Ecological Corridor Construction Based on Least-Cost Modeling Using Visible Infrared Imaging Radiometer Suite (VIIRS) Nighttime Light Data and Normalized Difference Vegetation Index

Anthropic pressure is one of the main drivers of landscape change and biodiversity loss. Artificial nighttime light, which can affect species behavior, is an important human-induced threat to biodiversity, but it is often ignored in ecological connectivity research. To mitigate the adverse impacts of artificial lighting on biodiversity, this study integrates artificial nighttime light in landscape ecology and analyzes the influence of artificial nighttime light on landscape connectivity. A quantitative approach integrating nighttime light brightness from a Visible Infrared Imaging Radiometer Suite (VIIRS) with a normalized difference vegetation index (NDVI) from a Moderate-resolution Imaging Spectroradiometer (MODIS) is proposed to estimate the matrix resistance, which can identify the sensitive areas that are disrupted by nighttime light. It was found that the nightscape in the study area is significantly disrupted by nighttime light and the matrix resistance in the center of the study area significantly increases. Compared to the least-cost routes from the NDVI, the “dark” least-cost ecological corridors constructed using our approach apparently change in both location and distance. The corridors moved to the outer suburbs and rural areas, and the maximum increase in distance of the least-cost paths was 37.94%. Due to less disturbance from human activity and the maintenance of a pristine nightscape, “dark” ecological corridors can reduce the adverse effects of night lights and contribute to biodiversity. However, natural habitats have been greatly affected by nighttime light with the increase in global illumination, and it is essential that we improve public awareness of light pollution and formulate light-reduction policies and legislation.

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An Optimized Approach for Extracting Urban Land Based on Log-Transformed DMSP-OLS Nighttime Light, NDVI, and NDWI

Remote Sensing ◽

10.3390/rs13040766 ◽

2021 ◽

Vol 13 (4) ◽

pp. 766

Author(s):

Yuanmao Zheng ◽

Qiang Zhou ◽

Yuanrong He ◽

Cuiping Wang ◽

Xiaorong Wang ◽

...

Keyword(s):

Urban Development ◽

Urban Areas ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Development Planning ◽

Urban Land ◽

Sustainable Urban Development ◽

Land Use Patterns ◽

Water Index ◽

Nighttime Light

Quantitative and accurate urban land information on regional and global scales is urgently required for studying socioeconomic and eco-environmental problems. The spatial distribution of urban land is a significant part of urban development planning, which is vital for optimizing land use patterns and promoting sustainable urban development. Composite nighttime light (NTL) data from the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) have been proven to be effective for extracting urban land. However, the saturation and blooming within the DMSP-OLS NTL hinder its capacity to provide accurate urban information. This paper proposes an optimized approach that combines NTL with multiple index data to overcome the limitations of extracting urban land based only on NTL data. We combined three sources of data, the DMSP-OLS, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI), to establish a novel approach called the vegetation–water-adjusted NTL urban index (VWANUI), which is used to rapidly extract urban land areas on regional and global scales. The results show that the proposed approach reduces the saturation of DMSP-OLS and essentially eliminates blooming effects. Next, we developed regression models based on the normalized DMSP-OLS, the human settlement index (HSI), the vegetation-adjusted NTL urban index (VANUI), and the VWANUI to analyze and estimate urban land areas. The results show that the VWANUI regression model provides the highest performance of all the models tested. To summarize, the VWANUI reduces saturation and blooming, and improves the accuracy with which urban areas are extracted, thereby providing valuable support and decision-making references for designing sustainable urban development.

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

Sensors ◽

10.3390/s19245423 ◽

2019 ◽

Vol 19 (24) ◽

pp. 5423

Author(s):

Shou-Hao Chiang ◽

Noel Ivan Ulloa

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Infrared Imaging ◽

Fast Marching Method ◽

Fast Marching ◽

Burnt Area ◽

Water Index ◽

Multiple Sensors ◽

Biological Reserve

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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Are Karst Rocky Desertification Areas Affected by Increasing Human Activity in Southern China? An Empirical Analysis from Nighttime Light Data

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16214175 ◽

2019 ◽

Vol 16 (21) ◽

pp. 4175

Author(s):

Shi ◽

Yang ◽

Keyword(s):

Human Activities ◽

Socioeconomic Development ◽

Infrared Imaging ◽

Southern China ◽

Annual Growth Rate ◽

Karst Rocky Desertification ◽

Rocky Desertification ◽

Nighttime Lights ◽

Nighttime Light ◽

Night Lights

Due to remarkable socioeconomic development, an increasing number of karst rocky desertification areas have been severely affected by human activities in southern China. Effectively analyzing human activities in karst rocky desertification areas is a critical prerequisite for managing and restoring areas with tremendous negative impacts from desertification. At present, a timely and accurate way of quantifying the spatiotemporal variations of human activities in karst rocky desertification areas is still lacking. In this communication, we attempted to quantify human activities from the corrected Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) nighttime light composite data from 2012 to 2018 based on statistical analysis. The results show that a significant increase of night lights could be clearly identified during the study period. The total nighttime lights (TL) related to severe karst rocky desertification (S) were particularly concentrated in Guizhou and Yunnan. The nighttime light intensity (LI) related to the S areas in Chongqing was the strongest due to its rapid socioeconomic development. The annual growth rate of nighttime lights (GL) has been slow or even negative in Guangdong because of its various karst rocky desertification restoration programs. This communication could provide an effective approach for quantifying human activities and provide useful information about where prompt attention is required for policy-making on the restoration of the karst rocky desertification areas.

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Exploring VIIRS Continuity with MODIS in an Expedited Capability for Monitoring Drought-Related Vegetation Conditions

Remote Sensing ◽

10.3390/rs13061210 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1210

Author(s):

Trenton D. Benedict ◽

Jesslyn F. Brown ◽

Stephen P. Boyte ◽

Daniel M. Howard ◽

Brian A. Fuchs ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Infrared Imaging ◽

Geometric Mean ◽

Drought Monitoring ◽

Zero Bias ◽

Modis Ndvi ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Common Application

Vegetation has been effectively monitored using remote sensing time-series vegetation index (VI) data for several decades. Drought monitoring has been a common application with algorithms tuned to capturing anomalous temporal and spatial vegetation patterns. Drought stress models, such as the Vegetation Drought Response Index (VegDRI), often use VIs like the Normalized Difference Vegetation Index (NDVI). The EROS expedited Moderate Resolution Imaging Spectroradiometer (eMODIS)-based, 7-day NDVI composites are integral to the VegDRI. As MODIS satellite platforms (Terra and Aqua) approach mission end, the Visible Infrared Imaging Radiometer Suite (VIIRS) presents an alternate NDVI source, with daily collection, similar band passes, and moderate spatial resolution. This study provides a statistical comparison between EROS expedited VIIRS (eVIIRS) 375-m and eMODIS 250-m and tests the suitability of replacing MODIS NDVI with VIIRS NDVI for drought monitoring and vegetation anomaly detection. For continuity with MODIS NDVI, we calculated a geometric mean regression adjustment algorithm using 375-m resolution for an eMODIS-like NDVI (eVIIRS’) eVIIRS’ = 0.9887 × eVIIRS − 0.0398. The resulting statistical comparisons (eVIIRS’ vs. eMODIS NDVI) showed correlations consistently greater than 0.84 throughout the three years studied. The eVIIRS’ VegDRI results characterized similar drought patterns and hotspots to the eMODIS-based VegDRI, with near zero bias.

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Mapping Impacts of Human Activities from Nighttime Light on Vegetation Cover Changes in Southeast Asia

Land ◽

10.3390/land10020185 ◽

2021 ◽

Vol 10 (2) ◽

pp. 185

Author(s):

Nan Xia ◽

Manchun Li ◽

Liang Cheng

Keyword(s):

Southeast Asia ◽

Human Activities ◽

Vegetation Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Infrared Imaging ◽

Human Impacts ◽

Time Lag ◽

Temporal Segmentation ◽

Vegetation Growth

It is commonly believed that the impacts of human activities have decreased the natural vegetation cover, while some promotion of the vegetation growth has also been found. In this study, negative or positive correlations between human impacts and vegetation cover were tested in the Southeast Asia (SEA) region during 2012–2018. The Visible Infrared Imaging Radiometer Suite—Day/Night Band (VIIRS/DNB) nocturnal data were used as a measure of human activities and the moderate resolution imaging spectroradiometer (MODIS)/normalized difference vegetation index (NDVI) diurnal data were used as a measure of vegetation cover. The temporal segmentation method was introduced to calculate features of two sets of time series with spatial resolution of about 500 m, including the overall trend, maximum trend, start date, and change duration. The regions with large variation in human activities (V-change region) were first extracted by the Gaussian fitted method, and 8.64% of the entire SEA (VIIRS overall trend <−0.2 or >0.4) was set as the target analysis area. According to statistics, the average overall VIIRS trend for the V-change region in SEA was about 2.12, with a slight NDVI increment. The time lag effect was also found between vegetation cover and human impacts change, with an average of 10.26 months. Our results indicated a slight green overall trend in the SEA region over the most recent 7 years. The spatial pattern of our trend analysis results can be useful for vegetation management and regional planning.

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Construction and Optimization of Ecological Security Pattern Based on Spatial Syntax Classification—Taking Ningbo, China, as an Example

Land ◽

10.3390/land10040380 ◽

2021 ◽

Vol 10 (4) ◽

pp. 380

Author(s):

Yu Han ◽

Chaoyue Yu ◽

Zhe Feng ◽

Hanchu Du ◽

Caisi Huang ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Landscape Connectivity ◽

Development Trend ◽

Ecological Security ◽

Security Patterns ◽

Ecological Corridors ◽

Resistance Surface ◽

Ecological Protection ◽

Security Pattern

Urbanization is the development trend of all countries in the world, but it has caused considerable ecological problems that need to be alleviated by building ecological security patterns. This study took Ningbo as an example to construct and optimize an ecological security pattern. We analyzed land use types, normalized difference vegetation index, and landscape connectivity for ecological sources selection. In constructing the resistance surface, we considered natural and socio-economic factors. On this basis, we identified ecological corridors based on a minimum cumulative resistance model. Finally, the ecological security pattern was optimized through space syntax. Results showed that Ningbo has 18 ecological sources, with an area of 3051.27 km2 and 29 ecological corridors, with a length of 1172.18 km. Among them, 11 are first-level, 10 are second-level, and 8 are third-level corridors. After optimization, the area and protection cost of the ecological security pattern were significantly reduced, which can effectively alleviate the trade-off between ecological protection and economic development. This research can provide a reference for the construction and optimization of ecological security patterns and has reference significance for ecological protection in rapidly urbanized areas.

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A Synergetic Approach to Burned Area Mapping Using Maximum Entropy Modeling Trained with Hyperspectral Data and VIIRS Hotspots

Remote Sensing ◽

10.3390/rs12050858 ◽

2020 ◽

Vol 12 (5) ◽

pp. 858 ◽

Cited By ~ 3

Author(s):

Alfonso Fernández-Manso ◽

Carmen Quintano

Keyword(s):

Maximum Entropy ◽

Forest Fires ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Infrared Imaging ◽

Hyperspectral Data ◽

Burned Area ◽

Burned Areas ◽

Mediterranean Countries ◽

Burned Area Mapping

Southern European countries, particularly Spain, are greatly affected by forest fires each year. Quantification of burned area is essential to assess wildfire consequences (both ecological and socioeconomic) and to support decision making in land management. Our study proposed a new synergetic approach based on hotspots and reflectance data to map burned areas from remote sensing data in Mediterranean countries. It was based on a widely used species distribution modeling algorithm, in particular the Maximum Entropy (MaxEnt) one-class classifier. Additionally, MaxEnt identifies variables with the highest contribution to the final model. MaxEnt was trained with hyperspectral indexes (from Earth-Observing One (EO-1) Hyperion data) and hotspot information (from Visible Infrared Imaging Radiometer Suite Near Real-Time 375 m active fire product). Official fire perimeter measurements by Global Positioning System acted as a ground reference. A highly accurate burned area estimation (overall accuracy = 0.99%) was obtained, and the indexes which most contributed to identifying burned areas included Simple Ratio (SR), Red Edge Normalized Difference Vegetation Index (NDVI750), Normalized Difference Water Index (NDWI), Plant Senescence Reflectance Index (PSRI), and Normalized Burn Ratio (NBR). We concluded that the presented methodology enables accurate burned area mapping in Mediterranean ecosystems and may easily be automated and generalized to other ecosystems and satellite sensors.

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An Improved Correction Method of Nighttime Light Data Based on EVI and WorldPop Data

Remote Sensing ◽

10.3390/rs12233988 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3988

Author(s):

Pengfei Liu ◽

Qing Wang ◽

Dandan Zhang ◽

Yongzong Lu

Keyword(s):

Time Series ◽

Vegetation Index ◽

Infrared Imaging ◽

Multiple Scales ◽

Correction Method ◽

Growth Trend ◽

Absolute Relative Error ◽

Long Time ◽

Nighttime Light ◽

Provincial Scale

Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) data has the shortcomings of discontinuous and pixel saturation effect. It was also incompatible with the Soumi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) data. In view those shortcomings, this research put forward the WorldPop and the enhanced vegetation index (EVI) adjusted nighttime light (WEANTL) using EVI and WorldPop data to achieve intercalibration and saturation correction of DMSP/OLS data. A long time series of nighttime light images of china from 2001 to 2018 was constructed by fitting the DMSP/OLS data and NPP/VIIRS data. Corrected nighttime light images were examined to discuss the estimation ability of gross domestic product (GDP) and electric power consumption (EPC) on national and provincial scales, respectively. The results indicated that, (1) after correction, the nighttime light (NTL) data can guarantee the growth trend on national and regional scales, and the interannual volatility of the corrected NTL data is lower than that of the uncorrected NTL data; (2) on the national scale, compared with the established model of NTL data and GDP data (NTL-GDP), the determination coefficient (R2) and the mean absolute relative error (MARE) are 0.981 and 8.518%. The R2 and MARE of the established model of NTL data and EPC data (NTL-EPC) were 0.990 and 4.655%; (3) on the provincial scale, the R2 and MARE of NTL-GDP model under the provincial units are 0.7386 and 38.599%. The R2 value and MARE of NTL-EPC model are 0.8927 and 29.319%; (4) on the provincial scale, the R2 and MARE of NTL-GDP model on time series are 0.9667 and 10.877%. The R2 and MARE of NTL-GDP model on time series are 0.9720 and 6.435%; the established TNL-GDP and TNL-EPC models with 30 provinces data all passed the F-test at the 0.001 level; (5) the prediction accuracy of GDP and EPC on time series was nearly 100%. Therefore, the correction method provided in this research can be applied in estimating the GDP and EPC on multiple scales reliably and accurately.

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