With the advent of the free U.S. Landsat data policy it is now feasible to consider the generation of global coverage 30m Landsat data sets with temporal reporting frequency similar to that provided ...by the monthly Web Enabled Landsat (WELD) products. A statistical Landsat metadata analysis is reported considering more than 800,000 Landsat 5 TM and Landsat 7 ETM+ acquisitions obtained from the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center archive. The global monthly probabilities of acquiring a cloud-free land surface observation for December 1998 to November 2001 (2000 epoch) and from December 2008 to November 2011 (2010 epoch) are reported to assess the availability of the Landsat data in the USGS Landsat archive for global multi-temporal land remote sensing applications. The global probabilities of acquiring a cloud-free land surface observation in each of three different seasons with the highest seasonal probabilities of cloud-free land surface observation are reported, considering one, two and three years of Landsat data, to assess the availability of Landsat data for global land cover mapping. The probabilities are derived considering Landsat 5 TM only, Landsat 7 ETM+ only, and both sensors combined, to examine the relative benefits of using one or both Landsat sensors. The results demonstrate the utility of combing both Landsat 5 TM and Landsat 7 ETM+ data streams to take advantage of their different acquisition patterns and to mitigate the deleterious impact of the Landsat 7 ETM+ 2003 scan line failure. Sensor combination provided a greater global acquisition coverage with a 1.7% to 14.4% higher percentage of land locations acquired monthly compared to considering Landsat 7 ETM+ data alone. The mean global monthly probability of a cloud-free land surface observation for the combined sensors was up to nearly 1.4 and 6.7 times greater than for ETM+ and TM alone respectively. The probability of acquiring a cloud-free Landsat land surface observation in different seasons was greater when more years of data were considered and when both Landsat sensor data were combined. Considering combined sensors and 36months of data, 86.4% and 84.2% of the global land locations had probabilities ≥0.95 for the 2000 and 2010 epochs respectively, with a global mean probability of 0.92 (σ 0.24) for the 2000 epoch and 0.90 (σ 0.28) for the 2010 epoch. These results indicate that 36months of combined Landsat sensor data will provide sufficient land surface observations for 30m global land cover mapping using a multi-temporal supervised classification scheme.
► The impact of clouds and SLC_OFF on availability of clear Landsat land observations. ► Combing Landsat 5 TM and Landsat 7 ETM+ data streams is advantages. ► Compare to ETM+ alone both sensors provide up to 14.4% higher monthly land coverage. ► 36 months of combined Landsat 5 and 7 data can support 30m global land cover mapping.
Spatiotemporal data fusion is a methodology to generate images with both high spatial and temporal resolution. Most spatiotemporal data fusion methods generate the fused image at a prediction date ...based on pairs of input images from other dates. The performance of spatiotemporal data fusion is greatly affected by the selection of the input image pair. There are two criteria for selecting the input image pair: the "similarity" criterion, in which the image at the base date should be as similar as possible to that at the prediction date, and the "consistency" criterion, in which the coarse and fine images at the base date should be consistent in terms of their radiometric characteristics and imaging geometry. Unfortunately, the "consistency" criterion has not been quantitatively considered by previous selection strategies. We thus develop a novel method (called "cross-fusion") to address the issue of the determination of the base image pair. The new method first chooses several candidate input image pairs according to the "similarity" criterion and then takes the "consistency" criterion into account by employing all of the candidate input image pairs to implement spatiotemporal data fusion between them. We applied the new method to MODIS-Landsat Normalized Difference Vegetation Index (NDVI) data fusion. The results show that the cross-fusion method performs better than four other selection strategies, with lower average absolute difference (AAD) values and higher correlation coefficients in various vegetated regions including a deciduous forest in Northeast China, an evergreen forest in South China, cropland in North China Plain, and grassland in the Tibetan Plateau. We simulated scenarios for the inconsistency between MODIS and Landsat data and found that the simulated inconsistency is successfully quantified by the new method. In addition, the cross-fusion method is less affected by cloud omission errors. The fused NDVI time-series data generated by the new method tracked various vegetation growth trajectories better than previous selection strategies. We expect that the cross-fusion method can advance practical applications of spatiotemporal data fusion technology.
Artificial impervious areas are predominant indicators of human settlements. Timely, accurate, and frequent information on artificial impervious areas is critical to understanding the process of ...urbanization and land use/cover change, as well as of their impacts on the environment and biodiversity. Despite their importance, there still lack annual maps of high-resolution Global Artificial Impervious Areas (GAIA) with longer than 30-year records, due to the high demand of high performance computation and the lack of effective mapping algorithms. In this paper, we mapped annual GAIA from 1985 to 2018 using the full archive of 30-m resolution Landsat images on the Google Earth Engine platform. With ancillary datasets, including the nighttime light data and the Sentinel-1 Synthetic Aperture Radar data, we improved the performance of our previously developed algorithm in arid areas. We evaluated the GAIA data for 1985, 1990, 1995, 2000, 2005, 2010, and 2015, and the mean overall accuracy is higher than 90%. A cross-product comparison indicates the GAIA data are the only dataset spanning over 30 years. The temporal trend in GAIA agrees well with other datasets at the local, regional, and global scales. Our results indicate that the GAIA reached 797,076 km2 in 2018, which is 1.5 times more than that in 1990. China and the United States (US) rank among the top two in artificial impervious area, accounting for approximately 50% of the world's total in 2018. The artificial impervious area of China surpassed that of the US in 2015. By 2018, the remaining eight among the top ten countries are India, Russia, Brazil, France, Italy, Germany, Japan, and Canada. The GAIA dataset can be freely downloaded from http://data.ess.tsinghua.edu.cn.
•We improved the performance of “Exclusion/Inclusion” approach in arid regions.•We mapped global artificial impervious areas (GAIA) with Google Earth Engine.•The mean overall accuracy over multiple years is higher than 90%.•GAIA reached 797,076 km2 by 2018, more than 2.5 times that of 1990.•The top five countries are China, US, India, Russia, and Brazil.
The estimation of land-surface evapotranspiration (ET) at high spatial and temporal resolutions is important for management and planning of agricultural water resources, but available remote sensing ...data generally have either high spatial resolution or high temporal resolution. To overcome this limitation, we evaluated the use of a data fusion scheme, Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), to determine the surface parameters needed to estimate daily ET at a Landsat-like scale (100 m). In particular, we fused Moderate Resolution Imaging Spectroradiometer (MODIS) data with Landsat Enhanced Thematic Mapper Plus (ETM+) data in analysis of the Heihe River Basin (HRB), an arid region of Northwest China. The surface parameters were then used to drive the revised Surface Energy Balance System (SEBS) model to estimate daily ET at a spatial resolution of 100 m for this an arid irrigation area during the crop growth period (April to October) in 2012. The results showed that the daily ET estimates had a mean absolute percent error (MAPE) of 12% and a root mean square error (RMSE) of 0.81 mm/day relative to ground measurements from 18 eddy covariance (EC) sites in the study area. The validation results indicated good accuracy for land cover types of maize and vegetables, a slight overestimation for residential and wetland sites, and a slight underestimation for orchard site. Our comparison of the input parameter fusion approach (IPFA) and the ET fusion approach (ETFA) with field measurements indicated the IPFA was superior than the ETFA for land surfaces with high spatial heterogeneity. Furthermore, our high spatiotemporal ET estimates indicated that irrigation water efficiencies of the irrigation districts (mean: 70%) and villages (mean: 62%) had large spatial heterogeneity. These results point to the need for calculating ET at a high spatiotemporal resolution for monitoring and improving irrigation water efficiency at local scales. Our findings suggest that the proposed framework of estimating daily ET at a Landsat-like scale using multi-source data may also be applicable to other heterogeneous landscapes by providing a foundation for management of water resources at the basin or finer scales.
•An estimation scheme for daily High-Temporal Landsat-Like (HiTLL) ET was proposed.•The input parameter fusion approach (IPFA) performs well for heterogeneous surface.•The HiTLL ET facilitate assessment of irrigation water efficiency at a finer scale.
Landsat 8 is the most recent generation of Landsat satellite missions that provides remote sensing imagery for earth observation. The Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images, together ...with Landsat-8 Operational Land Imager (OLI) and Thermal Infrared sensor (TIRS) represent fundamental tools for earth observation due to the optimal combination of the radiometric and geometric images resolution provided by these sensors. However, there are substantial differences between the information provided by Landsat 7 and Landsat 8. In order to perform a multi-temporal analysis, a cross-comparison between image from different Landsat satellites is required. The present study is based on the evaluation of specific intercalibration functions for the standardization of main vegetation indices calculated from the two Landsat generation images, with respect to main land use types. The NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), LSWI (Land Surface Water Index), NBR (Normalized Burn Ratio), VIgreen (Green Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and EVI (Enhanced Vegetation Index) have been derived from August 2017 ETM+ and OLI images (path: 188; row: 32) for the study area (Basilicata Region, located in the southern part of Italy) selected as a highly representative of Mediterranean environment. Main results show slight differences in the values of average reflectance for each band: OLI shows higher values in the near-infrared (NIR) wavelength for all the land use types, while in the short-wave infrared (SWIR) the ETM+ shows higher reflectance values. High correlation coefficients between different indices (in particular NDVI and NDWI) show that ETM+ and OLI can be used as complementary data. The best correlation in terms of cross-comparison was found for NDVI, NDWI, SAVI, and EVI indices; while according to land use classes, statistically significant differences were found for almost all the considered indices calculated with the two sensors.
The Landsat satellites have been providing spectacular imagery of the Earth's surface for over 40years. However, they acquire images at view angles ±7.5° from nadir that cause small directional ...effects in the surface reflectance. There are also variations with solar zenith angle over the year that can cause apparent change in reflectance even if the surface properties remain constant. When Landsat data from adjoining paths, or from long time series are used, a model of the surface anisotropy is required to adjust all Landsat observations to a uniform nadir view (primarily for visual consistency, vegetation monitoring, or detection of subtle surface changes). Here a generalized approach is developed to provide consistent view angle corrections across the Landsat archive. While this approach is not applicable for generation of Landsat surface albedo, which requires a full characterization of the surface bidirectional reflectance distribution function (BRDF), or for correction to a constant solar illumination angle across a wide range of sun angles, it provides Landsat nadir BRDF-adjusted reflectance (NBAR) for a range of terrestrial monitoring applications.
The Landsat NBAR is derived as the product of the observed Landsat reflectance and the ratio of the reflectances modeled using MODIS BRDF spectral model parameters for the observed Landsat and for a nadir view and fixed solar zenith geometry. In this study, a total of 567 conterminous United States (CONUS) January and July 2010 Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper (ETM+) images that have swath edge overlapping paths sensed in alternating backscatter and forward scattering orientations were used. The average difference between Landsat 5 TM and Landsat 7 ETM+ surface reflectance in the forward and backward scatter directions at the overlapping Landsat scan edges was quantified. The CONUS July view zenith BRDF effects were about 0.02 in the Landsat visible bands, and about 0.03, 0.05 and 0.06, in the 2.1μm, 1.6μm and near-infrared bands respectively. Comparisons of Landsat 5 TM and Landsat 7 ETM+ NBAR derived using MODIS BRDF spectral model parameters defined with respect to different spatial and temporal scales, and defined with respect to different land cover types, were undertaken. The results suggest that, because the BRDF shapes of different terrestrial surfaces are sufficiently similar over the narrow 15° Landsat field of view, a fixed set of MODIS BRDF spectral model parameters may be adequate for Landsat NBAR derivation with little sensitivity to the land cover type, condition, or surface disturbance. A fixed set of BRDF spectral model parameters, derived from a global year of highest quality snow-free MODIS BRDF product values, are provided so users may implement the described Landsat NBAR generation method.
•Landsat NBAR derivation method developed using MODIS BRDF model parameters•Based on BRDF shapes of terrestrial surfaces similar over Landsat field of view•Fixed BRDF model parameters compared with local ones•Global fixed parameters provided so users may implement the method•Method insensitive to land cover and so is applicable to all Landsat record
Formal planning and development of what became the first Landsat satellite commenced over 50 years ago in 1967. Now, having collected earth observation data for well over four decades since the 1972 ...launch of Landsat-1, the Landsat program is increasingly complex and vibrant. Critical programmatic elements are ensuring the continuity of high quality measurements for scientific and operational investigations, including ground systems, acquisition planning, data archiving and management, and provision of analysis ready data products. Free and open access to archival and new imagery has resulted in a myriad of innovative applications and novel scientific insights. The planning of future compatible satellites in the Landsat series, which maintain continuity while incorporating technological advancements, has resulted in an increased operational use of Landsat data. Governments and international agencies, among others, can now build an expectation of Landsat data into a given operational data stream. International programs and conventions (e.g., deforestation monitoring, climate change mitigation) are empowered by access to systematically collected and calibrated data with expected future continuity further contributing to the existing multi-decadal record. The increased breadth and depth of Landsat science and applications have accelerated following the launch of Landsat-8, with significant improvements in data quality.
Herein, we describe the programmatic developments and institutional context for the Landsat program and the unique ability of Landsat to meet the needs of national and international programs. We then present the key trends in Landsat science that underpin many of the recent scientific and application developments and follow-up with more detailed thematically organized summaries. The historical context offered by archival imagery combined with new imagery allows for the development of time series algorithms that can produce information on trends and dynamics. Landsat-8 has figured prominently in these recent developments, as has the improved understanding and calibration of historical data. Following the communication of the state of Landsat science, an outlook for future launches and envisioned programmatic developments are presented. Increased linkages between satellite programs are also made possible through an expectation of future mission continuity, such as developing a virtual constellation with Sentinel-2. Successful science and applications developments create a positive feedback loop—justifying and encouraging current and future programmatic support for Landsat.
•Landsat program approaching 50 years of continuous global data collection.•Landsat-8 successfully on-orbit; Landsat-9 under development; Landsat-10 being scoped.•Open data has accelerated science and application developments.•Value of calibrated data shown for science, applications, and towards virtual constellations.•Time series analysis of Landsat offering new insights on earth system and human activity.
Lakes are sensitive indicators of anthropogenic climate change and also respond to direct human activities. Yet, long-term lake inventories and quantitative evaluation of the factors driving observed ...lake changes across China remain elusive. Here, for the first time, we examined multi-decadal lake area changes in China during 1960s–2015, using historical topographic maps and >3831 Landsat satellite images, including lakes as fine as ≥1 km2 in size. In addition, we quantified the causes of lake changes from climatic and anthropogenic factors. The total area of lakes in China has increased by 5858.06 km2 (9%) between 1960s and 2015, and with heterogeneous spatial variations. Lake area changes in the Tibetan Plateau, Xinjiang, and Northeast Plain and Mountain regions reveal significant increases of 5676.75, 1417.15, 1134.87 km2 (≥15%), respectively, but the Inner-Mongolian Plateau shows an obvious decrease of 1223.76 km2 (22%). We find that 141 new lakes have appeared predominantly in the arid western China; but 333 lakes, mainly located in the humid eastern China, have disappeared over the past five decades. We conclude that climate factors have played a dominant role in lake changes across China, coupled with noticeable anthropogenic contribution of ~35% in the Eastern Plain and Yunnan-Guizhou Plateau. This study has substantial implications to improve decision support regarding water-resource management strategies and land-use planning throughout China.
•Produced a comprehensive China lake dataset from the 1960s to 2015•Total lake area increased by 9% over five decades with evident regional differences•New lakes and lake area expansion in the arid western China due to climate change•Disappeared and shrinking lakes in the humid eastern China due to human activity
Despite containing extraordinary levels of biodiversity, lowland (<200 m asl) tropical forests are extremely threatened globally. Southeast Asia is an area of high species richness and endemicity ...under considerable anthropogenic threat with, unfortunately, scant focus on its lowland forests. We estimated extent of lowland forest loss from 1998 to 2018, including inside protected areas and determined the vulnerability of this remaining forest. Maximum likelihood classification techniques were used to classify Landsat images to estimate lowland forest cover in 1998 and 2018. We used Bayesian belief networks with 20 variables to evaluate vulnerability of the forest that remained in 2018. Analyses were conducted at two spatial scales: landscape patch (analogous to ecoregion) and country level. Over 20 years, >120,000 km2 of forest (50% of forest present in 1998) was lost. Of the 14 lowland forest patches, 6 lost >50% of their area. At the country scale, Cambodia had the greatest deforestation (>47,500 km2). In 2018, 18% of the lowlands were forested, and 20% of these forests had some formal protection. Approximately 50% of the lowland forest inside protected areas (c. 11,000 km2) was also lost during the study period. Most lowland forest remaining is highly vulnerable; eight landscape patches had >50% categorized as such. Our results add to a growing body of evidence that the presence of protected areas alone will not prevent further deforestation. We suggest that more collaborative conservation strategies with local communities that accommodate conservation concessions specifically for lowland forests are urgently needed to prevent further destruction of these valuable habitats.
Pérdida y Vulnerabilidad de los Bosques de Tierras Bajas en la Parte Continental del Sudeste Asiático
Resumen
A pesar de que contienen niveles extraordinarios de biodiversidad, los bosques tropicales de tierras bajas (<200 m snm) se encuentran bajo amenazas extremas en todo el mundo. El sudeste de Asia es un área con una riqueza alta de especies y endemismos bajo amenazas antropogénicas considerables, desafortunadamente, con un enfoque exiguo sobre sus bosques de tierras bajas. Estimamos la extensión de la pérdida de bosques de tierras bajas desde 1998 hasta 2018, incluyendo aquellos bosques que se encuentran dentro de áreas protegidas, y determinamos la vulnerabilidad del bosque que permanece. Usamos técnicas de clasificación de la probabilidad máxima para clasificar imágenes Landsat y así estimar la cobertura de bosque de tierras bajas en 1998 y 2018. Usamos redes de opinión bayesiana con 20 variables para evaluar la vulnerabilidad del bosque que permanecía en pie en 2018. Los análisis fueron realizados a dos escalas espaciales: a nivel de fragmento de paisaje (análogo a la ecorregión) y a nivel de país. A lo largo de 20 años, se perdieron >120,000 km2 de bosque (50% del bosque presente en 1998). De los 14 fragmentos de bosque de tierras bajas, seis perdieron >50% de su área. A la escala de país, Camboya tuvo la mayor deforestación (>47,500 km2). En 2018, el 18% de las tierras bajas contaban con bosque y el 20% de estos bosques tenían algún tipo de protección formal. Aproximadamente el 50% del bosque de tierras bajas que se encuentra dentro de áreas protegidas (aprox. 11,000 km2) también se perdió durante el periodo de estudio. La mayoría del bosque de tierras bajas que todavía permanece tiene una vulnerabilidad muy alta; ocho de los fragmentos de paisaje tenían >50% categorizado de tal manera. Nuestros resultados se suman a un cuerpo creciente de evidencia de que la sola presencia de las áreas protegidas no va a prevenir una mayor deforestación. Sugerimos que se necesitan urgentemente más estrategias de conservación colaborativa con comunidades locales que acomoden las concesiones de conservación específicamente para los bosques de tierras bajas para prevenir una mayor destrucción de estos hábitats tan valiosos.
【摘要】
低地 (海拔低于两百米) 热带森林拥有及其丰富的生物多样性, 但在全球范围内正面临严重威胁。东南亚地区的物种丰富度和特有性很高, 同时也受到严重的人为威胁, 而不幸的是该地区的低地森林很少得到关注。本研究估计了1998 年至 2018 年包括保护区内部的低地森林的丧失程度, 并确定了剩余森林的脆弱性。我们利用最大似然分类法对 Landsat 陆地卫星图像进行了分类, 以估计低地森林覆盖面积在1998 年至 2018 年的变化, 并使用有 20 个变量的贝叶斯信念网络评估了 2018 年剩余森林的脆弱性。分析在景观斑块 (类似于生态区域) 和国家水平这两个空间尺度上进行。结果表明, 20 多年来, 有 12 万平方公里的森林消失了, 占 1998 年森林总面积的 50%。在 14 个低地森林斑块中, 6 个斑块的面积丧失达到 50% 。在国家尺度上, 柬埔寨森林丧失的情况最为严重 (>47,500 平方公里) 。2018 年, 18% 的低地被森林覆盖, 其中 20% 的森林受到正式保护。研究期间保护区内有大约 50% 的低地森林 (约 11000 平方公里) 已丧失。剩下的低地森林大多高度脆弱, 这类森林占 8 个景观斑块中超过 50% 的地区。我们的研究结果进一步证明, 仅靠建立保护区并不足以阻止森林丧失。我们建议, 为了防止这些宝贵的栖息地继续遭到破坏, 目前迫切需要与当地社区采取更多的合作保护策略, 以适应低地森林的保护特许协议。【翻译: 胡怡思; 审校: 聂永刚】关键词
Article impact statement: From 1998 to 2018, mainland Southeast Asia lost 50% of its lowland forest, including a 50% loss inside protected areas.
Dense time series of Landsat 8 and Sentinel-2 imagery are creating exciting new opportunities to monitor, map, and characterize temporal dynamics in land surface properties with unprecedented spatial ...detail and quality. By combining imagery from the Landsat 8 Operational Land Imager and the MultiSpectral Instrument on-board Sentinel-2A and -2B, the remote sensing community now has access to moderate (10–30 m) spatial resolution imagery with repeat periods of ~3 days in the mid-latitudes. At the same time, the large combined data volume from Landsat 8 and Sentinel-2 introduce substantial new challenges for users. Land surface phenology (LSP) algorithms, which estimate the timing of phenophase transitions and quantify the nature and magnitude of seasonality in remotely sensed land surface conditions, provide an intuitive way to reduce data volumes and redundancy, while also furnishing data sets that are useful for a wide range of applications including monitoring ecosystem response to climate variability and extreme events, ecosystem modelling, crop-type discrimination, and land cover, land use, and land cover change mapping, among others. To support the need for operational LSP data sets, here we describe a continental-scale land surface phenology algorithm and data product based on harmonized Landsat 8 and Sentinel-2 (HLS) imagery. The algorithm creates high quality times series of vegetation indices from HLS imagery, which are then used to estimate the timing of vegetation phenophase transitions at 30 m spatial resolution. We present results from assessment efforts evaluating LSP retrievals, and provide examples illustrating the character and quality of information related to land cover and terrestrial ecosystem properties provided by the continental LSP dataset that we have developed. The algorithm is highly successful in ecosystems with strong seasonal variation in leaf area (e.g., deciduous forests). Conversely, results in evergreen systems are less interpretable and conclusive.
•We present a new algorithm that maps continental scale land surface phenology at 30 m resolution.•Results capture fine scale variation in phenology related to land use, land cover and topography.•Comparisons against PhenoCam and MODIS data demonstrate the high quality of estimated metrics.
PDF
