Mapping and Tracking Forest Burnt Areas in the Indio Maiz Biological Reserve Using Sentinel-3 SLSTR and VIIRS-DNB Imagery

Wildfires are considered one of the most major hazards and environmental issues worldwide. Recently, Earth observation satellite (EOS) sensors have proven to be effective for wildfire detection, although the quality and usefulness of the data are often hindered by cloud presence. One practical workaround is to combine datasets from multiple sensors. This research presents a methodology that utilizes data of the recently-launched Sentinel-3 sea and land surface temperature radiometer (S3-SLSTR) to reflect its applicability for detecting wildfires. In addition, visible infrared imaging radiometer suite day night band (VIIRS-DNB) imagery was introduced to assure day-night tracking capabilities. The wildfire event in the Indio Maiz Biological Reserve, Nicaragua, during 3–13 April 2018, was the study case. Six S3-SLSTR images were processed to compute spectral indices, such as the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), and the normalized burn ratio (NBR), to perform image segmentation for estimating the burnt area. The results indicate that 5870.7 ha of forest was affected during the wildfire, close to the 5945 ha reported by local authorities. In this study, the fire expansion was delineated and tracked in the Indio Maiz Biological Reserve using a modified fast marching method on nighttime-sensed temporal VIIRS-DNB. This study shows the importance of S3-SLSRT for wildfire monitoring and how it can be complemented with VIIRS-DNB to track burning biomass at daytime and nighttime.

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STUDY ON SPATIAL-TEMPORAL VARIATION OF HONG LAKE BASED ON VIIRS AND MODIS IMAGES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w7-797-2017 ◽

2017 ◽

Vol XLII-2/W7 ◽

pp. 797-802

Author(s):

C. Li ◽

Y. Zhong ◽

W. Zhang

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Infrared Imaging ◽

Spatial Location ◽

Water Extraction ◽

Water Index ◽

Average Area ◽

Flood Season ◽

Modis Image ◽

Lake Waters

Hong Lake is the largest lake in Hubei Province. With the increase of Hong Lake economic activity, the area, spatial location and shape of Hong Lake have changed greatly in the past. In this paper, we used the images, which is from the visible infrared imaging radiometer (VIIRS). First, we selected the images of Hong Lake waters on December 6, 2016 and December 26, 2015. Then we extracted the water bodies by the single-band method, spectral relationship method, normalized difference water index (NDWI) were used, and the effect-s were compared. Second, the images of Hong Lake waters in summer and winter were selected from 2012 to 2016, respectively. Last, The NDWI was used to extract the water body and compared with the MODIS image extraction effect in the same period. As a result of the vegetation around Hong Lake, the water is extracted by NDWI and normalized difference vegetation index (NDVI). It is found that for the VIIRS image, the NDWI is the best in the water extraction of Hong Lake. The NDVI + NDWI method is beneficial to the extraction of water covered with aquatic plants. VIIRS image extraction is better than MODIS image. In addition, from the study of VIIRS and MODIS to Hong Lake waters in the five years of water extraction and area calculation, 2012&ndash;2016 period, Hong Lake’s average area of 348.213&thinsp;km2 in flood season, in dry season average area of 349.163&thinsp;km2. The largest area for the 2012 flood season 389.751&thinsp;km2, the smallest area of 2016 flood season 306.177&thinsp;km2. Overall, Hong Lake’s area changes little.

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Improving GOES Advanced Baseline Imager (ABI) aerosol optical depth (AOD) retrievals using an empirical bias correction algorithm

Atmospheric Measurement Techniques ◽

10.5194/amt-13-5955-2020 ◽

2020 ◽

Vol 13 (11) ◽

pp. 5955-5975

Author(s):

Hai Zhang ◽

Shobha Kondragunta ◽

Istvan Laszlo ◽

Mi Zhou

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Bias Correction ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Infrared Imaging ◽

Retrieval Algorithm ◽

Correction Algorithm ◽

Time Step

Abstract. The Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-R (GOES-R) series enables retrieval of aerosol optical depth (AOD) from geostationary satellites using a multiband algorithm similar to those of polar-orbiting satellites' sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). However, this work demonstrates that the current version of GOES-16 (GOES-East) ABI AOD has diurnally varying biases due to limitations in the land surface reflectance relationships between the 0.47 µm band and the 2.2 µm band and between the 0.64 µm band and 2.2 µm band used in the ABI AOD retrieval algorithm, which vary with the Sun–satellite geometry and NDVI (normalized difference vegetation index). To reduce these biases, an empirical bias correction algorithm has been developed based on the lowest observed ABI AOD of an adjacent 30 d period and the background AOD at each time step and at each pixel. The bias correction algorithm improves the performance of ABI AOD compared to AErosol RObotic NETwork (AERONET) AOD, especially for the high and medium (top 2) quality ABI AOD. AOD data for the period 6 August to 31 December 2018 are used to evaluate the bias correction algorithm. After bias correction, the correlation between the top 2 quality ABI AOD and AERONET AOD improves from 0.87 to 0.91, the mean bias improves from 0.04 to 0.00, and root-mean-square error (RMSE) improves from 0.09 to 0.05. These results for the bias-corrected top 2 qualities ABI AOD are comparable to those of the corrected high-quality ABI AOD. By using the top 2 qualities of ABI AOD in conjunction with the bias correction algorithm, the areal coverage of ABI AOD is increased by about 100 % without loss of data accuracy.

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Assessment of the Hail Damage on Agricultural Crops from North of Baragan Plain, Romania, Using Remote Sensing Data

10.21203/rs.3.rs-1180239/v1 ◽

2021 ◽

Author(s):

Claudiu Valeriu Angearu ◽

Irina Ontel ◽

Anisoara Irimescu ◽

Burcea Sorin

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Remote Sensing Data ◽

Weather Radar ◽

Radar Data ◽

Landsat 8 ◽

Water Index ◽

Hail Damage ◽

Sentinel 2A

Abstract Hail is one of the dangerous meteorological phenomena facing society. The present study aims to analyze the hail event from 20 July 2020, which affected the villages of Urleasca, Traian, Silistraru and Căldăruşa from the Traian commune, Baragan Plain. The analysis was performed on agricultural lands, using satellite images in the optical domain: Sentinel-2A, Landsat-8, Terra MODIS, as well as the satellite product in the radar domain: Soil Water Index (SWI), and weather radar data. Based on Sentinel-2A images, a threshold of 0.05 of the Normalized Difference Vegetation Index (NDVI) difference was established between the two moments of time analyzed (14 and 21 July), thus it was found that about 4000 ha were affected. The results show that the intensity of the hail damage was directly proportional to the Land Surface Temperature (LST) difference values in Landsat-8, from 15 and 31 July. Thus, the LST difference values higher than 12° C were in the areas where NDVI suffered a decrease of 0.4-0.5. The overlap of the hail mask extracted from NDVI with the SWI difference situation at a depth of 2 cm from 14 and 21 July confirms that the phenomenon recorded especially in the west of the analyzed area, highlighted by the large values (greater than 55 dBZ) of weather radar reflectivity as well, indicating medium–large hail size. This research also reveals that satellite data is useful for cross validation of surface-based weather reports and weather radar derived products.

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AUTOMATIC PADDY RICE MAPPING INTERFACE USING ARCENGINE AND LANDSAT8 IMAGERY (CASE STUDY IN NORTH PART OF IRAN)

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-2-w3-79-2014 ◽

2014 ◽

Vol XL-2/W3 ◽

pp. 79-83 ◽

Cited By ~ 1

Author(s):

Sh. Bahramvash Shams

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Initial Period ◽

Rice Paddy ◽

Paddy Rice ◽

Landsat 8 ◽

Water Index ◽

The North ◽

North Part

Recognition of paddy rice boundaries is an essential step for many agricultural processes such as yield estimation, cadastre and water management. In this study, an automatic rice paddy mapping is proposed. The algorithm is based on two temporal images: an initial period of flooding and after harvesting. The proposed method has several steps include: finding flooded pixels and masking unwanted pixels which contain water bodies, clouds, forests, and swamps. In order to achieve final paddy map, indexes such as Normalized Difference Vegetation Index (NDVI) and Land Surface Water Index (LSWI) are used. Validation is performed by rice paddy boundaries, which were drawn by an expert operator in Google maps. Due to this appraisal good agreement (close to 90%) is reached. The algorithm is applied to Gilan province located in the north part of Iran using Landsat 8 date 2013. Automatic Interface is designed based on proposed algorithm using Arc Engine and visual studio. In the Interface, inputs are Landsat bands of two time periods including: red (0.66 μm), blue (0.48 μm), NIR (0.87 μm), and SWIR (2.20 μm), which should be defined by user. The whole process will run automatically and the final result will provide paddy map of desire year.

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Remote Sensing Derived Indices for Tracking Urban Land Surface Change in Case of Earthquake Recovery

Remote Sensing ◽

10.3390/rs12050895 ◽

2020 ◽

Vol 12 (5) ◽

pp. 895 ◽

Cited By ~ 2

Author(s):

Sahar Derakhshan ◽

Susan L. Cutter ◽

Cuizhen Wang

Keyword(s):

Remote Sensing ◽

Land Surface ◽

Disaster Recovery ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Temporal Variations ◽

Growth Trend ◽

Reconstruction Process ◽

Water Index ◽

Post Disaster

The study of post-disaster recovery requires an understanding of the reconstruction process and growth trend of the impacted regions. In case of earthquakes, while remote sensing has been applied for response and damage assessment, its application has not been investigated thoroughly for monitoring the recovery dynamics in spatially and temporally explicit dimensions. The need and necessity for tracking the change in the built-environment through time is essential for post-disaster recovery modeling, and remote sensing is particularly useful for obtaining this information when other sources of data are scarce or unavailable. Additionally, the longitudinal study of repeated observations over time in the built-up areas has its own complexities and limitations. Hence, a model is needed to overcome these barriers to extract the temporal variations from before to after the disaster event. In this study, a method is introduced by using three spectral indices of UI (urban index), NDVI (normalized difference vegetation index) and MNDWI (modified normalized difference water index) in a conditional algebra, to build a knowledge-based classifier for extracting the urban/built-up features. This method enables more precise distinction of features based on environmental and socioeconomic variability, by providing flexibility in defining the indices’ thresholds with the conditional algebra statements according to local characteristics. The proposed method is applied and implemented in three earthquake cases: New Zealand in 2010, Italy in 2009, and Iran in 2003. The overall accuracies of all built-up/non-urban classifications range between 92% to 96.29%; and the Kappa values vary from 0.79 to 0.91. The annual analysis of each case, spanning from 10 years pre-event, immediate post-event, and until present time (2019), demonstrates the inter-annual change in urban/built-up land surface of the three cases. Results in this study allow a deeper understanding of how the earthquake has impacted the region and how the urban growth is altered after the disaster.

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Mapeo de la recuperación de la vegetación quemada mediante la clasificación de índices espectrales pre- y post-incendio

Revista de Teledetección ◽

10.4995/raet.2017.7931 ◽

2017 ◽

pp. 37 ◽

Cited By ~ 1

Author(s):

M. A Peña ◽

J. Ulloa

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Confusion Matrix ◽

The State ◽

Spectral Indices ◽

Landsat Images ◽

Full Data ◽

Burnt Area ◽

Water Index ◽

Normalized Difference Water Index

This study analyzed the state of recovery of the burnt vegetation in the National Park of Torres del Paine between December, 2011 and March, 2012. The calculation and comparison of the NVDI (normalized difference vegetation index) of the burnt area throughout a time series of 24 Landsat images acquired before, during and after the fire (2009- 2015), showed the temporal variation in the biomass levels of the burnt vegetation. The subsequent classification and comparison of the spectral indices: NDVI, NBR (normalized burnt ratio) and NDWI (normalized difference water index) on a full-data available and phenologically matched pre- and post-fire image pair (acquired in October 2009 and 2014), enabled to analyze and mapping the state of recovery of the burnt vegetation. The results show that the area of the lowest classes of all the spectral indices of the pre-fire date became the most dominant on the post-fire date. The pre- and post- fire NDVI class crossing by a confusion matrix showed that the highest and most prevailing pre-fire NDVI classes, mostly corresponding to hydromorphic forests and Andean scrubs, turned into the lowest class in 2014. The remaining area, comprising Patagonian steppe, reestablished its biomass levels in 2014, mostly exhibiting the same pre-fire NDVI classes. These results may provide guidelines to monitor and manage the regeneration of the vegetation impacted by this fire.

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Analysis on Thermal Islands Effectors in Ramadi City, Iraq Using Multi-temporal Landsat Images

Al-Adab Journal ◽

10.31973/aj.v1i135.983 ◽

2020 ◽

Vol 1 (135) ◽

pp. 67-78

Author(s):

Ismael Abbas Hurat

Keyword(s):

Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Landsat 8 ◽

Landsat Images ◽

Water Index ◽

Normalized Difference Water Index ◽

Landsat 7 ◽

Multi Temporal

This paper analyzes the effects of urban density, vegetation cover, and water body on thermal islands measured by land surface temperature in Al Anbar province, Iraq using multi-temporal Landsat images. Images from Landsat 7 ETM and Landsat 8 OLI for the years 2000, 2014, and 2018 were collected, pre-processed, and anal yzed. The results suggested that the strongest correlation was found between the Normalized Difference Built-up Index (NDBI) and the surface temperature. The correlation between the Normalized Difference Vegetation Index (NDVI) and the surface temperature was slightly weaker compared to that of NDBI. However, the weakest correlation was found between the Normalized Difference Water Index (NDWI) and the temperature. The results obtained in this research may help the decision makers to take actions to reduce the effects of thermal islands by looking at the details in the produced maps and the analyzed values of these spectral indices.

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Development of a New Phenology Algorithm for Fine Mapping of Cropping Intensity in Complex Planting Areas Using Sentinel-2 and Google Earth Engine

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10090587 ◽

2021 ◽

Vol 10 (9) ◽

pp. 587

Author(s):

Yan Guo ◽

Haoming Xia ◽

Li Pan ◽

Xiaoyang Zhao ◽

Rumeng Li ◽

...

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Growth Cycle ◽

Google Earth ◽

Henan Province ◽

Cropping Intensity ◽

Water Index ◽

Google Earth Engine ◽

Sentinel 2

Cropping intensity is a key indicator for evaluating grain production and intensive use of cropland. Timely and accurately monitoring of cropping intensity is of great significance for ensuring national food security and improving the level of national land management. In this study, we used all Sentinel-2 images on the Google Earth Engine cloud platform, and constructed an improved peak point detection method to extract the cropping intensity of a heterogeneous planting area combined with crop phenology. The crop growth cycle profiles were extracted from the multi-temporal normalized difference vegetation index (NDVI) and land surface water index (LSWI) datasets. Results show that by 2020, the area of single cropping, double cropping, and triple cropping in the Henan Province are 52,236.9 km2, 74,334.1 km2, and 1927.1 km2, respectively; the corresponding producer accuracies are 86.12%, 93.72%, and 91.41%, respectively; the corresponding user accuracies are 88.99%, 92.29%, and 71.26%, respectively. The overall accuracy is 90.95%, and the Kappa coefficient is 0.81. Using the sown area in the statistical yearbook data of cities in the Henan Province to verify the extraction results of this paper, the R2 is 0.9717, and the root mean square error is 1715.9 km2. This study shows that using all the Sentinel-2 data, the phenology algorithm, and cloud computing technology has great potential in producing a high spatio-temporal resolution dataset for crop remote sensing monitoring and agricultural policymaking in complex planting areas.

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Quantitative Analysis of Spatial Heterogeneity and Driving Forces of the Thermal Environment in Urban Built-up Areas: A Case Study in Xi’an, China

Sustainability ◽

10.3390/su13041870 ◽

2021 ◽

Vol 13 (4) ◽

pp. 1870

Author(s):

Xuan Zhao ◽

Jianjun Liu ◽

Yuankun Bu

Keyword(s):

Spatial Heterogeneity ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Driving Forces ◽

Thermal Environment ◽

Explanatory Power ◽

Sustainable Urban Development ◽

Road Density ◽

Water Index

Clarifying the spatial heterogeneity of urban heat island (UHI) effect is of great significance for promoting sustainable urban development. A GeoDetector was used to detect the influential natural and society factors. Natural factors (normalized difference vegetation index (NDVI), soil-regulating vegetation index (SAVI), normalized building index (NDBI), and modified normalized difference water index (MNDWI)) as well as society factors (road density (RDD), and population density (POPD)) were selected as driving factors to be tested for their explanatory power for land surface temperature (LST). Results indicated that the Moran’s I index value for the LST of the built-up area is 0.778. The top three factors influencing the LST were NDBI, NDVI, and SAVI, the explanatory power of which was 0.7593, 0.6356, and 0.6356, respectively. The interactive explanatory power for NDBI and MNDWI was 0.8108 and for NDBI and RDD was 0.8002, these two interactions are double enhanced interaction relationships. The results of this study play a guiding role in the development of urban thermal environment regulation schemes and ecological environment planning.

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