Deriving Water Fraction and Flood Maps From MODIS Images Using a Decision Tree Approach

Sun, Donglian; Yu, Yunyue; Goldberg, Mitchell D.

doi:10.1109/jstars.2011.2125778

Cited by 95 publications

(62 citation statements)

References 30 publications

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“…This method was applied to many different regions over the Earth and published in several works: the Aral Sea [12], The Andean Altiplano ( [13,14]), Lake Tchad [11] , The IND ( [15,16]), and the Ganga basin [17]. Other methods using MODIS images were published for similar studies [18][19][20][21][22][23].…”

Section: Methodology To Detect Water Over the Ind With Modis And Valimentioning

confidence: 99%

Inundations in the Inner Niger Delta: Monitoring and Analysis Using MODIS and Global Precipitation Datasets

Bergé-Nguyen

Crétaux

2015

Remote Sensing

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A method of wetland mapping and flood survey based on satellite optical imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra instrument was used over the Inner Niger Delta (IND) from 2000-2013. It has allowed us to describe the phenomenon of inundations in the delta and to decompose the flooded areas in the IND into open water and mixture of water and dry land, and that aquatic vegetation is separated from bare soil and "dry" vegetation. An Empirical Orthogonal Function (EOF) analysis of the MODIS data and precipitation rates from a global gridded data set is carried out. Connections between flood sequence and precipitation patterns from the upstream part of the Niger and Bani river watersheds up to the IND are studied. We have shown that inter-annual variability of flood dominates over the IND and we have estimated that the surface extent of open water varies by a factor of four between dry and wet years. We finally observed an increase in vegetation over the 14 years of study and a slight decrease of open water.

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Section: Methodology To Detect Water Over the Ind With Modis And Valimentioning

confidence: 99%

Inundations in the Inner Niger Delta: Monitoring and Analysis Using MODIS and Global Precipitation Datasets

Bergé-Nguyen

Crétaux

2015

Remote Sensing

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“…Many studies show that water bodies are highly dynamic, and a large number of mixed pixels exist, particularly near the water boundaries [25][26][27]. The FROM-GLC water mask is derived from a hard classifier, which assigns each pixel to one of the defined land-cover types.…”

Section: Solving the Spectral Mixing Problem Using Local Spectral Unmmentioning

confidence: 99%

Improving the Accuracy of the Water Surface Cover Type in the 30 m FROM-GLC Product

Gong

Geng

et al. 2015

Remote Sensing

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Abstract:The finer resolution observation and monitoring of the global land cover (FROM-GLC) product makes it the first 30 m resolution global land cover product from which one can extract a global water mask. However, two major types of misclassification exist with this product due to spectral similarity and spectral mixing. Mountain and cloud shadows are often incorrectly classified as water since they both have very low reflectance, while more water pixels at the boundaries of water bodies tend to be misclassified as land. In this paper, we aim to improve the accuracy of the 30 m FROM-GLC water mask by addressing those two types of errors. For the first, we adopt an object-based method by computing the topographical feature, spectral feature, and geometrical relation with cloud for every water object in the FROM-GLC water mask, and set specific rules to determine whether a water object is misclassified. For the second, we perform a local spectral unmixing using a two-endmember linear mixing model for each pixel falling in the water-land boundary zone that is 8-neighborhood connected to water-land boundary pixels. Those pixels with big enough water fractions are determined as water. The procedure is automatic. Experimental results show that the total area of inland water has been decreased by 15.83% in the new global water mask compared with the FROM-GLC water mask. Specifically, OPEN ACCESSRemote Sens. 2015, 7 13508 more than 30% of the FROM-GLC water objects have been relabeled as shadows, and nearly 8% of land pixels in the water-land boundary zone have been relabeled as water, whereas, on the contrary, fewer than 2% of water pixels in the same zone have been relabeled as land. As a result, both the user's accuracy and Kappa coefficient of the new water mask (UA = 88.39%, Kappa = 0.87) have been substantially increased compared with those of the FROM-GLC product (UA = 81.97%, Kappa = 0.81).

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“…Because of its linearity in correlation while measured against biomass in paddy field, it is highly recommended to study the fuel load in cropland (Sakamoto et al, 2005 Temporal smoothing of EVI using Savitzky-Golay filter: Since our focus is to model the fuel load and its associated pollutant emission from fire pixels under rice and wheat systems, as a prerequisite to this, it is necessary to classify all the crop pixels according to cropping pattern. To accomplish it, we extracted the crop pixels for the year 2011-12 by running decision tree (DT) classification method based on the band threshold rules of descriptive statistics and factor analysis (Sun et al, 2011). The baseline assumption of doing DT classification during 2011-12 is that-these crop pixels have not undergone any non-agrarian transformation throughout the study period .…”

Section: Enhanced Vegetation Index (Evi)mentioning

confidence: 99%

GHG and Aerosol Emission From Fire Pixel During Crop Residue Burning Under Rice and Wheat Cropping Systems in North-West India

Acharya¹,

Sreekesh²,

Kulshrestha³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Emission of smoke and aerosol from open field burning of crop residue is a long-standing subject matter of atmospheric pollution. In this study, we proposed a new approach of estimating fuel load in the fire pixels and corresponding emissions of selected GHGs and aerosols i.e. CO 2 , CO, NO 2 , SO 2 , and total particulate matter (TPM) due to burning of crop residue under rice and wheat cropping systems in Punjab in north-west India from 2002 to 2012. In contrasts to the conventional method that uses RPR ratio to estimate the biomass, fuel load in the fire pixels was estimated as a function of enhanced vegetation index (EVI). MODIS fire products were used to detect the fire pixels during harvesting seasons of rice and wheat. Based on the field measurements, fuel load in the fire pixels were modelled as a function of average EVI using second order polynomial regression. Average EVI for rice and wheat crops that were extracted through Fourier transformation were computed from MODIS time series 16 day EVI composites. About 23% of net shown area (NSA) during rice and 11% during wheat harvesting seasons are affected by field burning. The computed average fuel loads are 11.32 t/ha (±17.4) during rice and 10.89 t/ha (±8.7) during wheat harvesting seasons. Calculated average total emissions of CO 2 , CO, NO 2 , SO 2 and TPM were 8108. 41, 657.85, 8.10, 4.10, and 133.21 Gg during rice straw burning and 6896.85, 625.09, 1.42, 1.77, and 57.55 Gg during wheat burning. Comparison of estimated values shows better agreement with the previous concurrent estimations. The method, however, shows its efficiency parallel to the conventional method of estimation of fuel load and related pollutant emissions.

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Deriving Water Fraction and Flood Maps From MODIS Images Using a Decision Tree Approach

Cited by 95 publications

References 30 publications

Inundations in the Inner Niger Delta: Monitoring and Analysis Using MODIS and Global Precipitation Datasets

Inundations in the Inner Niger Delta: Monitoring and Analysis Using MODIS and Global Precipitation Datasets

Improving the Accuracy of the Water Surface Cover Type in the 30 m FROM-GLC Product

GHG and Aerosol Emission From Fire Pixel During Crop Residue Burning Under Rice and Wheat Cropping Systems in North-West India

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