Landsat Series Of Satellites Research Articles

Spatially Weighted Estimation of Broadacre Crop Growth Improves Gap-Filling of Landsat NDVI

Seasonal climate is the main driver of crop growth and yield in broadacre grain cropping systems. With a 40-year record of 30 m resolution images and 16-day revisits, the Landsat satellite series is ideal for producing long-term records of remotely sensed phenology to build understanding of how climate affects crop growth. However, the time-series of Landsat images exhibits gaps caused by cloud cover, which is common in wet periods when crops reach maximum growth. We propose a novel spatial–temporal approach to gap-filling that avoids data fusion. Crop growth curve estimation is used to perform temporal smoothing and incorporation of spatial weights allows spatial smoothing. We tested our approach using Landsat NDVI data acquired for an 8000 ha study area in Western Australia using a train/test approach where 157 available Landsat-7 images between 2013 and 2019 were used to train the model, and 95 at least 80% cloud-free Landsat-8 images from the same period were used to test its performance. We found that compared to nonspatial estimation, use of spatial weights in growth curve estimation improved correlation between observed and predicted NDVI by 75%, MAE by 31% and RMSE by 75%. For cropland, the correlation is improved by 58%, the MAE by 36% and the RMSE by 76%. We conclude that spatially weighted estimation of crop growth curves can be used to fill spatial and temporal gaps in Landsat NDVI for the purpose of within-field monitoring. Our approach is also applicable to other data sources and vegetation indices.

Land Use - Land Cover Change Analysis of Karur Town - A GIS Approach

Land use / Land cover change is one of the most sensitive factors that show the interactions between human activities and the ecological environment. This research study demonstrated the importance of geographical information system and remote sensing technologies in spatial temporal data analysis and also this paper shows a GIS and remote sensing approach for modeling of spatial - temporal pattern of land use and land cover change (LULC) in a fastest growing towns / industrial region of Karur town. QGIS 3.10 version and Arc GIS 10.2 software platforms were utilized in the study for Image processing, LULC mapping and change detection analysis. USGS Earth explorer Landsat series satellite imageries were acquired and LULC maps were prepared for the years 1991, 2000, 2010 and 2020. Supervised classification with maximum likelihood algorithm is adopted for LULC classification. The LULC classes are Built upland, Agricultural land, Barren land and Water body based on NRSA Level – I supervised classification. The Built-up area has drastically increased from 1991 to 2020. It has increased more than double. It was 17 percent in 1991 and increased to 40 percent in 2020. This clearly shows Karur town is the becoming more and more urbanized.

Wetland Changes and Their Relation to Climate Change in the Pumqu Basin, Tibetan Plateau.

Wetland ecosystems play one of the most crucial roles in the world. Wetlands have the functions of ecological water storage, water supply, and climate regulation, which plays an indispensable role in global environmental security. The Pumqu River Basin (PRB) is located in an area with extremely vulnerable ecological environment, where climate change is obvious. Understanding wetland distribution, changes and causes in the PRB are of great importance to the rational management and protection of wetlands. Using the Landsat series satellite images, wetlands of this area in 2000, 2010, and 2018 were extracted. The results showed that (1) there were obvious regional differences in wetland types and their distribution patterns in the basin. Wetlands were mainly distributed in areas with slopes less than 12° and at elevations between 4000 m and 5500 m. (2) During the past 20 years, the wetland area in the basin decreased, and the changing trend of wetlands was different. Palustrine wetlands decreased tremendously, riverine and lacustrine wetlands first decreased and then increased, while floodplain wetlands first increased and then decreased. Palustrine wetlands were reclaimed to cultivated land, but the proportion of reclamation is small. (3) Climate dominated wetland changes in the PRB. The changes in riverine and lacustrine wetlands were mainly affected by the warm-season average temperature, the change in palustrine wetlands was mainly related to the annual precipitation and the warm-season average temperature, and the change in floodplain wetlands was related to the warm-season precipitation. To achieve sustainable development, the government plays a guiding role and actively formulates and implements wetland protection policies, such as restricting or prohibiting grazing on wetlands, which play an important role in wetland protection and restoration.

Monitoring and driving force analysis of spatial and temporal change of water area of Hongjiannao Lake from 1973 to 2019

Hongjiannao Lake is the largest desert freshwater lake in China and the world's largest breeding habitat for gulls, which is of great significance to the local ecological environment and the breeding of key protected birds, and was listed as a national key protected lake in 2012. This study uses Landsat series satellite remote sensing data from 1973 to 2019 in combination with the NDWI water body index and OSTU adaptive threshold methods to obtain a 46-year area dataset for the study area. On this basis, the temporal and spatial evolution of the lake and its coupling relationship with natural factors and human activities were studied. The results show that (1) during the period 1973–2019, Hongjiannao Lake has undergone three stages: slight shrinkage (1973–1997), sharp shrinkage (1997–2015), and expansion (2015–2019); (2) The shrinkage of the Hongjiannao Lake is mainly caused by anthropogenic factors, followed by natural factors. The anthropogenic factors mainly consist of the upstream river construction reservoirs, industrial development of water and vegetation growth increased water demand; (3) For the first time, the preliminary results of the conservation measures implemented since 2012 were analyzed. This is mainly reflected in the positive growth of the Hongjiannao Lake for the first time since its long-term shrinkage in 1997. This phenomenon is mainly due to measures such as artificially increasing rainfall and ecological water replenishment. Climate change (high evaporation, low precipitation) and human activities (construction of upstream water conservancy projects, coal mining, lagoon road construction, irrigation water use, etc.) are the key factors contributing to the changes in lake area during the shrinkage period. It is suggested that the study area should carry out artificial rain enhancement and surface ecological water replenishment to scientifically and rationally utilize water resources in the basin, effectively suppress the shrinkage of Hongjiannao Lake, and achieve long-term sustainable restoration of the wetland ecology.

Application of Landsat Time-Series Data in Island Ecological Environment Monitoring: A Case Study of Zhoushan Islands, China

Chen, C.; He, X.; Lu, Y., and Chu, Y., 2020. Application of Landsat time-series data in island ecological environment monitoring: A case study of Zhoushan Islands, China. In: Yang, D.F. and Wang, H. (eds.), Recent Advances in Marine Geology and Environmental Oceanography. Journal of Coastal Research, Special Issue No. 108, pp. 193–199. Coconut Creek (Florida), ISSN 0749-0208.An ecological environment is a complex system fundamental to the sustainable development of societies and economies. Landsat series satellites are widely used for monitoring the changes in Earth's surface morphology. With long time-series data, Landsat can provide new methods and technical support for eco-environmental monitoring. This study developed remote-sensing monitoring methods based on Landsat data to observe the changes in the ecological environment in the Zhoushan region, China. The regional environmental changes in vegetation, water, and other key ecological factors were analyzed using multitemporal remote-sensing data. This study shows the effectiveness of this method for the extraction of key eco-environmental factors such as vegetation and water body area and the evaluation of regional eco-environmental quality. This study also reveals ecological patterns in the Zhoushan region and provides data support for regional eco-environmental assessment and management.

Land Cover Change Dynamics and their Impacts on Thermal Environment of Dadri Block, Gautam Budh Nagar, India

Abstract Land use / land cover (LULC) has been considered as one of the important bio-physical parameters and have significant affect on local environmental change, particularly increasing anthropogenic temperature. Remote sensing images from Landsat series satellites are a major information source for LULC change analysis. In the present investigation, long term changes in LULC and its negative impact on land surface temperature (LST) were analyzed using multi-temporal Landsat satellite images between 2000 to 2016. firstly LULC of the study area has been classified and temporal changes in land use classes were quantify, and observed that in most of the land use classes such as vegetation (-1.28 %), water bodies (-1.65 %), agriculture (-3.52) and open land (-2.43 %) have shown negative change, however large scale positive changes in built-up area (+8.87 %) has been observed during the analysis, which is mainly due to continuous urbanization and growth of population in the area. The classified thermal images from the same period also show mean temperature of the area has increased by 1.60 °C since last 16 years. The observation from the present study reveals that due to the large-scale land use change practices in urban and peri-urban area witnessed for the rising temperature due to loss natural vegetation and other natural resources.

Assessment of Landsat atmospheric correction methods for water color applications using global AERONET-OC data

With the longest archive of satellite remote sensing images, the Landsat series of satellites have demonstrated their great potential in aquatic environmental studies. However, although various atmospheric correction (AC) methods have been developed for Landsat observations in water color applications, a comprehensive assessment of their accuracies across different AC methods and instruments has yet to be performed. Using in situ spectral data collected by Aerosol Robotic Network-Ocean Color (AERONET-OC) sites, the performances of five types of AC methods over three different Landsat missions (i.e., Landsat 5/7/8) were evaluated. The Landsat 8 Operational Land Imager (OLI) showed more accurate AC retrievals than the other two instruments, and the results for its green and red bands appeared more reliable than those for the other wavelengths (uncertainty levels of ∼30 %). The iterative NIR algorithm with 2-bands (NIR-SWIR2) model selection embedded in SeaDAS showed the best performances for OLI in two blue bands. Moreover, larger residual errors were found for most Landsat 5/7 bands regardless of the AC methods and spectral bands employed with an uncertainty of >50 %. Interestingly, a simple aerosol subtraction method over the Rayleigh-corrected reflectance (Rrc) outperformed the exponential extrapolation (EXP) algorithms, especially for Landsat 5/7. Neither the image-based AC algorithm nor the surface reflectance (SR) products provided by the United States Geological Survey (USGS) showed acceptable performances over coastal environments. The uncertainties in the various Landsat reflectance products over water surfaces could be associated with a relatively poor signal-to-noise ratio (SNR) in addition to radiometric calibration uncertainties, imperfect aerosol removal methods. Future research is required to collect in situ data across a wider range of water optical properties (particularly more turbid inland waters) to examine the corresponding applicability of Landsat-series observations.

Estimasi Perubahan Kualitas Udara Berbasis Citra Satelit Penginderaan Jauh Di Sekitar PLTU Cirebon

Steam power plant construction and operation is an effort to meet electricity needs. In Indonesia, two steam power plants were built and changed the landscape in Cirebon. The presence of Cirebon steam power plants has disturbed the community and potential to decrease air quality. This study aims to estimate air quality changes around the power plants based on remote sensing satellite imageries. The main data in this study obtained from Landsat-8 OLI (2019) and Landsat-7 ETM (2004) satellite imageries were processed with four parameters of air quality algorithm namely PM10, CO, SO2, and NOx on AOI with ranging of 2000 m from the source point. Validation uses comparative data from MODIS and Sentinel-2 MSS satellite imageries in the same period. Changes analysis in air quality used the Mann-Whitney method (U-Test). This research shows that the Landsat series satellite imagery is suitable to be used as the main data for estimating air quality because it has a similar pattern to comparable data. The Cirebon PLTU operation caused a significant increase in CO levels of 1.25 mg/l on a wide range. In other air quality parameters such as PM10, SO2 and NOx were decreased.

Detecting Long Time Changes in Benthic Macroalgal Cover Using Landsat Image Archive

Coastal macroalgae worldwide provide multiple ecological functions and support vital ecosystem services. Thereby, it is important to monitor changes in the extent of benthic macroalgal cover. However, as in situ sampling is costly and time-consuming, areal estimates of macroalgal species cover are often based only on a limited number of samples. This low sampling effort likely yields very biased estimates, as macroalgal communities are often characterized by large spatial variability at multiple spatial scales. Moreover, ecological time series are often short-term, making it impossible to assess changes in algal communities over decades and relate this to different human pressures and/or climate change. The Landsat series satellites have operated for 40 years. In the current study, we tested if the Landsat sensors could be used for mapping the cover of shallow water benthic macroalgae. This study was carried out at two sites in the West Estonian Archipelago, in the northeastern Baltic Sea. Our results show that the Landsat imagery accurately reflected both spatial and temporal variability in benthic algal cover. To conclude, the current methodology can be used to improve the existing assessments of areal macroalgal cover, or to estimate the cover values, in areas and times lacking ecological observations.

Quantitative assessment of landscape transformation due to coal mining activity using earth observation satellite data in Jharsuguda coal mining region, Odisha, India

Opencast (coal) mining activities significantly affect the society and environment in several aspects, including land-useland-cover (LULC) alteration. The present study aims to quantify the alteration in LULC patterns in every 4 year from 2006 to 2018 in the Jharsuguda coal mining region in Odisha, India. The study has used the multitemporal Landsat series satellite data for LULC classification. A support vector machine algorithm was designed for LULC classifications into five broad classes, viz. water body, mining area, forest/vegetation area, bare land, and built-up area. The key findings of the study indicated that the coverage of mining area was gradually increased from 2006 to 2018 with an annual change rate of + 0.03%. On the other hand, a significant loss in the forest cover/vegetation was observed with the annual change rate of − 0.04% from 2006 to 2018. The remarkable increment in the coverage of bare land area was also noted during the study period. The mining activity has posed a serious threat to the forest resources over the study region. Hence, a proper management policy for mine reclamation should be practised over the Jharsuguda coal mining region to protect the forest, environment, and society.

Changes in Water Surface Area of the Lake in the Steppe Region of Mongolia: A Case Study of Ugii Nuur Lake, Central Mongolia

The Ugii Nuur Lake is not only one of the small hydrologically closed lakes located in the Orkhon River Basin in Central Mongolia but also the most vulnerable area for global climate change. Therefore, this study aims to investigate the impacts of recent global climate change on the water surface area. The data we analyzed were various measured hydro-meteorological variables of the lake basin and the lake surface area, which was estimated from Landsat series satellite data from 1986 to 2018. The methods we used were Mann-Kendall (MK), Innovative trend analysis method (ITAM), Sen’s slope estimator test, correlation, and regression analysis. The variation of lake water surface area has a strong positive correlation with the change of the lake water level (r = 0.95). The Mann-Kendall trend analysis has indicated that under a significant decrease in total annual precipitation ( Z = −0.902) and inflow river discharge ( Z = −5.392) and a considerable increase in total annual evaporation ( Z = 4.385) and annual average air temperature ( Z = 4.595), the surface area of the Ugii Nuur Lake has decreased sharply ( Z = −6.021). The total annual evaporation (r = −0.64) and inflow river discharge (r = 0.67) were the essential hydro-meteorological factors affecting the surface area of the Ugii Nuur Lake. The lake surface area decreased by 13.5% in 2018 compared with 1986. In the near future, it is vital to conduct scientific studies considering the volume of lake water, groundwater, and the anthropogenic impact.

Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series

Land Surface Temperature (LST) is increasingly important for various studies assessing land surface conditions, e.g., studies of urban climate, evapotranspiration, and vegetation stress. The Landsat series of satellites have the potential to provide LST estimates at a high spatial resolution, which is particularly appropriate for local or small-scale studies. Numerous studies have proposed LST retrieval algorithms for the Landsat series, and some datasets are available online. However, those datasets generally require the users to be able to handle large volumes of data. Google Earth Engine (GEE) is an online platform created to allow remote sensing users to easily perform big data analyses without increasing the demand for local computing resources. However, high spatial resolution LST datasets are currently not available in GEE. Here we provide a code repository that allows computing LSTs from Landsat 4, 5, 7, and 8 within GEE. The code may be used freely by users for computing Landsat LST as part of any analysis within GEE.

A Scaling-Based Method for the Rapid Retrieval of FPAR From Fine-Resolution Satellite Data in the Remote-Sensing Trend-Surface Framework

Accurate estimation of the fine-resolution fraction of absorbed photosynthetically active radiation (FPAR) across broad spatial extents and long time periods requires efficient and applicable methods. The existing methods can hardly provide a balance between accuracy, simplicity, and transferability through space and time. Within the remote-sensing trend-surface conceptual framework, this article proposes a scaling-based method to efficiently retrieve FPAR from fine-resolution satellite data using coarse-resolution FPAR products as a reference. The method was particularly developed and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) FPAR product and Landsat imagery. First, necessary prior knowledge related to FPAR retrieval and scaling theories was used to explicitly linearize the complex relationship between MODIS FPAR and Landsat surface reflectance. Second, the explicit linear model for FPAR estimation was trained through one-pair image learning for each date to estimate FPAR from Landsat imagery in real time. Both homogeneous and heterogeneous cases were considered. The method was validated at ten selected worldwide sites from the Validation of Land European Remote Sensing Instruments (VALERI) program and derived an overall root mean squared error (RMSE) of 0.133. A long time series of FPAR data set at the 30-m resolution was generated at the regional scale (approximately 2000 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) for 13 years (2000–2012). The results were accurate (RMSE = 0.072) and MODIS-consistent, which were significantly better than those of the normalized difference vegetation index (NDVI) downscaling-based and regression tree methods. The scaling-based method provides accurate, MODIS-consistent and spatially consistent FPAR estimates in real time, is highly transferrable through space and time, and allows for future extension of FPAR estimates to the era of the Landsat series satellites.

Applying the NDVI from satellite images in delimiting management zones for annual crops

The utilization of Normalized Difference Vegetation Index (NDVI) data obtained through satellite images can technically improve the process of delimiting management zones (MZ) for annual crops, resulting in socio-economic and environmental benefits. The aim of this study was to compare delimited MZ, using crop productivity data, with delimited MZ using the NDVI obtained from satellite images in areas under a no-tillage system. The study was carried out in three areas located in the state of Rio Grande do Sul, Brazil. Three crop productivity maps, from 2009 to 2015, were used for each area, whereby the NDVI was calculated for each crop productivity map using images from the Landsat series of satellites. Descriptive and geostatistical analysis were conducted to determine the productivity and NDVI data. The MZ were then delimited using the fuzzy c-means algorithm. Spearman’s correlation matrix was used to compare the methodologies used for delimiting the MZ. The MZ based on NDVI calculated from the satellite images correlated with the MZ based on crop productivity data (0.48 < r< 0.61), suggesting that the NDVI can replace or be complementary to productivity data in delimiting MZ for annual cropping systems.

Анализ климатогенной динамики Батагайского термоденудационного «кратера» с использованием данных дистанционного зондирования

The article discusses the contribution of the climatogenic factor to the development of the Batagai thermo-denudation “crater” — the largest object in the world of thermo-denudation origin. The Batagai “crater” is the result of accelerated activation of exodynamic processes in the continuous permafrost zone of the Yansky Plateau (Northern Yakutia), located in the coldest region of Russia. According to satellite imagery from Landsat series satellites (Landsat-7 and Ladsat-8) for the period from 1999 to 2017. The rate of its growth was established taking into account the exposure factor for certain time intervals. Based on the selected satellite images, they were visually decrypted to highlight the “crater” edge, with reference to each year for the entire 18-year period. The results obtained were compared with climatic indicators for the corresponding time intervals. From climate data, those were analyzed that had positive dynamics and favored the development of thermo-denudation processes. So, for the Batagai “crater” area over the past 50 years, there has been a stable tendency towards an increase in temperature values (average summer by 1,6°C and average annual by 2,2°C) and the amount of precipitation (an increase in the annual amount of 25 mm due to summer autumn period). Over the past two decades, a certain correlation has been revealed between the growth rate of the Batagai “crater” and the dynamics of the main climatic indicators. During the analyzed period, it was established that the northwestern side of the “crater” is distinguished by the highest rate of degradation. Given the statistically revealed relationship, as well as the high ice content of the rocks (40–60 %) and their special sand vein structure, this allows us to consider the Batagai “crater” — a vivid indicator of climate warming.

An Efficient Framework for Producing Landsat-Based Land Surface Temperature Data Using Google Earth Engine

A long time-series land surface temperature (LST) product is useful for ecological and environmental studies. However, current LST products cannot provide a global coverage at a fine spatial resolution (∼100 m) over a long period (>30 years). Landsat series satellites that have been launched since 1972 provide a unique opportunity to fill the gap. Here, we proposed a single-channel framework for producing global long time-series Landsat LST retrievals on a Google earth engine (GEE) cloud computing platform. This framework unifies the LST, land surface emissivity (LSE) and atmospheric water vapor (AWV) estimation algorithms, as well as the emissivity and atmospheric input data for the Landsat LST retrievals from the entire Landsat thermal infrared image archive. In situ LST measurements and the MODIS LST products were employed to evaluate Landsat LST retrievals using the proposed framework over land and water surfaces, respectively. In total, 1317 clear-sky LST samples were collected from the Landsat 5–8 series after spatiotemporal registration with seven sites, and the average bias and root-mean-square error (RMSE) were 0.33 and 2.01 K, respectively. Intercomparison between Landsat and MODIS LST retrievals based on 100 clear-sky scenes over 12 inland lakes showed an average bias of 0.17 K and RMSE of 1.11 K. We conclude that the proposed single-channel framework can produce Landsat LST with high accuracy following a simple yet robust way. Implementation of the single-channel method on GEE shows promise in providing the community with freely accessible and global long time-series (>30 years) LST data.

Comparative Analysis of Different Methods for Extracting Water Body Area of Miyun Reservoir and Driving Forces for Nearly 40 Years

The Miyun Reservoir is the most important source of drinking water in Beijing, China. Based on the Landsat satellite series image, this paper compared four index methods (NDWI, RNDWI, MNDWI, AWEIsh-superimposed MNDWI) to extract the water body area of Miyun Reservoir from 1979 to 2018 to study its changes in the past 40 years. Furthermore, the internal and external driving forces of reservoir water body area change are studied by correlation analysis and catastrophe analysis. Compared to the characteristic curve of reservoir area calculation, it indicated that the AWEIsh-superimposed MNDWI method has the highest accuracy. The statistical, temporal and spatial analysis showed that the water body area changes of Miyun Reservoir are mainly divided into four distinct stages: (1) It increased in 1979–1997, which was mainly due to the increase in precipitation and the low level of economic development. In 1997, the maximum value (161.14 km2) was reached, which was 108.64 km2 higher than the minimum value. (2) It shrank rapidly in 1997–2004. The areas had dropped by 101.61 km2. By 2004, it was 59.61 km2. The main reason is that the reservoir has suffered from persistent drought for many years after discharging water. (3) It maintained at a lower water level in 2004–2015, with an average annual area of 78 km2. The reason is the low precipitation and rapid economic development. (4) It grew rapidly after 2015, mainly due to water from the South-to-North Water Transfer Project, coupled with the increased precipitation. By 2018, the water body area reached 125.42 km2.

Cartografia do Invisível: Revelando a Agricultura de Pequena Escala com Imagens Rapideye na Região do Baixo Tocantins, Pa.

Dados derivados de sensoriamento remoto aliados ao uso de técnicas de classificação digital de imagens fornecem uma visão sinóptica e informações sobre a dimensão temporal dos fenômenos espaciais, possibilitando gerar informações sobre a dinâmica e os padrões espaciais da paisagem em áreas de grandes extensões territoriais como a Amazônia. Nos mapeamentos extensivos, como os realizados com imagens de satélite pelo INPE para a Amazônia Legal, é comum a definição e descrição de classes de uso e cobertura da terra em função da resolução espacial e radiométrica dos sensores utilizados. Por essa razão, pequenas áreas que não atingem o tamanho mínimo da área de mapeamento e/ou com usos diversificados são, em geral, agregadas em uma única classe. Esse é o exemplo da classe denominada Mosaico de Ocupação presente na legenda do sistema de monitoramento do uso e cobertura da terra da Amazônia-TerraClass. Esta classe procura representar, em parte, a agricultura familiar, no entanto, como o mapeamento é feito pelo sensor TM ou OLI dos satélites da série Landsat, com resolução espacial de 30m e como o TerraClass define uma área mínima de mapeamento de 6,25 hectares (ha), a identificação de pequenas áreas agrícolas é comprometida, pois essas categorias são agregadas em classes mistas e não são mais distinguíveis. Para estudos que procuram dar visibilidade aos padrões espaciais de atividades de produção de pequena escala torna-se necessário realizar o refinamento dessas classes com dados de resolução espacial com melhor definição. Neste contexto, o objetivo desse trabalho é testar três algoritmos semiautomáticos de classificação, baseados em pixel e em regiões: os algoritmos MAXVER, Bhattacharya e K-Vizinho Mais Próximo (KNN), para avaliar a capacidade de refinamento da classe Mosaico de Ocupação do dado produzido pelo TerraClass-2014. A área de estudo compreende parte dos municípios de Cametá, Mocajuba e Baião, localizados na região Nordeste do Pará, onde a produção de mandioca, pimenta-do-reino, cacau e açaí têm grande importância econômica para população local. Para o mapeamento das categorias contidas na classe Mosaico de Ocupação, foram utilizadas imagens do RapidEye, sensor REIS, ortoimagens com 5m de resolução espacial. Foi estimada a exatidão global dos algoritmos testados obtendo-se índices de 26%, 38% e 78%, para os algoritmos MAXVER, Bhattacharya e K-Vizinho Mais Próximo, respectivamente. Além da maior exatidão Global (78%), o algoritmo K-Vizinho Mais Próximo apresentou melhores resultados relativo às classes de vegetação secundária, hidrografia, e pasto sujo, com mais de 90% de acerto. A classe agricultura anual de pequena escala apresentou acerto de 62%, enquanto o índice de acerto dos outros dois algoritmos testados não passou de 8%. A abordagem metodológica desenvolvida demonstrou a viabilidade do uso das imagens de alta resolução espacial e de métodos semiautomáticos para a classificação de classes de uso e cobertura da terra associadas à classe Mosaico de Ocupação do TerraClass. A metodologia pode ser utilizada para complementar às atuais bases de dados existentes para a Amazônia (TerraClass, MapBiomas e IBGE), explicitando as categorias agrícolas de produção em pequena escala, dando visibilidade a esses sistemas de produção, frequentemente negligenciados nos mapeamentos que abrangem grandes extensões territoriais.

Cyanobacteria Bloom in the Azov Sea According to Landsat Data

In this paper we studied the spatial and temporal characteristics of cyanobacteria bloom in the Azov Sea based on data from Landsat satellites sensors for 1999–2016. The strongest blooms were observed in 1999 and 2006–2009, when cyanobacteria occupied the entire central part of the Azov Sea. The maximal blooming in these years was in August–September. In 2006, cyanobacteria penetrated from the Azov Sea into the Black Sea through the Kerch Strait. The longest and most extensive cyanobacteria blooming is recorded in the Taganrog Bay, which is associated with its shallow water and the nutrient supply from the Don River. Here, blooms are observed from February to November. Local maximums of cyanobacteria accumulation are observed along the eastern coast of the Azov Sea, near the mouth of the Kuban and Protoka rivers, in the Akhtarsky estuary and in the Temryuk Bay. They are most rarely found near the northwest coast. The data from the Landsat series satellites sensors show that cyanobacteria spread from the Taganrog Bay to the south or west in summer. From the mouth of the Kuban River, cyanobacteria move north. An analysis of numerical calculations using the Nemo model showed that this distribution is associated with northern currents that occur in the southern part of the sea in summer due to the impact of river discharge. Maps of the probability distribution of the cyanobacteria observation were obtained and estimates of the seasonal variation of the area covered by cyanobacteria blooming were given according to the data from the Landsat series satellites sensors. In the Azov Sea, maximal blooming is observed in July–August and minimal blooming is observed in winter, from November to February.

Performance Evaluation and Comparison of Modified Spectral Mixture Analysis Method for Different Images of Landsat Series Satellites

Urban impervious surface is considered one of main factors affecting urban heat island and urban waterlogging. It is commonly extracted utilizing the original linear spectral mixture analysis (LSMA) model. However, due to the deficiencies of this method, many improvements and modifications have been proposed. In this paper, a modified dynamic endmember linear spectral mixture analysis (DELSMA) model was introduced and tested in Zhengzhou, China, using different images of Landsat series satellites. The accuracy and performance of DELSMA model was evaluated in terms of R M S E , r and R 2 . Results show that (1) the DELSMA model performed equally well for Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced Thematic Mapper (ETM+) images, and obtained better accuracy by using Landsat-8 Operational Land Imager (OLI) than Landsat TM/ETM+; (2) the DELSMA model achieved a better performance than the original LSMA model consistently, using images of Landsat from different sensors. Based exclusively on the overall accuracy reports, the DELSMA model proved to be a more efficient method for extracting impervious surface. Our study will provide a reliable method of impervious surface estimation for the urban planner and management in monitoring urban expansion, revealing urban heat island, and estimating urban surface runoff, using time-series Landsat imagery.