANPP residuals has some limitations and it is proposed that both indices should be used together with relatively long-term datasets in order to maximise the reliability of the assessments.

scholarly journals Temporal Variations and Associated Remotely Sensed Environmental Variables of Dengue Fever in Chitwan District, Nepal

2018 ◽  
Vol 7 (7) ◽  
pp. 275 ◽  
Author(s):  
Bipin Acharya ◽  
Chunxiang Cao ◽  
Min Xu ◽  
Laxman Khanal ◽  
Shahid Naeem ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Dengue Fever ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Environmental Variables ◽  
Vegetation Index ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Remotely Sensed ◽  
Cross Correlation Analysis

Dengue fever is one of the leading public health problems of tropical and subtropical countries across the world. Transmission dynamics of dengue fever is largely affected by meteorological and environmental factors, and its temporal pattern generally peaks in hot-wet periods of the year. Despite this continuously growing problem, the temporal dynamics of dengue fever and associated potential environmental risk factors are not documented in Nepal. The aim of this study was to fill this research gap by utilizing epidemiological and earth observation data in Chitwan district, one of the frequent dengue outbreak areas of Nepal. We used laboratory confirmed monthly dengue cases as a dependent variable and a set of remotely sensed meteorological and environmental variables as explanatory factors to describe their temporal relationship. Descriptive statistics, cross correlation analysis, and the Poisson generalized additive model were used for this purpose. Results revealed that dengue fever is significantly associated with satellite estimated precipitation, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) synchronously and with different lag periods. However, the associations were weak and insignificant with immediate daytime land surface temperature (dLST) and nighttime land surface temperature (nLST), but were significant after 4–5 months. Conclusively, the selected Poisson generalized additive model based on the precipitation, dLST, and NDVI explained the largest variation in monthly distribution of dengue fever with minimum Akaike’s Information Criterion (AIC) and maximum R-squared. The best fit model further significantly improved after including delayed effects in the model. The predicted cases were reasonably accurate based on the comparison of 10-fold cross validation and observed cases. The lagged association found in this study could be useful for the development of remote sensing-based early warning forecasts of dengue fever.

Inter-comparison of remotely-sensed actual evapotranspiration products in the Zayandehrud river basin, Iran

2021 ◽  
Author(s):  
Neda Abbasi ◽  
Hamideh Nouri ◽  
Sattar Chavoshi Borujeni ◽  
Pamela Nagler ◽  
Christian Opp ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Field Measurements ◽  
Water Shortage ◽  
Remotely Sensed ◽  
Irrigation Scheduling ◽  
Accurate Estimation ◽  
Agricultural Areas ◽  
Eta Products

<p>Accurate estimation of evapotranspiration (ET) helps to create a better understanding of water allocation, irrigation scheduling, and crop management especially in arid and semiarid regions where agricultural areas are far more affected by water shortage and drought events. Remote sensing (RS) facilitates estimating the ET in regions where long-term field measurements are missed.  In this study, we compare the performance of free open-access remotely sensed actual ET products at eleven counties of the Zayandehrud basin. The Zayandehrud basin, one of the major watersheds of Iran, suffers from recurrent droughts and long-term impacts of aridity. The RS products used in this study are namely WaPOR (2009-2019), MOD16A2 (2003-2019), SSEBOp (2003-2019). We also merged the two products of SSEBOp and WaPOR and assessed its performance. To prepare the Merged ETa Product (MEP), WaPOR was resampled to the spatial resolution of SSEBOp. Then, the average pixel values of the resampled ETa product and SSEBOp were calculated. To compare ETa estimations over croplands in each county, maximum Normalized Difference Vegetation Index (NDVI) maps at annual scale (2003-2019) were prepared using LANDSAT 5, 7, and 8 images. Annual mean ETa estimations were then extracted over croplands by using annual maximum NDVI layers. We compared the RS-based ETa with reported long-term ETa values extracted from the local available literature. Our results showed a consistent underestimation of MOD16A2 in all counties. The MEP and WaPOR outperformed other products in the estimation of ETa in seven. Estimations of WaPOR and SSEBOp agreed in most of the counties. Our analysis displayed that, although MOD16A2 underestimated ETa values, it could together with SSEBOp capture the drought better than that of WaPOR and MEP in the lower reaches of the basin. Further study is needed to evaluate the monthly and seasonal performance of RS-based ETa products.</p>

scholarly journals Model-Assisted Bird Monitoring Based on Remotely Sensed Ecosystem Functioning and Atlas Data

2020 ◽  
Vol 12 (16) ◽  
pp. 2549 ◽  
Author(s):  
Adrián Regos ◽  
Pablo Gómez-Rodríguez ◽  
Salvador Arenas-Castro ◽  
Luis Tapia ◽  
María Vidal ◽  
...  
Keyword(s):  
Life History ◽  
Energy Balance ◽  
Species Distribution ◽  
Ecosystem Functioning ◽  
Life History Traits ◽  
Vegetation Index ◽  
Bird Species ◽  
Remotely Sensed ◽  
Distribution Models ◽  
Atlas Data

Urgent action needs to be taken to halt global biodiversity crisis. To be effective in the implementation of such action, managers and policy-makers need updated information on the status and trends of biodiversity. Here, we test the ability of remotely sensed ecosystem functioning attributes (EFAs) to predict the distribution of 73 bird species with different life-history traits. We run ensemble species distribution models (SDMs) trained with bird atlas data and 12 EFAs describing different dimensions of carbon cycle and surface energy balance. Our ensemble SDMs—exclusively based on EFAs—hold a high predictive capacity across 71 target species (up to 0.94 and 0.79 of Area Under the ROC curve and true skill statistic (TSS)). Our results showed the life-history traits did not significantly affect SDM performance. Overall, minimum Enhanced Vegetation Index (EVI) and maximum Albedo values (descriptors of primary productivity and energy balance) were the most important predictors across our bird community. Our approach leverages the existing atlas data and provides an alternative method to monitor inter-annual bird habitat dynamics from space in the absence of long-term biodiversity monitoring schemes. This study illustrates the great potential that satellite remote sensing can contribute to the Aichi Biodiversity Targets and to the Essential Biodiversity Variables framework (EBV class “Species distribution”).

scholarly journals Modeling Mid-Season Rice Nitrogen Uptake Using Multispectral Satellite Data

2019 ◽  
Vol 11 (15) ◽  
pp. 1837 ◽  
Author(s):  
James Brinkhoff ◽  
Brian W. Dunn ◽  
Andrew J. Robson ◽  
Tina S. Dunn ◽  
Remy L. Dehaan
Keyword(s):  
Vegetation Index ◽  
Field Experiments ◽  
N Uptake ◽  
Satellite Image ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Data Set ◽  
Multispectral Data ◽  
Input Variables ◽  
Better Than

Mid-season nitrogen (N) application in rice crops can maximize yield and profitability. This requires accurate and efficient methods of determining rice N uptake in order to prescribe optimal N amounts for topdressing. This study aims to determine the accuracy of using remotely sensed multispectral data from satellites to predict N uptake of rice at the panicle initiation (PI) growth stage, with a view to providing optimum variable-rate N topdressing prescriptions without needing physical sampling. Field experiments over 4 years, 4–6 N rates, 4 varieties and 2 sites were conducted, with at least 3 replicates of each plot. One WorldView satellite image for each year was acquired, close to the date of PI. Numerous single- and multi-variable models were investigated. Among single-variable models, the square of the NDRE vegetation index was shown to be a good predictor of N uptake (R 2 = 0.75, RMSE = 22.8 kg/ha for data pooled from all years and experiments). For multi-variable models, Lasso regularization was used to ensure an interpretable and compact model was chosen and to avoid over fitting. Combinations of remotely sensed reflectances and spectral indexes as well as variety, climate and management data as input variables for model training achieved R 2 < 0.9 and RMSE < 15 kg/ha for the pooled data set. The ability of remotely sensed data to predict N uptake in new seasons where no physical sample data has yet been obtained was tested. A methodology to extract models that generalize well to new seasons was developed, avoiding model overfitting. Lasso regularization selected four or less input variables, and yielded R 2 of better than 0.67 and RMSE better than 27.4 kg/ha over four test seasons that weren’t used to train the models.

Exploring the possibilities of a vegetation index (GESAVI) from remotely sensed data

2001 ◽  
Author(s):  
Beatriz Martinez ◽  
F. Camacho-de Coca ◽  
Joan Garcia-Haro ◽  
M. A. Gilabert
Keyword(s):  
Vegetation Index ◽  
Remotely Sensed ◽  
Remotely Sensed Data
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