scholarly journals Performance of the Enhanced Vegetation Index to Detect Inner-annual Dry Season and Drought Impacts on Amazon Forest Canopies

2015 ◽  
Vol XL-7/W3 ◽  
pp. 337-344 ◽  
Author(s):  
B. Brede ◽  
J. Verbesselt ◽  
L. Dutrieux ◽  
M. Herold
Keyword(s):  
Large Scale ◽  
Vegetation Index ◽  
Standardized Precipitation Index ◽  
Dry Season ◽  
Drought Indices ◽  
Amazon Forest ◽  
Enhanced Vegetation Index ◽  
Sun Sensor ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Sensor Geometry

The Amazon rainforests represent the largest connected forested area in the tropics and play an integral role in the global carbon cycle. In the last years the discussion about their phenology and response to drought has intensified. A recent study argued that seasonality in greenness expressed as Enhanced Vegetation Index (EVI) is an artifact of variations in sun-sensor geometry throughout the year. We aimed to reproduce these results with the Moderate-Resolution Imaging Spectroradiometer (MODIS) MCD43 product suite, which allows modeling the Bidirectional Reflectance Distribution Function (BRDF) and keeping sun-sensor geometry constant. The derived BRDF-adjusted EVI was spatially aggregated over large areas of central Amazon forests. The resulting time series of EVI spanning the 2000-2013 period contained distinct seasonal patterns with peak values at the onset of the dry season, but also followed the same pattern of sun geometry expressed as Solar Zenith Angle (SZA). Additionally, we assessed EVI’s sensitivity to precipitation anomalies. For that we compared BRDF-adjusted EVI dry season anomalies to two drought indices (Maximum Cumulative Water Deficit, Standardized Precipitation Index). This analysis covered the whole of Amazonia and data from the years 2000 to 2013. The results showed no meaningful connection between EVI anomalies and drought. This is in contrast to other studies that investigate the drought impact on EVI and forest photosynthetic capacity. The results from both sub-analyses question the predictive power of EVI for large scale assessments of forest ecosystem functioning in Amazonia. Based on the presented results, we recommend a careful evaluation of the EVI for applications in tropical forests, including rigorous validation supported by ground plots.

scholarly journals TROPOMI reveals dry-season increase of solar-induced chlorophyll fluorescence in the Amazon forest

2019 ◽  
Vol 116 (44) ◽  
pp. 22393-22398 ◽  
Author(s):  
Russell Doughty ◽  
Philipp Köhler ◽  
Christian Frankenberg ◽  
Troy S. Magney ◽  
Xiangming Xiao ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Forest Canopy ◽  
Dry Season ◽  
Amazon Forest ◽  
Wet Season ◽  
Carbon Cycles ◽  
Sun Sensor ◽  
Sensor Geometry ◽  
Earth Observation Satellites

Photosynthesis of the Amazon rainforest plays an important role in the regional and global carbon cycles, but, despite considerable in situ and space-based observations, it has been intensely debated whether there is a dry-season increase in greenness and photosynthesis of the moist tropical Amazonian forests. Solar-induced chlorophyll fluorescence (SIF), which is emitted by chlorophyll, has a strong positive linear relationship with photosynthesis at the canopy scale. Recent advancements have allowed us to observe SIF globally with Earth observation satellites. Here we show that forest SIF did not decrease in the early dry season and increased substantially in the late dry season and early part of wet season, using SIF data from the Tropospheric Monitoring Instrument (TROPOMI), which has unprecedented spatial resolution and near-daily global coverage. Using in situ CO2 eddy flux data, we also show that cloud cover rarely affects photosynthesis at TROPOMI’s midday overpass, a time when the forest canopy is most often light-saturated. The observed dry-season increases of forest SIF are not strongly affected by sun-sensor geometry, which was attributed as creating a pseudo dry-season green-up in the surface reflectance data. Our results provide strong evidence that greenness, SIF, and photosynthesis of the tropical Amazonian forest increase during the dry season.

DROUGHT RISK ASSESSMENT DURING THE DRY SEASON IN TIEN RIVER ESTUARY

2020 ◽  
Vol 65 (6) ◽  
pp. 191-200
Author(s):  
Hung Dao Ngoc ◽  
Luan Nguyen Thanh
Keyword(s):  
Large Scale ◽  
Vegetation Index ◽  
Satellite Image ◽  
Standardized Precipitation Index ◽  
River Estuary ◽  
Meteorological Drought ◽  
Dry Season ◽  
Drought Risk ◽  
Widespread Application ◽  
Landsat Satellite

Drought simply is a period of moisture deficiency. It depends on temperature, evaporation capacity, vegetation cover, topography, etc., in addition, it often happens on a large scale making it difficult to use traditional research methods. With the development and widespread application of remote sensing technology and geographic information systems (GIS), the use of satellite images as well as GIS software is becoming more and more effective in monitoring, monitoring and assessing drought. In this study, the author assessed the risk of drought at Tien river estuary through two indices: Normalize Difference Vegetation Index (NDVI) and Standardized Precipitation Index (SPI) during the dry season months of 1991, 2001, 2010 and 2018. SPI values are interpolated to construct spatial modeling of meteorological drought levels. Through the LANDSAT satellite image, NDVI is calculated and built on a map of drought levels. Weighted overlay SPI and NDVI map layers for a drought risk map. Research results have shown that the Tien river estuary area is divided into 2 zones: light drought and moderate drought occurs in the dry season. Drought occurred with strong intensity in the eastern coastal area of Ben Tre and Tra Vinh provinces, the deeper the inland the level of drought decreased.

A novel index for ecological drought monitoring based on ecological water deficit: a case study of Northwestern China

2021 ◽  
Author(s):  
Tianliang Jiang ◽  
Xiaoling Su
Keyword(s):  
Water Deficit ◽  
Time Scales ◽  
Drought Index ◽  
Vegetation Index ◽  
Standardized Precipitation Index ◽  
Northwestern China ◽  
Drought Monitoring ◽  
Drought Indices ◽  
Drought Severity ◽  
Different Time Scales

<p>Although the concept of ecological drought was first defined by the Science for Nature and People Partnership (SNAPP) in 2016, there remains no widely accepted drought index for monitoring ecological drought. Therefore, this study constructed a new ecological drought monitoring index, the standardized ecological water deficit index (SEWDI). The SEWDI is based on the difference between ecological water requirements and consumption, referred to as the standardized precipitation index (SPI) method, which was used to monitor ecological drought in Northwestern China (NWRC). The performances of the SEWDI and four widely-used drought indices [standardized root soil moisture index (SSI), self-calibrated Palmer drought index (scPDSI), standardized precipitation-evaporation drought index (SPEI), and SPI) in monitoring ecological drought were evaluated through comparing the Pearson correlations between these indices and the standardized normalized difference vegetation index (SNDVI) under different time scales, wetness, and water use efficiencies (WUEs) of vegetation. Finally, the rotational empirical orthogonal function (REOF) was used to decompose the SEWDI at a 12-month scale in the NWRC during 1982–2015 to obtain five ecological drought regions. The characteristics of ecological drought in the NWRC, including intensity, duration, and frequency, were extracted using run theory. The results showed that the performance of the SEWDI in monitoring ecological drought was highest among the commonly-used drought indices evaluated under different time scales [average correlation coefficient values (r) between SNDVI and drought indices: SEWDI<sub></sub>= 0.34, SSI<sub></sub>= 0.24, scPDSI<sub></sub>= 0.23, SPI<sub></sub>= 0.20, SPEI<sub></sub>= 0.18), and the 12-month-scale SEWDI was largely unaffected by wetness and WUE. In addition, the results of the monitoring indicated that serious ecological droughts in the NWRC mainly occurred in 1982–1986, 1990–1996, and 2005–2010, primarily in regions I, II, and V, regions II, and IV, and in region III, IV, and V, respectively. This study provides a robust approach for quantifying ecological drought severity across natural vegetation areas and scientific evidence for governmental decision makers.</p>

scholarly journals Assessment of paddy fields’ damage caused by Cyclone Nargis using MODIS time-series images (2004–2013)

2020 ◽  
Author(s):  
Keisuke Omori ◽  
Toru Sakai ◽  
Jun Miyamoto ◽  
Akihiko Itou ◽  
Aung Naing Oo ◽  
...  
Keyword(s):  
Large Scale ◽  
Saline Water ◽  
Dry Season ◽  
Paddy Fields ◽  
Natural Ecosystems ◽  
Personal Property ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cyclone Nargis ◽  
Time Series Images

Abstract The Ayeyarwady Delta in the Bay of Bengal, the rice bowl of Myanmar, depends on natural conditions, especially rainfall. During the dry season, the delta’s coastal zone experiences saline water intrusion due to its low-lying topography. On May 2, 2008, Cyclone Nargis made landfall and crossed Ayeyarwady Region and Yangon City, affecting more than 50 townships and causing massive destruction of personal property and natural ecosystems. There is no doubt that Nargis caused an unprecedented large-scale disaster, but there is no objective method to quantify crop yield and salinity damage in the delta post-Nargis. The purpose of this study, therefore, is to clarify the changes in vegetation in paddy fields in the Ayeyarwady Delta using Moderate Resolution Imaging Spectroradiometer data pre- and post-Nargis and determine whether this method can be applied to measure crop and salinity damage. The study used daily composite data at a 250-m resolution (MOD09GQ, collection 6) from 2004 to 2013 and calculated NDVI and salinity indices smoothed by locally weighted regression (Lowess). Based on the results of our studies, NDVI peak value in 2008 was lower by 19% compared to 2007 data, and that the NDVI peak values declined for three straight years since May 2008 when Nargis struck. However, salinity damage evaluation pre- and post-Nargis (using the salinity index equation) showed that soil electrical conductivity did not tend to move up in the post-Nargis dry season (2009), indicating that the decrease in NDVI values was not due to salinity damage.

scholarly journals Implicaciones del filtrado de calidad del índice de vegetación EVI para el seguimiento funcional de ecosistemas

2015 ◽  
pp. 11 ◽  
Author(s):  
A. Reyes Díez ◽  
D. Alcaraz-Segura ◽  
J. Cabello Piñar
Keyword(s):  
Quality Assessment ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Enhanced Vegetation Index ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

<p>El seguimiento de los ecosistemas con imágenes procedentes del sensor MODIS (<em>Moderate Resolution Imaging Spectroradiometer</em>, espectroradiómetro de imágenes de resolución media) está actualmente muy extendido tanto en tareas de investigación como de gestión. Los índices de vegetación NDVI (<em>Normalized Difference Vegetation Index, </em>índice de vegetación de la diferencia normalizada) y EVI (<em>Enhanced Vegetation Index, </em>índice de vegetación mejorado) son ampliamente usados para la caracterización del funcionamiento ecosistémico. Ambos índices se emplean como estimadores lineales de la fracción de radiación fotosintéticamente activa interceptada por la vegetación (fAPAR), el principal control de la producción primaria. A pesar de sus ventajas, las imágenes de índices de vegetación no están libres de errores. El producto índices de vegetación MOD13Q1 proporciona una capa QA (<em>Quality assessment</em>,evaluación de la calidad) que informa sobre la calidad asociada a cada píxel. Esta información representa una gran ventaja para el usuario, al permitir filtrar aquellos datos que puedan inducir a errores al verse alterados por la presencia de aerosoles, nubes, nieve o sombras. Sin embargo, la realización de un filtrado homogéneo a lo largo de una gran región puede ocasionar la pérdida sistemática de información en determinadas zonas o épocas del año, introduciendo así un sesgo espacial o en la serie temporal. Esta situación puede ser especialmente crítica en regiones con alta heterogeneidad ambiental, como el Sureste Ibérico. En este trabajo evaluamos el efecto que el filtrado de calidad tiene sobre la información espacial y temporal de la base de datos del EVI en el periodo 2001-2010. Los esultados, expresados en porcentaje de información perdida (filtrada) y como efecto de estas pérdidas sobre los valores del EVI, indican que mientras que las áreas de menor altitud no se ven afectadas por el filtrado, las regiones de alta montaña muestran variaciones significativas en sus valores del EVI cuando son filtrados por aerosoles, sombras o la presencia de hielo o nieve. Esto pone de manifiesto la importancia del establecimiento de un protocolo para el procesamiento de la información que considere las características espaciales y temporales de los datos a filtrar.</p>

scholarly journals Mapping Maize Cultivated Area Combining MODIS EVI Time Series and the Spatial Variations of Phenology over Huanghuaihai Plain

2020 ◽  
Vol 10 (8) ◽  
pp. 2667 ◽  
Author(s):  
Xueting Wang ◽  
Sha Zhang ◽  
Lili Feng ◽  
Jiahua Zhang ◽  
Fan Deng
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Regression Equations ◽  
Summer Maize ◽  
Temporal Shift ◽  
Modis Evi ◽  
Multi Temporal ◽  
Moderate Resolution Imaging Spectroradiometer

Crop phenology is a significant factor that affects the precision of crop area extraction by using the multi-temporal vegetation indices (VIs) approach. Considering the phenological differences of maize among the different regions, the summer maize cultivated area was estimated by using enhanced vegetation index (EVI) time series images from the Moderate Resolution Imaging Spectroradiometer (MODIS) over the Huanghuaihai Plain in China. By analyzing the temporal shift in summer maize calendars, linear regression equations for simulating the summer maize phenology were obtained. The simulated maize phenology was used to correct the MODIS EVI time series curve of summer maize. Combining the mean absolute distance (MAD) and p-tile algorithm, the cultivated areas of summer maize were distinguished over the Hunaghuaihai Plain. The accuracy of the extraction results in each province was above 85%. Comparing the maize area of two groups from MODIS-estimated and statistical data, the validation results showed that the R2 reached 0.81 at the city level and 0.69 at the county level. It demonstrated that the approach in this study has the ability to effectively map the summer maize area over a large scale and provides a novel idea for estimating the planting area of other crops.

Fire danger estimation from MODIS Enhanced Vegetation Index data: application to Galicia region (north-west Spain)

2011 ◽  
Vol 20 (3) ◽  
pp. 465 ◽  
Author(s):  
M. M. Bisquert ◽  
J. M. Sánchez ◽  
V. Caselles
Keyword(s):  
Forest Fires ◽  
Vegetation Index ◽  
Estimation Error ◽  
Fire Danger ◽  
Enhanced Vegetation Index ◽  
North West ◽  
Remote Sensing Technique ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Terra Satellite

Galicia, in north-west Spain, is a region especially affected by devastating forest fires. The development of a fire danger prediction model adapted to this particular region is required. In this paper, we focus on changes in the condition of vegetation as an indicator of fire danger. The potential of the Enhanced Vegetation Index (EVI) together with period-of-year to monitor vegetation changes in Galicia is shown. The Moderate Resolution Imaging Spectroradiometer (MODIS), onboard the Terra satellite, was chosen for this study. A 6-year dataset of EVI images, from the product MOD13Q1 (16-day composites), together with fire data in a 10 × 10-km grid basis, were used. Logistic regression was used to assess the relationship between the percentage of fire activity and EVI variations together with period-of-year. The results show the ability of the model obtained to discriminate different levels of fire occurrence danger, with an estimation error of ~5%. This remote sensing technique may contribute to improving the efficiency of the currently used fire prevention systems.

scholarly journals Estudo da dinâmica espaço-temporal do bioma Pantanal por meio de imagens MODIS

2008 ◽  
Vol 43 (10) ◽  
pp. 1371-1378 ◽  
Author(s):  
Marcos Adami ◽  
Ramon Morais de Freitas ◽  
Carlos Roberto Padovani ◽  
Yosio Edemir Shimabukuro ◽  
Mauricio Alves Moreira
Keyword(s):  
Vegetation Index ◽  
Tropical Rainfall Measuring Mission ◽  
Shuttle Radar Topography Mission ◽  
Enhanced Vegetation Index ◽  
Tropical Rainfall ◽  
Modis Evi ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

O objetivo deste trabalho foi avaliar dados multitemporais, obtidos pelo sensor "moderate resolution imaging spectroradiometer" (MODIS), para o estudo da dinâmica espaço-temporal de duas sub-regiões do bioma Pantanal. Foram utilizadas 139 imagens "enhanced vegetation index" (EVI), do produto MOD13 "vegetation index", dados de altimetria oriundos do "shuttle radar topography mission" (SRTM) e dados de precipitação do "tropical rainfall measuring mission" (TRMM). Para a redução da dimensionalidade dos dados, as imagens MODIS-EVI foram amostradas com base nas curvas de nível espaçadas em 10 m. Foram aplicadas as técnicas de análise de autocorrelação e análise de agrupamentos aos dados das amostras, e a análise de componentes principais na área total da imagem. Houve dependência tanto temporal quanto espacial da resposta espectral com a precipitação. A análise de agrupamentos apontou a presença de dois grupos, o que indicou a necessidade da análise completa da área. A análise de componentes principais permitiu diferenciar quatro comportamentos distintos: as áreas permanentemente alagadas; as áreas não inundáveis, compostas por vegetação; as áreas inundáveis com maior resposta de vegetação; e áreas com vegetação ripária.

scholarly journals A joint probabilistic index for objective drought identification: the case study of Haiti

2020 ◽  
Vol 20 (2) ◽  
pp. 471-487
Author(s):  
Beatrice Monteleone ◽  
Brunella Bonaccorso ◽  
Mario Martina
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Extreme Event ◽  
Risk Mitigation ◽  
Characteristic Curve ◽  
Standardized Precipitation Index ◽  
Early Warning Systems ◽  
Mitigation Strategies ◽  
Agricultural Drought ◽  
Drought Indices

Abstract. Since drought is a multifaceted phenomenon, more than one variable should be considered for a proper understanding of such an extreme event in order to implement adequate risk mitigation strategies such as weather or agricultural indices insurance programmes or disaster risk financing tools. This paper proposes a new composite drought index that accounts for both meteorological and agricultural drought conditions by combining in a probabilistic framework two consolidated drought indices: the standardized precipitation index (SPI) and the vegetation health index (VHI). The new index, called the probabilistic precipitation vegetation index (PPVI), is scalable, transferable all over the globe and can be updated in near real time. Furthermore, it is a remote-sensing product, since precipitation is retrieved from satellite data and the VHI is a remote-sensing index. In addition, a set of rules to objectively identify drought events is developed and implemented. Both the index and the set of rules have been applied to Haiti. The performance of the PPVI has been evaluated by means of a receiver operating characteristic curve and compared to that of the SPI and VHI considered separately. The new index outperformed SPI and VHI both in drought identification and characterization, thus revealing potential for an effective implementation within drought early-warning systems.

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