scholarly journals Spatial Downscaling of TRMM Precipitation Using Geostatistics and Fine Scale Environmental Variables

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
No-Wook Park
Keyword(s):  
Environmental Variables ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Tropical Rainfall Measuring Mission ◽  
Predictive Performance ◽  
Environmental Modeling ◽  
Fine Scale ◽  
Coarse Scale ◽  
Uncertainty Measures ◽  
Trmm Precipitation

A geostatistical downscaling scheme is presented and can generate fine scale precipitation information from coarse scale Tropical Rainfall Measuring Mission (TRMM) data by incorporating auxiliary fine scale environmental variables. Within the geostatistical framework, the TRMM precipitation data are first decomposed into trend and residual components. Quantitative relationships between coarse scale TRMM data and environmental variables are then estimated via regression analysis and used to derive trend components at a fine scale. Next, the residual components, which are the differences between the trend components and the original TRMM data, are then downscaled at a target fine scale via area-to-point kriging. The trend and residual components are finally added to generate fine scale precipitation estimates. Stochastic simulation is also applied to the residual components in order to generate multiple alternative realizations and to compute uncertainty measures. From an experiment using a digital elevation model (DEM) and normalized difference vegetation index (NDVI), the geostatistical downscaling scheme generated the downscaling results that reflected detailed characteristics with better predictive performance, when compared with downscaling without the environmental variables. Multiple realizations and uncertainty measures from simulation also provided useful information for interpretations and further environmental modeling.

scholarly journals Spatial and Temporal Downscaling of TRMM Precipitation with Novel Algorithms

2020 ◽  
Vol 21 (6) ◽  
pp. 1259-1278 ◽  
Author(s):  
Huihui Zhang ◽  
Hugo A. Loáiciga ◽  
Da Ha ◽  
Qingyun Du
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Principal Component ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Low Resolution ◽  
Spatial And Temporal Scales ◽  
Geographically Weighted Regression Model ◽  
Trmm Precipitation

AbstractTropical Rainfall Measuring Mission (TRMM) satellite products constitute valuable precipitation datasets over regions with sparse rain gauge networks. Downscaling is an effective approach to estimating the precipitation over ungauged areas with high spatial resolution. However, a large bias and low resolution of original TRMM satellite images constitute constraints for practical hydrologic applications of TRMM precipitation products. This study contributes two precipitation downscaling algorithms by exploring the nonstationarity relations between precipitation and various environment factors [daytime surface temperature (LTD), terrain slope, normalized difference vegetation index (NDVI), altitude, longitude, and latitude] to overcome bias and low-resolution constraints of TRMM precipitation. Downscaling of precipitation is achieved with the geographically weighted regression model (GWR) and the backward-propagation artificial neural networks (BP_ANN). The probability density function (PDF) algorithm corrects the bias of satellite precipitation data with respect to spatial and temporal scales prior to downscaling. The principal component analysis algorithm (PCA) provides an alternative method of obtaining accurate monthly rainfall estimates during the wet rainfall season that minimizes the temporal uncertainties and upscaling effects introduced by direct accumulation (DA) of precipitation. The performances of the proposed downscaling algorithms are assessed by downscaling the latest version of TRMM3B42 V7 datasets within Hubei Province from 0.25° (about 25 km) to 1-km spatial resolution at the monthly scale. The downscaled datasets are systematically evaluated with in situ observations at 27 rain gauges from the years 2005 through 2010. This paper’s results demonstrate the bias correction is necessary before downscaling. The high-resolution precipitation datasets obtained with the proposed downscaling model with GWR relying on the NDVI and slope are shown to improve the accuracy of precipitation estimates. GWR exhibits more accurate downscaling results than BP_ANN coupled with the genetic algorithm (GA) in most dry and wet seasons.

scholarly journals MONITORAMENTO DA SECA 2011/2012 NO NORDESTE BRASILEIRO A PARTIR DO SATÉLITE SPOT-VEGETATION E TRMM - DOI: 10.13083/1414-3984.v22n03a03

2014 ◽  
Vol 22 (3) ◽  
pp. 211-221
Author(s):  
Janice Freitas Leivas ◽  
Ricardo Guimarães Andrade ◽  
Daniel De Castro Victoria ◽  
Fabio Enrique Torresan ◽  
Edson Luis Bolfe
Keyword(s):  
El Niño ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
El Nino ◽  
Tropical Rainfall Measuring Mission ◽  
Tropical Rainfall

A seca afeta várias partes do mundo e provoca impactos sociais, econômicos e ambientais. O objetivo deste estudo foi avaliar o comportamento do Índice de Vegetação Padronizado (IVP), obtido a partir do produto NDVI (Normalized Difference Vegetation Index) decendial do satélite SPOT-Vegetation, para o monitoramento da seca no nordeste brasileiro, a partir da série histórica de 1998 a 2012. Para subsidiar os resultados foi realizada a padronização dos dados de precipitação obtidos do satélite TRMM (Tropical Rainfall Measuring Mission), de março de 2011 a março de 2012. A partir de dezembro de 2011, observa-se que a precipitação ficou abaixo do normal na maior parte do nordeste brasileiro, acarretando diminuição do IVP em toda a região estudada. Fatores como o posicionamento da Zona de Convergência Intertropical e El Niño influenciaram no regime de chuvas da região. Os resultados são satisfatórios, indicando a ocorrência de intensa seca no nordeste brasileiro, sendo observada variabilidade do IVP e defasagem na resposta da vegetação à precipitação estimada a partir do TRMM. As análises comprovam que o IVP mostrou-se eficaz no monitoramento das secas na região nordeste do Brasil.

scholarly journals Mapping Regional Ecosystem Functional Types Based on Sentinel-2 Satellite Imagery

2020 ◽  
Vol 194 ◽  
pp. 05047
Author(s):  
Rong Liu ◽  
Fang Huang ◽  
Yue Ren
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Terrestrial Ecosystems ◽  
Ecosystem Functions ◽  
Series Data ◽  
Fine Scale ◽  
Functional Types ◽  
Sentinel 2

Ecosystem functional types (EFTs) are the patches of land surface showing similar in carbon dynamics. EFTs are not defined by the structure and composition of vegetation and represent the spatial heterogeneity of ecosystem functions. Identifying EFTs based on low-resolution satellite remote sensing data cannot satisfy the needs of fine-scale characterization of regional ecosystem functional patterns. Here, taking Zhenlai County, Northeast China as an example, the heterogeneity in ecosystem functions was characterized by identifying EFTs from Sentinel-2 time series data using ISODATA algorithm. Ecosystem functional attributes derived from dynamics of the normalized difference vegetation index (NDVI), the fraction of absorbed photosynthetically active radiation (FAPAR), and canopy water content (CWC) in the growing season were calculated. The correspondence analysis (CA) method was used to reveal relationships between the EFTs and land cover types. Our results showed that the nine selected remotely sensed variables indicating carbon and water flux of the regional ecosystems could be adopted in ecosystem functions classification. The obtained EFTs based on Sentinel-2 images reflected the internal structure of carbon balance well and the distribution pattern of ecosystem functional diversity a fine scale. This study helps to understand the functional heterogeneity pattern of temperate terrestrial ecosystems.

scholarly journals Geographical Distribution of Zebu Breeds in Brazil And Their Relationship With Environmental Variables and Human Development Indicator

2021 ◽  
Author(s):  
Paulo Ricardo Martins Lima ◽  
Vanessa Peripolli ◽  
Luiz Antônio Josahkian ◽  
Concepta McManus
Keyword(s):  
Human Development ◽  
Geographical Distribution ◽  
Environmental Variables ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Zebu Cattle ◽  
Forest Protection ◽  
Dual Purpose ◽  
Family Agriculture ◽  
Development Indicator

Abstract The aim of this study was to evaluate the geographical distribution of zebu breeds in Brazil and correlate their occurrence with environmental variables and human development indicator. The herds of purebred zebu cattle in Brazil were classified as beef breeds (Brahman, Polled Brahman, Nelore, Polled Nelore and Tabapuã), dairy breeds (Gir and Polled Gir), and dual-purpose breeds (Guzerá, Indubrasil, Polled Indubrasil, Sindhi and Polled Sindhi), all breeds being spatialized in ArcGIS program. Variables examined included environmental and human development indicator. The statistical analysis included analysis and logistic regression.The lower distribution of zebu cattle in the states of Northeast compared to other locations is probably due to its extreme climate, highly susceptible to long periods of high temperatures and lower precipitation, which directly affects local livestock. The beef breeds were evenly spread throughout the country. The location occupied for beef breeds was influenced by environmental variables, showing a higher incidence with increased precipitation, normalized difference vegetation index (NDVI), temperature, relative humidity and temperature humidity index (THI), as well as establishments without family agriculture and rivers and streams with forest protection. The location used for dual-purpose and dairy breeds was influenced by areas with cultivated cutting forages, areas with integrated crop-livestock forest systems and areas with rotational grazing system, indicating a higher occupation in fertile lands. The Gir breed, the only one with dairy exploration in this study, showed herds in establishments with family agriculture, characterized by small to medium farms, and in regions with higher altitude.

scholarly journals Variations of NDVI and Its Association with Rainfall and Evapotranspiration over Bangladesh

2015 ◽  
Vol 43 ◽  
pp. 21-28 ◽  
Author(s):  
Md. Monirul Islam ◽  
Md. Mainul Islam Mamun
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Temporal Correlation ◽  
Periodic Variation ◽  
Important Variable ◽  
Northwestern Part ◽  
Different Seasons ◽  
Moderate Resolution Imaging Spectroradiometer

This paper deals with the variability of Normalized Difference Vegetation Index (NDVI) and its association with rain rate and total evapotranspiration over Bangladesh during the period of 2003-2011 using MODerate-resolution Imaging Spectroradiometer (MODIS), Tropical Rainfall Measuring Mission (TRMM) and Global Land Assimilation System (GLDAS) data. NDVI shows higher concentration in eastern parts of the country. The maximum NDVI is found in the month of October and minimum in February. It reveals excellent periodic variation in relation to rain rate and total evapotranspiration. NDVI shows strong spatial and temporal correlation with rain rate and total evapotranspiration especially in northwestern part of the country. Total evapotranspiration is more strongly correlated with vegetation than rain rate as it integrates rainfall, temperature and soil water statistics during the entire period. Thus, NDVI is an important variable for agronomical and climate applications. Also, it is important to study the vegetation for different seasons and different agro-ecological areas to investigate the variables affecting the vegetation types and growth rate.

scholarly journals The Midsummer Dry Spell’s Impact on Vegetation in Jamaica

2010 ◽  
Vol 49 (7) ◽  
pp. 1590-1595 ◽  
Author(s):  
Theodore L. Allen ◽  
Scott Curtis ◽  
Douglas W. Gamble
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Tropical Rainfall Measuring Mission ◽  
Early Summer ◽  
Annual Rainfall ◽  
Dry Spell ◽  
Moderate Resolution ◽  
Rainfall Reduction ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Impact

Abstract The annual rainfall pattern of the intra-Americas sea reveals a bimodal feature with a minimum during the midsummer known as the midsummer dry spell (MSD). A first attempt is made to examine the impact of the MSD on vegetation through a normalized difference vegetation index (NDVI) analysis in Jamaica. Tropical Rainfall Measuring Mission rainfall estimates and NDVI derived from the Terra Moderate Resolution Imaging Spectroradiometer highlight a consistent MSD feature in both rainfall and vegetative vigor. Spatial variation of this MSD NDVI response is evident throughout Jamaica, with the strongest relationship between the rainfall reduction and NDVI decline throughout the southern portions of Jamaica including the area of major domestic food production. In all years except 2005 there is a notable reduction from early-summer NDVI to midsummer NDVI in this agricultural region. However, the lagged vegetative response undergoes clear interannual variation and is affected by other forcings besides rainfall, such as brush fires and extreme wind.

scholarly journals Random forest variable selection in spatial malaria transmission modelling in Mpumalanga Province, South Africa

2016 ◽  
Vol 11 (3) ◽  
Author(s):  
Thandi Kapwata ◽  
Michael T. Gebreslasie
Keyword(s):  
South Africa ◽  
Random Forest ◽  
Malaria Transmission ◽  
Environmental Variables ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Mean Squared Error ◽  
Predictor Variable ◽  
Variable Importance ◽  
Mpumalanga Province

Malaria is an environmentally driven disease. In order to quantify the spatial variability of malaria transmission, it is imperative to understand the interactions between environmental variables and malaria epidemiology at a micro-geographic level using a novel statistical approach. The random forest (RF) statistical learning method, a relatively new variable-importance ranking method, measures the variable importance of potentially influential parameters through the percent increase of the mean squared error. As this value increases, so does the relative importance of the associated variable. The principal aim of this study was to create predictive malaria maps generated using the selected variables based on the RF algorithm in the Ehlanzeni District of Mpumalanga Province, South Africa. From the seven environmental variables used [temperature, lag temperature, rainfall, lag rainfall, humidity, altitude, and the normalized difference vegetation index (NDVI)], altitude was identified as the most influential predictor variable due its high selection frequency. It was selected as the top predictor for 4 out of 12 months of the year, followed by NDVI, temperature and lag rainfall, which were each selected twice. The combination of climatic variables that produced the highest prediction accuracy was altitude, NDVI, and temperature. This suggests that these three variables have high predictive capabilities in relation to malaria transmission. Furthermore, it is anticipated that the predictive maps generated from predictions made by the RF algorithm could be used to monitor the progression of malaria and assist in intervention and prevention efforts with respect to malaria.

Nesting sites in agricultural landscapes may reduce the reproductive success of populations of Blanding’s Turtles (Emydoidea blandingii)

2016 ◽  
Vol 94 (1) ◽  
pp. 61-67 ◽  
Author(s):  
A.B. Mui ◽  
C.B. Edge ◽  
J.E. Paterson ◽  
B. Caverhill ◽  
B. Johnson ◽  
...  
Keyword(s):  
Site Selection ◽  
Vegetation Cover ◽  
Environmental Variables ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Landscape ◽  
Agricultural Landscapes ◽  
Turtle Species ◽  
Emydoidea Blandingii ◽  
Parallel Change

Almost all turtle species nest in terrestrial environments and maternal site selection represents a critical component of nest success. Females use cues in the current environment to predict the future conditions for embryo development. However, in disturbed landscapes, current and future conditions may not be correlated. We compared selection of nest sites by Blanding’s Turtles (Emydoidea blandingii (Holbrook, 1838)) in a (relatively undisturbed) park and a (heavily disturbed) agricultural landscape in Ontario, Canada, using field measurements and satellite imagery. Environmental variables were compared using logistic regression and Akaike’s information criterion (AIC) based on data measured at nest (presence) and random (pseudoabsence) locations. Specific environmental variables associated with site selection differed between study areas. Most notably, NDVI (normalized difference vegetation index, a proxy for vegetation cover) increased significantly during the year at the agricultural locale, corresponding with the growth of planted fields. No parallel change was observed at the park locale where canopy cover remained more consistent. An increase in vegetation cover may alter nest temperatures and soil moisture. Combined with the unpredictability in timing of crop sowing and harvesting, findings suggest that nests in agricultural fields may act as ecological sinks and that other species nesting in similarly altered habitats may be subjected to the same threats.

scholarly journals The Thar, Rajputana desert unprecedented rainfall in 2006 and 2010: effect of climate change?

2011 ◽  
Vol 50 (4) ◽  
Author(s):  
Vadlamudi Brahmananda Rao ◽  
Egidio Arai ◽  
Sergio H. Franchito ◽  
Yosio E. Shimabukuro ◽  
S.S.V.S. Ramakrishna ◽  
...  
Keyword(s):  
Climate Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Tropical Rainfall Measuring Mission ◽  
Enhanced Vegetation Index ◽  
Tropical Rainfall ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

En agosto de 2006 la región de Rajasthan registró lluvias excepcionalmente intensas que provocaron severas inundaciones. También, en la temporada del monzón de 2010 Rajasthan recibió fuertes lluvias, aunque no superiores a las de agosto de 2006. Pero los altos valores de precipitación en el año 2010 se produjeron durante toda la temporada del monzón. En el 2006 varias estaciones registraron lluvias intensas de alrededor de 125 mm en 24 horas. Un estudio reciente mostró que, en el futuro, eventos extremos similares tenderán a ocurrir en la India Central, que incluye una parte de Rajasthan. En este trabajo estos eventos son estudiados en el contexto de un probable cambio climatico sobre esta región usando datos de satélites. Para ello fueron usados los índices de vegetación NDVI (Normalized Difference Vegetation Index) y EVI (Enhanced Vegetation Index), derivados del MODIS (Moderate Resolution Imaging Spectroradiometer) y los datos de precipitación del satélite TRMM (Tropical Rainfall Measuring Mission) para 11 años (2000-2010). Ambos productos NDVI y EVI revelaron un crecimiento exuberante de la vegetación sobre el desierto de Rajputana en setiembre de 2006 y en agosto y septiembre de 2010. El análisis de los datos de precipitación y EVI confirmó el crecimiento de la vegetación en 2006 y 2010, mostrando la utilidad de los datos obtenidos por satélite en la captura de los cambios en esta región. En algunos estudios previos se ha señalado que la lluvia sobre la región Oeste de Rajasthan durante la temporada del monzón muestra una importante tendencia creciente. Así, en el futuro, el crecimiento de la vegetación en el desierto Rajputana parece ser muy posible.

Pemetaan Pulau Kecil Gelasa Kabupaten Bangka Tengah

2019 ◽  
Vol 2 (1) ◽  
pp. 11-14
Author(s):  
Wahyu Adi
Keyword(s):  
Coral Reefs ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Image Interpretation ◽  
Vegetation Coverage ◽  
Small Island ◽  
Shallow Waters ◽  
Small Islands ◽  
Seagrass Beds ◽  
Cover Density

Pulau Kecil Gelasa merupakan daerah yang belum banyak diteliti. Pemetaan ekosistem di pulau kecil dilakukan dengan bantuan citra Advanced Land Observing Satellite (ALOS). Penelitian terdahulu diketahui bahwa ALOS memiliki kemampuan memetakan terumbu karang dan padang lamun di perairan dangkal serta mampu memetakan kerapatan penutupan vegetasi. Metode interpretasi citra menggunakan alogaritma indeks vegetasi pada citra ALOS yaitu NDVI (Normalized Difference Vegetation Index), serta pendekatan Lyzengga untuk mengkoreksi kolom perairan. Hasil penelitian didapatkan luasan Padang Lamun di perairan dangkal 41,99 Ha, luasan Terumbu Karang 125,57 Ha. Hasil NDVI di daratan/ pulau kecil Gelasa untuk Vegetasi Rapat seluas 47,62 Ha; luasan penutupan Vegetasi Sedang 105,86 Ha; dan penutupan Vegetasi Jarang adalah 34,24 Ha.   Small Island Gelasa rarely studied. Mapping ecosystems on small islands with the image of Advanced Land Observing Satellite (ALOS). Previous research has found that ALOS has the ability to map coral reefs and seagrass beds in shallow water, and is able to map vegetation cover density. The method of image interpretation uses the vegetation index algorithm in the ALOS image, NDVI (Normalized Difference Vegetation Index), and the Lyzengga approach to correct the water column. The results of the study were obtained in the area of Seagrass Padang in the shallow waters of 41.99 ha, the area of coral reefs was 125.57 ha. NDVI results on land / small islands Gelasa for dense vegetation of 47.62 ha; area of Medium Vegetation coverage 105.86 Ha; and the coverage of Rare Vegetation is 34.24 Ha.

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