Meteorological Satellite Program-Operational Linescan System Research Articles

Predicting malaria outbreaks using earth observation measurements and spatiotemporal deep learning modelling: a South Asian case study from 2000 to 2017

BackgroundMalaria remains one the leading communicable causes of death. Approximately half of the world's population is considered at risk of infection, predominantly in African and South Asian countries. Although malaria is preventable, heterogeneity in sociodemographic and environmental risk factors over time and across diverse geographical and climatological regions make outbreak prediction challenging. Data-driven approaches accounting for spatiotemporal variability could offer potential for location-specific early warning tools for malaria. MethodsIn this case study, we developed and internally validated a data fusion approach to predict malaria incidence in Pakistan, India, and Bangladesh using geo-referenced environmental factors. For 2000–17, district-level malaria incidence rates for each country were obtained from the US Agency for International Development's Demographic and Health Survey datasets. Environmental factors included average annual temperature, rainfall, and normalised difference vegetation index, obtained from the Advancing Research on Nutrition and Agriculture (known as AReNA) project conducted by the International Food Policy Research Institute in 2020. Data on night-time light intensity was derived from two satellites of the National Oceanic and Atmospheric Administration Defense Meteorological Satellite Program—Operational Linescan System: Nighttime Lights Time Series Version 4, and VIIRS Nighttime Day/Night Band Composites version 1. A multi-dimensional spatiotemporal long short-term memory (M-LSTM) model was developed using data from 2000–16 and internally validated for the year 2017. The M-LSTM model consisted of four hidden layers, each with 100 LSTM units; a fully connected layer was used, followed by linear regression, to predict the malaria incidence rate for 2017 using spatiotemporal partitioning. Model performance was measured using accuracy and root mean squared error. Country-specific models were produced for Bangladesh, India, and Pakistan. Bivariate geospatial heatmaps were produced for a qualitative comparison of univariate environmental factors with malaria rates. FindingsMalaria incidence was predicted with 80·6% accuracy in districts across Pakistan, 76·7% in districts across India, and 99·1% in districts across Bangladesh. The root mean squared error was 7 × 10–4 for Pakistan, 4·86 × 10–6 for India, and 1·32 × 10–5 for Bangladesh. Bivariate maps showed an inverse relationship between night-time lights and malaria rates; whereas high malaria rates were found in areas with high temperature, rainfall, and vegetation. InterpretationMalaria outbreaks could be forecasted using remotely measured environmental factors. Modelling techniques that enable simultaneously forecasting ahead in time as well as across large geographical areas might potentially empower regional decision makers to manage outbreaks early. FundingNIHR Oxford Biomedical Research Centre Programme and The Higher Education Commission of Pakistan.

Tourism and marine crises: The impact of Sargassum invasion on Caribbean small island developing sates

Since 2011 the Caribbean has been experiencing large amounts of Sargassum on its beaches causing disruptions to tourism. There are nonetheless no large-scale empirical studies across the regizon on the impact of Sargassum on tourism and local economies. In addressing this knowledge gap this study investigates the impact of Sargassum on sandy beaches on tourism and sandy beach economic activity across 30 Caribbean Small Island Developing States. Spectral reflectance data from the MODIS satellite mission and a Floating Algae Index were used to detect the presence of Sargassum on sandy beaches. Tourism activity was measured using monthly tourist arrivals data from the Caribbean Tourism Organization and near sandy beaches nightlights data from the United States Air Force Defense Meteorological Satellite Program-Operational Linescan System. A Fixed effects panel regression model which controls for island specific time invariant effects, monthly and yearly time effects, and island specific time trends and allow for cross-sectional dependence and serial correlation was adopted. The results demonstrate that the average incidence of Sargassum reduces tourist arrivals growth by 1.1 percentage points up to 8 months after an outbreak with the highest incidence event having a 9 percentage points reduction. The results from nightlight intensity show a fall in sandy beach economic activity up to 6 months after an outbreak. An average Sargassum event of 0.25 and 0.15 constitute a reduction in sandy beach nightlight intensity of about 1.6 and 0.9 percent respectively.

Spatiotemporal Evolution of West Africa’s Urban Landscape Characteristics Applying Harmonized DMSP-OLS and NPP-VIIRS Nighttime Light (NTL) Data

Investigating urban expansion patterns aids in the management of urbanization and in ameliorating the socioeconomic and environmental issues associated with economic transformation and sustainable development. Applying Harmonized Defense Meteorological Satellite Program-Operational Line-scan System (DMSP-OLS) and the Suomi National Polar-Orbiting Partnership-Visible Infrared Imagery Radiometer Suite (NPP-VIIRS) Nighttime Light (NTL) data, this paper investigated the characteristics of urban landscape in West Africa. Using the harmonized NTL data, spatial comparison and empirical threshold methods were employed to detect urban changes from 1993 to 2018. We examined the rate of urban change and calculated the direction of the urban expansion of West Africa using the center-of-gravity method for urban areas. In addition, we used the landscape expansion index method to assess the processes and stages of urban growth in West Africa. The accuracy of urban area extraction based on NTL data were R 2 = 0.8314 in 2000, R 2 = 0.8809 in 2006, R 2 = 0.9051 in 2012 for the DMSP-OLS and the simulated NPP-VIIRS was R 2 = 0.8426 in 2018, by using Google Earth images as validation. The results indicated that there was a high rate and acceleration of urban landscapes in West Africa, with rates of 0.016 0, 0.017 3, 0.018 9, and 0.068 6, and accelerations of 0.31, 0.42, 0.54, and 0.90 for the periods of 1998-2003, 2003-2008, 2008-2013, and 2013-2018, respectively. The expansion direction of urban agglomeration in West Africa during 1993-2018 was mainly from the coast to inland. However, cities located in the Sahel Region of Africa and in the middle zone expanded from north to south. Finally, the results showed that the urban landscape of West Africa was mainly in a scattered and disordered 'diffusion' process, whereas only a few cities located in coastal areas experiencing the process of 'coalescence' according to urban growth phase theory. This study provides urban planners with relevant insights for the urban expansion characteristics of West Africa.

Effects of human activity intensity on habitat quality based on nighttime light remote sensing: A case study of Northern Shaanxi, China

Habitat quality is a crucial expression of the value of ecosystem services. Habitat quality issues caused by human activities are troubling dilemmas worldwide, and there is an urgent need to assess the impact of the large-scale human activity intensity on habitat quality. In this study, Northern Shaanxi, China, is used as an example to demonstrate how the impact on habitat quality can be explored by simulating the intensity of human activities using nighttime light remote sensing data from 2000 to 2020. Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) data, Visible Infrared Imaging Radiometer Suite-Day-Night Band (VIIRS-DNB) data, and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model were used in this study. The results showed that from 2000 to 2020, human activity intensity in Northern Shaanxi increased by 361.4 %, and the habitat quality decreased by 2.3 %. Habitat quality was more spatially clustered and spatially dependent than habitat degradation. Human activity intensity and habitat quality were significantly correlated. Habitat quality in Yulin city decreased significantly compared with that in Yan'an city. The differences in human activity types and economic structures of Yulin and Yan'an were an important factor for this phenomenon. The assessment of the impact of human activity intensity on habitat quality using nighttime light data is feasible and can be applied in other larger regions worldwide.

An Approach for Retrieving Consistent Time Series “Urban Core–Suburban-Rural” (USR) Structure Using Nighttime Light Data from DMSP/OLS and NPP/VIIRS

The long time series and consistent “urban core-suburban-rural” (USR) structure in a city region is essential to understanding urban–suburban–rural interaction and urbanization pathways. It is always considered to be a single land use type (e.g., impervious area) in remote sensing research. The long-term (1992–present) nighttime light (NTL) data of the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) provide the potential for retrieving time series of USR structure. In this study, we propose an improved approach to mapping the USR structure of the three subcategories based on a heuristic algorithm of Mann–Kendall mutation detection on the NTL quantile curve. First, a minor adjustment of VIIRS NTL is applied for matching the value ranges of DMSP NTL data and keeping the advantage of VIIRS to generate a long-term NTL dataset. Second, the heuristic algorithm of Mann–Kendall mutation detection is processed to find two optimal thresholds in the NTL quantile curve, which is used for USR extraction. Finally, a temporal consistency check is used to post-process the initial USR area for obtaining a more consistent and reliable USR sequence. To evaluate the performance of the proposed method, we retrieved the USR structures of 19 typical cities in China from 1992 to 2020 based on NTL datasets. The evaluations of spatiotemporal consistency compared with the validation data indicate that the USR retrieval results show good agreement with the land use map derived from Landsat images and the time series product from MODIS. The average overall accuracy (OA) of overall urban extent is higher than 0.95 and the average kappa coefficient (KC) reaches 0.6. Moreover, we investigated the urban dynamics and USR interactions of 19 cities from 1992 to 2020. Overall, this study proposes an improved approach for long-term USR mapping from NTL images at a regional scale and it will provide a valuable method for urbanization dynamics analysis.

A Novel Consistency Calibration Method for DMSP-OLS Nighttime Stable Light Time-Series Images

The Defense Meteorological Satellite Program-Operational Line-Scan System (DMSP-OLS) nighttime stable light (NSL) products have been widely employed for studying human activities and urbanization. However, due to the lack of on-board calibration of the OLS sensors, narrow radiation range, and different satellite platforms, the NSL products suffer from several limitations such as saturation effect and inconsistency problem. To solve these issues, this study proposed a spatio-temporal adaptive pseudo-invariant pixel (STAPIP) scheme for NSL time-series consistency calibration based on eight-year DMSP-OLS radiance calibrated (RC) products. The unsaturated pixels were calibrated with the temporal adaptive pseudo-invariant pixel (TAPIP) method, while the saturated pixels were calibrated with the spatial adaptive PIP (SAPIP) method. To verify the proposed method's effectiveness, we applied it in China and compared its results with the results of several existing NSL calibration methods. Then we further analyzed the correlations between the calibrated NSL time-series images and several important socio-economic indicators, such as population, gross domestic product (GDP), and electricity consumption (EC). Results showed that the NSL time-series images calibrated by the STAPIP method have the best consistency and highest correlation with socio-economic indicators at both provincial and city levels. Thus, this study can provide an accurate and stable NSL time-series product for research on human activities and the urbanization process.

Political Dynasties and Economic Development: Evidence using Nighttime Light in the Philippines

Abstract Political dynasties, by limiting political competition, are thought to exacerbate corruption, poverty, and abuse of power. This paper examines the economic effects of the presence of political dynasties in Philippine cities and municipalities, taking into account possible channels in the local dynastic cycle – the framework in which politicians try to balance their goals to perform well for their constituents, to divert resources for personal gain, and to continue to be in power. Due to the lack of extensive income accounts or other economic indicators in finer geographical units (i.e., city or municipality level), we use the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) nighttime light data as our proxy for economic activity. Using a panel of Philippine municipalities and cities, we find that, in general, the relationship of political dynasties on economic performance is weak. However, we find that a higher share of economic expenditures leads to lower economic development in municipalities where the mayor, governor, and congressman belong to the same clan. We see this as an indication of weak institutions of checks and balances in localities with dynasties.

An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration

Abstract. The nighttime light (NTL) satellite data have been widely used to investigate the urbanization process. The Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) stable nighttime light data and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data are two widely used NTL datasets. However, the difference in their spatial resolutions and sensor design requires a cross-sensor calibration of these two datasets for analyzing a long-term urbanization process. Different from the traditional cross-sensor calibration of NTL data by converting NPP-VIIRS to DMSP-OLS-like NTL data, this study built an extended time series (2000–2018) of NPP-VIIRS-like NTL data through a new cross-sensor calibration from DMSP-OLS NTL data (2000–2012) and a composition of monthly NPP-VIIRS NTL data (2013–2018). The proposed cross-sensor calibration is unique due to the image enhancement by using a vegetation index and an auto-encoder model. Compared with the annual composited NPP-VIIRS NTL data in 2012, our product of extended NPP-VIIRS-like NTL data shows a good consistency at the pixel and city levels with R2 of 0.87 and 0.95, respectively. We also found that our product has great accuracy by comparing it with DMSP-OLS radiance-calibrated NTL (RNTL) data in 2000, 2004, 2006, and 2010. Generally, our extended NPP-VIIRS-like NTL data (2000–2018) have an excellent spatial pattern and temporal consistency which are similar to the composited NPP-VIIRS NTL data. In addition, the resulting product could be easily updated and provide a useful proxy to monitor the dynamics of demographic and socioeconomic activities for a longer time period compared to existing products. The extended time series (2000–2018) of nighttime light data is freely accessible at https://doi.org/10.7910/DVN/YGIVCD (Chen et al., 2020).

Analysis of spatial inequality using DMSP‐OLS nighttime‐light satellite imageries: A case study of Thailand

AbstractUnbalanced regional development has been a persistent concern in Thailand, and satellite imageries can provide alternative data for examining the dynamics of regional development. This study validated the consistency between Defense Meteorological Satellite Program–Operational Linescan System (DMSP‐OLS) nighttime light (NTL) imageries and data from the official nationwide socio‐economic survey. It found that the two sources exhibited a statistically significant correlation in 1994–2013. On the basis of this finding, the NTL index was applied to the computations of the local indicators of spatial association (LISA) and Moran's I statistics. The results for LISA showed that Bangkok and its vicinity were highly concentrated development areas during the study period. However, the obtained Moran's I statistics indicated that the degree of concentration continuously decreased. NTL‐based Gini, Theil, and Atkinson inequality indices were also computed to validate the spatial expansion of regional development. All three NTL‐based indices showed that regional inequality decreased during the study period.

Long-Term Resilience Curve Analysis of Wenchuan Earthquake-Affected Counties Using DMSP-OLS Nighttime Light Images

This article demonstrates the potential of Defense Meteorological Satellite Program-Operational Linescan System stable nighttime light imagery for county-level earthquake resilience analysis. In this article, we firstly intercalibrated the nighttime light data and calculated the total sum of stable lights for each of the 261 counties affected by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mw</i> 7.9 Wenchuan earthquake from 1992 to 2013; secondly established an earthquake resilience model based on a nighttime light index to analyze the static and dynamic resilience of different counties; and lastly discussed the possible influencing factors from geographic, disaster-related, political and socioeconomic perspectives. The results first show that the static resilience of the extremely hard-hit counties in the Wenchuan earthquake-affected area increased from south to north along the long axis of the intensity zone, and plain and hilly counties exhibited faster short-term economic recovery than plateau and mountainous counties. Moreover, all extremely hard-hit counties except Wenchuan, Dujiangyan and Pingwu recovered to the normal level before 2011, whereas 52% of the counties in the generally affected areas recovered within three years. Through linear regression analysis, we also found that the different earthquake resilience capabilities across counties are most likely related to the soil liquefaction risk, average elevation, land-use degree, and socioeconomic factors, such as the industrial structure, the population age distribution, and social welfare.

Study on Average Housing Prices in the Inland Capital Cities of China by Night-time Light Remote Sensing and Official Statistics Data

In this paper, the annually average Defense Meteorological Satellite Program-Operational Linescan System (DMSP/OLS) night-time light data is first proposed as a surrogate indicator to mine and forecast the average housing prices in the inland capital cities of China. First, based on the time-series analysis of individual cities, five regression models with gross error elimination are established between average night-time light intensity (ANLI) and average commercial residential housing price (ACRHP) adjusted by annual inflation rate or not from 2002 to 2013. Next, an optimal model is selected for predicting the ACRHPs in 2014 of these capital cities, and then verified by the interval estimation and corresponding official statistics. Finally, experimental results show that the quadratic polynomial regression is the optimal mining model for estimating the ACRHP without adjustments in most provincial capitals and the predicted ACRHP of these cities are almost in their interval estimations except for the overrated Chengdu and the underestimated Wuhan, while the adjusted ACRHP is all in prediction interval. Overall, this paper not only provides a novel insight into time-series ACRHP data mining based on time-series ANLI for capital city scale but also reveals the potentiality and mechanism of the comprehensive ANLI to characterize the complicated ACRHP. Besides, other factors influencing housing prices, such as the time-series lags of government policy, are tested and analysed in this paper.

Observing community resilience from space: Using nighttime lights to model economic disturbance and recovery pattern in natural disaster

A major challenge for measuring community resilience is the lack of empirical observations in disasters. As an effective tool to observe human activities on the earth surface, night-time light (NTL) remote sensing images can fill the gap of empirical data for measuring community resilience in natural disasters. This study introduces a quantitative framework to model recovery patterns of economic activity in a natural disaster using the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) images. The utility of the framework is demonstrated in a retrospective study of Hurricane Katrina, which uncovered the great economic impact of Katrina and spatial variation of the disturbance and recovery pattern of economic activity. Environmental and socio-economic factors that potentially influence economic recovery were explored in statistical analyses. Instead of a static and holistic index, the framework measures resilience as a dynamic process. The analysis results provide actionable information for prompting resilience in diverse communities and in different phases of a disaster. In addition to Hurricane Katrina, the resilience modeling framework is applicable for other disaster types. The introduced approaches and findings increase our understanding about the complexity of community resilience and provide support for developing resilient and sustainable communities.

Assessment of the impact of the 2010 Haiti earthquake on human activity based on DMSP/OLS time series nighttime light data

Earthquake disasters and postdisaster reconstruction have profound impacts on human society. We use time series nighttime light images collected by the defense meteorological satellite program-operational linescan system sensors as a proxy data source for human activities (HAs). After calibration, a time series analysis method is used to study the distribution and intensity of the changes in HAs after an earthquake. We consider the Haiti earthquake an example to analyze the HA sequence patterns and the aggregation patterns of the HA centers. The results show the following: (1) postearthquake recovery and reconstruction efforts led to increases in the long-term HAs, but the level of increased HA was usually low. (2) The level of postearthquake HAs in the most affected areas (seismic intensity zone VIII+) increased, whereas the postearthquake HAs in severely affected areas (seismic intensity zones VI and VII) were more fragmented. (3) The recovery of HAs in seismic intensity zone VIII+ (mainly Port-au-Prince) required 2 years, but the actual time may be far longer.

An assessment of global electric power consumption using the Defense Meteorological Satellite Program-Operational Linescan System nighttime light imagery

Industrialization and urbanization have led to a remarkable increase of electric power consumption (EPC) during the past decades. To assess the changing patterns of EPC at the global scale, this study utilized nighttime lights in conjunction with population and built-up datasets to map EPC at 1 km resolution. Firstly, the inter-calibrated nighttime light data were enhanced using the V4.0 Gridded Population Density data and the Global Human Settlement Layer. Secondly, linear models were calibrated to relate EPC to the enhanced nighttime light data; these models were then employed to estimate per-pixel EPC in 2000 and 2013. Finally, the spatiotemporal patterns of EPC between the periods were analyzed at the country, continental, and global scales. The evaluation of the EPC estimation shows a reasonable accuracy at the provincial scale with R2 of 0.8429. Over 30% of the human settlements in Asia, Europe, and North America showed apparent EPC growth. At the national scale, moderate and high EPC growth was observed in 45% of the built-up areas in East Asia. The spatial clustering patterns revealed that EPC decreased in Russia and the Western Europe. This study provides fresh insight into the spatial pattern and variations of global electric power consumption.

Comparison on Multi-Scale Urban Expansion Derived from Nightlight Imagery between China and India

“The Dragon and the Elephant” between China and India is an important manifestation of global multipolarization in the 21st century. As engines of global economic growth, the two rising powers have followed similar courses of development but possess important differences in modes of development and urban development, which have attracted the widespread attention of scholars. From a geospatial perspective, and based on continuous annual night light data (Defense Meteorological Satellite Program-Operational Linescan System, DMSP-OLS) from 1992 to 2012, this paper conducts a multi-scale comparative analysis of urban development differences between China and India by employing various approaches such as the Gini coefficient, Getis–Ord Gi* index, and the Urban Expansion Intensity Index (UEII). The results show that: (1) The urban land space of the two countries expand rapidly, with the average annual expansion rate of China and India being 5.24% and 3.85%, respectively. The urban land expansion rate in China is 1.36 times faster than that in India. Resource-typed towns in arid northwest China and the resource-typed towns in central India have developed rapidly in recent years. (2) The unbalanced development in India is more prominent than in China; and the regional and provincial development imbalances in China are shrinking, while India’s imbalances are improving slowly and its regional differences are gradually widening. (3) The spatial pattern of land use in both countries shows significant coastal and inland differences. The difference between the east, the central regions, and the west is the main spatial pattern of China’s regional development, while the difference between the north and the south is the spatial pattern of India’s regional development. (4) There are obvious differences in the expansion intensity of core cities between the two countries. From 1997 to 2007, the expansion intensity of core cities in China was relatively higher than that in India, while that in India was relatively higher than that in China from 2007 to 2012.

Further adjustment of the Defense Meteorological Satellite Program—Operational Linescan System using radar data

In the past, the Defense Meteorological Satellite Program—Operational Linescan System (DSMP/OLS) data have been successfully adjusted based on the presence of vegetation in each pixel. We propose a further adjustment based on radar data obtained by the SeaWinds scatterometer. It is shown that the adjustment results in substantial accuracy gains, measured as increased correlation with the more accurate Visible Infrared Imaging Radiometer Suite data as well as with the existing instances of radiance calibrated DSMP/OLS data. The proposed adjustment is easy to implement. The improvement of the proposed method is particularly significant in arid regions where vegetation is relatively sparse.

Estimation of Cargo Handling Capacity of Coastal Ports in China Based on Panel Model and DMSP-OLS Nighttime Light Data

The cargo handling capacity of a port is the most basic and important indicator of port size. Based on the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) nighttime light data and panel model, this study attempts to estimate the cargo handling capacity of 28 coastal ports in China using satellite remote sensing. The study confirmed that there is a very close correlation between DMSP-OLS nighttime light data and the cargo handling capacity of the ports. Based on this correlation, the panel data model was established for remote sensing-based estimation of cargo handling capacity at the port and port group scales. The test results confirm that the nighttime light data can be used to accurately estimate the cargo handling capacity of Chinese ports, especially for the Yangtze River Delta Port Group, Pearl River Delta Port Group, Southeast Coastal Port Group, and Southwest Coastal Port Group that possess huge cargo handling capacities. The high accuracy of the model reveals that the remote sensing analysis method can make up for the lack of statistical data to a certain extent, which helps to scientifically analyze the spatiotemporal dynamic changes of coastal ports, provides a strong basis for decision-making regarding port development, and more importantly provides a convenient estimation method for areas that have long lacked statistical data on cargo handling capacity.

Evolution of the Pattern of Spatial Expansion of Urban Land Use in the Poyang Lake Ecological Economic Zone.

To grasp the evolutionary characteristics and regularity of urban land expansion patterns in the Poyang Lake Ecological Economic Zone, this study, based on nighttime lighting data, uses the Landsat series satellite simultaneous data and cluster analysis to correct the Defense Meteorological Satellite Program–Operational Linescan System (DMSP-OLS) nighttime lighting data and then uses the auxiliary data-based comparison method to determine the threshold for extracting the urban built-up area. Based on this threshold, a total of eight typical landscape pattern indicators, including landscape total area, total patches number, patches density, maximum patches index, and agglomeration index, etc., are selected. Next, the landscape spatial pattern analysis method and standard deviation ellipse method are used. The results show the following: (1) In 1992–2013, urbanization in the Poyang Lake Ecological Economic Zone expanded rapidly. The urban built-up area increased by 8.13 times, the number of plaques increased by 1.5 times, and the shape complexity of landscape plaques gradually increased. There is a large correlation between the changes in the total boundary length, and the average boundary density, the average annual growth rate of the two is 21.33% and 17.45%. (2) The two indicators of maximum plaque index and aggregation index show a downward trend year by year. However, there are some fluctuations and irregularities in the evolution of the total landscape area, total plaque number and plaque density. (3) The long axis and the short axis of the standard deviation ellipse of the Poyang Lake Ecological Economic Zone show small variation during the inspection period and generally have an elliptical shape. The movement of the center of gravity is mainly from the southwest to the northeast, but the migration of the center of gravity is relatively small. Based on this, this paper proposes three countermeasures and suggestions as a guide to promote the optimization and development of the spatial expansion pattern of the Poyang Lake eco-economic zone.

An improved temporal mixture analysis unmixing method for estimating impervious surface area based on MODIS and DMSP-OLS data

Impervious surface area (ISA) is an important indicator for monitoring the intensity of human activity and ecological environment changes. Developing effective methods for estimation of ISA at different scales has thus been pursued by many scientists. The temporal mixture analysis (TMA), which is a variant of spectral mixture analysis that makes full use of the phenological information of different land cover types, is suitable for estimating the ISA fraction at a large scale. The existing TMA-based ISA fraction estimation methods rely on the assumption that pure pixels exist for all the endmembers, which, however, is not true in the case of coarse-resolution datasets. Moreover, the existing method cannot effectively differentiate bare soil from ISA effectively, which may lead to overestimation of the ISA fraction. To address these problems, we propose a new ISA estimation method based on TMA in this study, using a Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) products, the GlobeLand30 product, and the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) data. The proposed method contains four major steps. First, the MODIS NDVI time-series datasets and GlobeLand30 land cover product were used to create an NDVI temporal profile subset for the TMA model. Second, a preliminary ISA fraction map was derived on the basis of optimized endmember temporal profiles, which were generated by unmixing the selected NDVI temporal profile subset through an improved spatial-spectral preprocessing nonnegative matrix factorization algorithm (ISSPP-NMF). Then, the preliminary ISA fraction was further optimized by incorporating the EVI-adjusted night-time light index (EANTLI), which can mitigate both saturation problems and the blooming effect of the DMSP-OLS data. An effective threshold method was introduced in this step to reduce the impact of bare soil on the ISA estimation. Finally, the estimated fraction of ISA was evaluated through accuracy assessment. The proposed method was tested in two study areas, namely, Guangdong Province and the Yangtze River Delta (YRD) of China, to prove its applicability in different regions. Effectiveness of the proposed method was proven through the comparison between the proposed method with traditional TMA-based methods. The results from these analyses indicate that the proposed method outperforms the others in ISA estimation, with an overall root mean square error (RMSE) of 9.2% and a coefficient of determination (R2) of 0.8872 in Guangdong and a RMSE of 8.9% and R2 of 0.8923 in YRD. This study also proves that the ISSPP-NMF method can produce more appropriate endmembers regardless of the existence of pure pixels. The post-processing with the EANLTI procedure can effectively reduce the bare soil effect in TMA-based ISA estimation.

Long-Term Monitoring of the Impacts of Disaster on Human Activity Using DMSP/OLS Nighttime Light Data: A Case Study of the 2008 Wenchuan, China Earthquake

Time series monitoring of earthquake-stricken areas is significant in evaluating post-disaster reconstruction and recovery. The time series of nighttime light (NTL) data collected by the defense meteorological satellite program-operational linescan system (DMSP/OLS) sensors provides a unique and valuable resource to study changes in human activity (HA) because of the long period of available data. In this paper, the DMSP/OLS NTL images’ digital number (DN) is used as a proxy for the intensity of HA since there is a high correlation between them. The purpose of this study is to develop a methodology to analyze the changes of intensity and distribution of HA in different areas affected by a 2008 earthquake in Wenchuan, China. In order to compare the trends of HA before and after the earthquake, the DMSP/OLS NTL images from 2003 to 2013 were processed and analyzed. However, their analysis capability is greatly limited owing to a lack of in-flight calibration. To improve the continuity and comparability of DMSP/OLS NTL images, this study developed an automatic intercalibration method to systematically correct NTL data. The results reveal that: (1) compared with the HA before the earthquake, the reconstruction and recovery of the Wenchuan earthquake have led to a significant increase of HA in earthquake-stricken areas within three years after the earthquake; (2) the fluctuation of HA in a severely-affected area is greater than that in a less-affected area; (3) recovery efforts increase development in the most affected areas to levels that exceeded the rates in similar areas which experienced less damage; and (4) areas alongside roads and close to reconstruction projects exhibited increased development in regions with otherwise low human activity.