Medium Spatial Resolution Research Articles

Monitoring changes in land use and distribution of mangroves in the southeastern part of the Mekong River Delta, Vietnam

Mangroves are one of the most valuable and productive coastal ecosystems. Previous studies show a severe loss of mangroves around the world over the last several decades due to natural and anthropogenic activities. Mangroves located in the southeastern part of the Mekong River Delta (MRD) are also affected by these activities. Shrimp farming is considered as one of the main drivers causing the rapid loss and degradation of mangroves. The goal of this study is to assess the spatiotemporal changes in land use and distribution of mangroves in the Soc Trang and Bac Lieu provinces of the southeastern part of the MRD. Multi-temporal Landsat data were used for land use and land cover (LULC) classification using the maximum-likelihood classification algorithm. The changes in mangrove forest areas were monitored using medium spatial resolution (Landsat-5 Thematic Mapper and Landsat-8 Operational Land Imager) satellite imageries from 1988 to 2018. In the study area, there were seven major LULC types namely, dense mangroves, sparse mangroves, aquaculture farms, arable land with crop cover, arable land without crop cover, settlements, and water bodies. The overall accuracies of the LULC maps in 1988, 1998, 2008, and 2018 were 81.2%, 83.3%, 78.3%, and 81.9%, respectively. This study reveals that dense and sparse mangrove forests have decreased by 90% from 5495 hectares (ha) to 515 ha and by 55% from 14,105 to 6289 ha, respectively from 1988 to 2018. On the other hand, the aquaculture farm has increased at the rate of 5024 ha/year for a period of 30 years. This rapid growth of aquaculture farming activities caused the rapid loss and degradation of mangroves in the MRD. Quantitative information about mangrove change obtained by this study is considered to be useful for future coastal management and relevant policies in the MRD.

Automated detection of deforestation based on multi-spectrum satellite data

A brief description and the results of experimental testing of a technique for automated processing and analyzing multispectral satellite images of medium and high spatial resolution with the aim of detecting and assessing the dynamics of large logging areas in the territory of the Republic of Kazakhstan are presented. More than 50 large logging areas of about 100 hectares were detected in the selected monitoring site (district of Kokshetau, Akmola territory).

Comparison of multi-seasonal Landsat 8, Sentinel-2 and hyperspectral images for mapping forest alliances in Northern California

The current era of earth observation now provides constellations of open-access, multispectral satellite imagery with medium spatial resolution, greatly increasing the frequency of cloud-free data for analysis. The Landsat satellites have a long historical record, while the newer Sentinel-2 (S2) satellites offer higher temporal, spatial and spectral resolution. The goal of this study was to evaluate the relative benefits of single- and multi-seasonal multispectral satellite data for discriminating detailed forest alliances, as defined by the U.S. National Vegetation Classification system, in a Mediterranean-climate landscape (Sonoma County, California). Results were compared to a companion analysis of simulated hyperspectral satellite data (HyspIRI) for the same study site and reference data (Clark et al., 2018). Experiments used real and simulated S2 and Landsat 8 (L8) data. Simulated S2 and L8 were from HyspIRI images, thereby focusing results on differences in spectral resolution rather than other confounding factors. The Support Vector Machine (SVM) classifier was used in a hierarchical classification of land-cover (Level 1), followed by alliances (Level 2) in forest pixels, and included summer-only and multi-seasonal sets of predictor variables (bands, indices and bands plus indices). Both real and simulated multi-seasonal multispectral variables significantly improved overall accuracy (OA) by 0.2–1.6% for Level 1 tree/no tree classifications and 3.6–25.8% for Level 2 forest alliances. Classifiers with S2 variables tended to be more accurate than L8 variables, particularly for S2, which had 0.4–2.1% and 5.1–11.8% significantly higher OA than L8 for Level 1 tree/no tree and Level 2 forest alliances, respectively. Combining multispectral bands and indices or using just bands was generally more accurate than relying on just indices for classification. Simulated HyspIRI variables from past research had significantly greater accuracy than real L8 and S2 variables, with an average OA increase of 8.2–12.6%. A final alliance-level map used for a deeper analysis used simulated multi-seasonal S2 bands and indices, which had an overall accuracy of 74.3% (Kappa = 0.70). The accuracy of this classification was only 1.6% significantly lower than the best HyspIRI-based classification, which used multi-seasonal metrics (Clark et al., 2018), and there were alliances where the S2-based classifier was more accurate. Within the context of these analyses and study area, S2 spectral-temporal data demonstrated a strong capability for mapping global forest alliances, or similar detailed floristic associations, at medium spatial resolutions (10–30 m).

Assessment of the Impacts of Image Signal-to-Noise Ratios in Impervious Surface Mapping

Medium spatial resolution satellite images are frequently used to characterize thematic land cover and a continuous field at both regional and global scales. However, high spatial resolution remote sensing data can provide details in landscape structures, especially in the urban environment. With upgrades to spatial resolution and spectral coverage for many satellite sensors, the impact of the signal-to-noise ratio (SNR) in characterizing a landscape with highly heterogeneous features at the sub-pixel level is still uncertain. This study used WorldView-3 (WV3) images as a basis to evaluate the impacts of SNR on mapping a fractional developed impervious surface area (ISA). The point spread function (PSF) from the Landsat 8 Operational Land Imager (OLI) was used to resample the WV3 images to three different resolutions: 10 m, 20 m, and 30 m. Noise was then added to the resampled WV3 images to simulate different fractional levels of OLI SNRs. Furthermore, regression tree algorithms were incorporated into these images to estimate the ISA at different spatial scales. The study results showed that the total areal estimate could be improved by about 1% and 0.4% at 10-m spatial resolutions in our two study areas when the SNR changes from half to twice that of the Landsat OLI SNR level. Such improvement is more obvious in the high imperviousness ranges. The root-mean-square-error of ISA estimates using images that have twice and two-thirds the SNRs of OLI varied consistently from high to low when spatial resolutions changed from 10 m to 20 m. The increase of SNR, however, did not improve the overall performance of ISA estimates at 30 m.

Detailed geological mapping of the Olon-Ovoot gold-ore cluster (South Mongolia) based on the interpretation of satellite imagery of medium and high spatial resolution.

Detailed geological mapping of Olon-Ovoot gold-ore cluster (South Mongolia) on the basis of interpretation of satellite imagery of medium and high spatial resolution The article presents the results of geological interpretation the territory of the Olon-Ovoot ore cluster by space imagery of medium and high spatial resolution. A Sentinel-2 imagery, chosen for interpretation, was orthorectified and reduced to a common spatial resolution (10m) The iron-hydroxid and ferrous-silicates indices in Sentinel-2 imagery were used to detect the perspective gold-bearing objects. The sub-pixel structure of the imagery Sentinel-2 were analyzed by means of satellite imagery of high spatial resolution by Google Earth for detecting areas concentration of the quartz-carbonate veins. The study of the spectral domain in high-resolution imagery not necessary for detecting lineaments by structural and morphological interpretation. The interpretation of the remote sensing data provide a unique opportunity to substantial specify the geological structure of the territory and change the level of mapping from the scale of 1 : 200 000 to the scale of 1 : 20 000 for the perspective areas. The integration of satellite images of different functional scale provided an tenfold increase for some geological objects (for example dikes). Detailed mapping of the territory allowed to come for geoinformation modeling of geological structural elements and predictive indicators.

Natural and revitalized grassy ecosystems as biodiversity refuges: on the abilities of remote sensing for their detection and study

The natural and revitalized on the place of former fields grassy ecosystems can be the refuges (places of conservation in natural conditions) for many types of rare plant species. Such ecosystems are of great interest for the environment exploration and management activities planning over many countries including EU and USA. Samara region territory demonstrates the residual nature of grassy ecosystems as a result of intensive agricultural exploration of virgin grasslands, located here at the past. Consolidative usage of remote sensing data and ground-based surveys is the common practice nowadays. This paper describes the possibilities of high-nature value grasslands classification in Samara Region using intra-annual time series of multispectral remote sensing data of medium spatial resolution with the use of spotted ground-based surveys. Investigations engage the training set generation for the large natural vegetation communities with ground-based survey optimization and include the classification results for two vast grassland objects with protected status.

Atmospheric correction algorithm over coastal and inland waters based on the red and NIR bands: application to Landsat-8/OLI and VNREDSat-1/NAOMI observations.

Water pixel extraction and correction of the atmospheric signal represent prerequisite steps prior to applying algorithms dedicated to the assessment of water quality of natural surface water bodies. The recent multiplication of medium spatial resolution sensors (10-60 m) provides the required constellation to monitoring bio-optical and biogeochemical parameters of surface waters at the relevant spatial-temporal scales. Here we present a new approach to identify water pixels and to extract the atmospheric contribution to the top of atmosphere signal measured by the NAOMI sensor on board the first Vietnamese satellite, VNREDSat-1. After verifying the TOA calibration of NAOMI through a vicarious calibration exercise, we adapt a recent water pixel extraction algorithm (WiPE) to NAOMI, and develop a new atmospheric correction algorithm (referred to as red-NIR) based on the use of the red and NIR bands (the only bands available for that purpose on NAOMI) and spectral relationships. The evaluation of red-NIR with a match-up data set gathering remote sensing reflectance, Rrs, measurements performed at the AERONET-OC stations in moderately turbid waters indicates excellent performance in the blue and green part of the spectrum (similar to the performances reached by the SeaDAS NIR-SWIR algorithms) and lower accuracy in the red. Intercomparison of simultaneous images collected by NAOMI and OLI over a more turbid water body shows an excellent agreement between the NAOMI-Rrs estimated by the present processing, and the OLI-Rrs estimated from the ACOLITE algorithm. This approach will allow sensors that do not have SWIR bands, such as SPOT-6 and -7, to be processed, making their data exploitation available for long-term temporal analyses.

Identifying and mapping very small (<0.5 km2) mountain glaciers on coarse to high-resolution imagery

AbstractSmall mountain glaciers are an important part of the cryosphere and tend to respond rapidly to climate warming. Historically, mapping very small glaciers (generally considered to be <0.5 km2) using satellite imagery has often been subjective due to the difficulty in differentiating them from perennial snowpatches. For this reason, most scientists implement minimum size-thresholds (typically 0.01–0.05 km2). Here, we compare the ability of different remote-sensing approaches to identify and map very small glaciers on imagery of varying spatial resolutions (30–0.25 m) and investigate how operator subjectivity influences the results. Based on this analysis, we support the use of a minimum size-threshold of 0.01 km2for imagery with coarse to medium spatial resolution (30–10 m). However, when mapping on high-resolution imagery (<1 m) with minimal seasonal snow cover, glaciers <0.05 km2and even <0.01 km2are readily identifiable and using a minimum threshold may be inappropriate. For these cases, we develop a set of criteria to enable the identification of very small glaciers and classify them ascertain,probableorpossible.This should facilitate a more consistent approach to identifying and mapping very small glaciers on high-resolution imagery, helping to produce more comprehensive and accurate glacier inventories.

Hyperspectral remote sensing monitoring of cyanobacteria blooms in a large South American reservoir: high- and medium-spatial resolution satellite algorithm simulation

We used hyperspectral remote sensing with the aim of establishing a monitoring program for cyanobacteria in a South American reservoir. We sampled at a wide temporal (2012–16; 10 seasons) and spatial (30km) gradient, and retrieved 111 field hyperspectral signatures, chlorophyll-a, cyanobacteria densities and total suspended solids. The hyperspectral signatures for cyanobacteria-dominated situations (n=75) were used to select the most suitable spectral bands in seven high- and medium-spatial resolution satellites (Sentinel 2, Landsat 5, 7 and 8, SPOT-4/5 and -6/7, WorldView 2), and for the development of chlorophyll and cyanobacteria cell abundance algorithms (λ550 – λ650+λ800) ÷ (λ550+λ650+λ800). The best-performing chlorophyll algorithm was Sentinel 2 ((λ560 – λ660+λ703) ÷ (λ560+λ660+λ703); R2=0.80), followed by WorldView 2 ((λ550 – λ660+λ720) ÷ (λ550+λ660+λ720); R2=0.78), Landsat and the SPOT series ((λ550 – λ650+λ800) ÷ (λ550+λ650+λ800); R2=0.67–0.74). When these models were run for cyanobacteria abundance, the coefficient of determination remained similar, but the root mean square error increased. This could affect the estimate of cyanobacteria cell abundance by ~20%, yet it still enable assessment of the alert level categories for risk assessment. The results of this study highlight the importance of the red and near-infrared region for identifying cyanobacteria in hypereutrophic waters, demonstrating coherence with field cyanobacteria abundance and enabling assessment of bloom distribution in this ecosystem.

Mapping impervious surface fractions using automated Fisher transformed unmixing

Spatial explicit monitoring of impervious surface fractions provides essential information for urban planning, urban disaster prevention and mitigation. Moderate spectral and spatial resolution satellite observation has become a tool to perform such monitoring across different spatial and temporal scales. Yet, one major challenge in urban remote sensing is the high spatial heterogeneity causing the presence of image pixels constituting a mixture of different land cover materials. Here we apply an automated unmixing procedure specifically designed to optimally account for the pronounced spectral variability (i.e. high within-class and low between-class variability) prevailing in urban settings. The approach is based on Fisher Discriminant Analysis (FDA), a data dimensionality reduction technique in which between-class variability is maximized and within-class variability is minimized while transforming spectra from the reflectance to the Fisher feature space. We integrated the FDA transformation in the widely-applied Multiple Endmember Spectral Mixture Analysis (MESMA) approach in an attempt to more effectively address the endmember variability problem. We made use of online spectral libraries to train the Fisher transformation parameters and steer the automated selection of image endmembers. Our Fisher transformed MESMA algorithm (F-MESMA) was tested on medium spatial resolution Landsat scenes and impervious fraction maps were generated for five major cities located in different regions of the world. The output of F-MESMA was compared to the output of state-of-the-art spectral mixture analysis approaches which were specifically designed to reduce endmember variability issues. We could demonstrate that compared to other spectral transformation approaches, the ratio of within- vs between-class variability of the endmembers was most strongly reduced after applying the Fisher transformation. As a direct consequence, across all five cities F-MESMA consistently provided the most accurate impervious surface fraction estimates (RMSEF-MESMA = 0.13 vs. RMSEalternative approaches = [0.16–0.17]).

Landsat-8 and Sentinel-2 burned area mapping - A combined sensor multi-temporal change detection approach

The free-availability of global coverage Landsat-8 and Sentinel-2 data provides the opportunity for systematic generation of medium spatial resolution land products. This paper presents a combined Landsat-8 Sentinel-2 burned area mapping algorithm. The data handling integrates recent research on Landsat-8 and Sentinel-2 pre-processing to generate registered, surface nadir BRDF-adjusted reflectance (NBAR) sensor time series that are used as an input. The different sensor data are combined through a random forest change regression, trained with synthetic data built from laboratory and field spectra and using a spectral model of fire effects on reflectance. The random forest regression is applied independently at each gridded 30 m pixel location on a temporally rolling basis considering three months of sensor data to map the central month. Temporal consistency checks are used to reduce commission errors due to non-fire related spectral changes, and a region growing algorithm is used to reduce omission errors due to temporally sparse observations. In the resulting product, each 30 m pixel is labelled as burned, unburned or unmapped. At burned pixels the estimated day of burning, and a single value that provides an estimate of the product of the subpixel fraction burned (f) and the combustion completeness (cc), henceforth termed “f.cc”, and an associated quality measure, are defined. The algorithm is demonstrated using six months of every available Landsat-8 and Sentinel-2A acquisition over 10° × 10° of Southern Africa. Experiments comparing the mapped burned areas considering Sentinel-2A only and both Landsat-8 and Sentinel-2A data indicate a greater area burned and a smaller number of unmapped pixels when both sensors are used. The results are compared with contemporaneous NASA MODIS fire products to gain insights into their temporal and spatial reporting differences. Temporally, the Sentinel-2A and Landsat-8 30 m product reports the day of burning on average three days later than the MODIS 500 m burned area product, because of the lower revisit frequency of the Sentinel-2A and Landsat-8 observations. Spatially, the Sentinel-2A and Landsat-8 30 m product captures more detail than the MODIS 500 m burned area product, with systematically higher burned area estimates. Despite the areal differences, the spatial pattern of the two products is similar, as reflected by the correlation (r2 ~ 0.7) and slope (>0.8) of regressions of the proportions of area burned defined in coarse resolution cells between the two products, taking into account the 3 day temporal bias. A comparison with multi-date PLANET 3 m data, shows high visual agreement in the location and approximate day of burning and higher f.cc values in the interiors of distinct burns than at the edges, and lower values in areas containing mixes of burned and unburned 3 m pixels. The Landsat-8/Sentinel-2A burned area mapping results are validated by comparison with burned areas interpreted visually from two date Sentinel-2A data, over 30 km × 30 km boxes selected by systematic sampling. The results mapped less area burned than the interpreted maps, as reflected by a 0.24 omission error and a negative relative bias (−0.19), with a small 0.06 commission error. Regression between the proportions of area burned defined in 1.5 km cells provide a high correlation (r2 = 0.89) and slope close to unity (0.82) and indicate that the Landsat-8/Sentinel-2A results identified similar burned area spatial patterns as the interpreted maps.

Remote Sensing to Detect Nests of the Leaf-Cutting Ant Atta sexdens (Hymenoptera: Formicidae) in Teak Plantations

Leaf-cutting ants of the genus Atta are an important insect pest in forest plantations in many countries of South America. The objective of this work was to evaluate the potential for using Landsat-8 images, with medium spatial resolution and distributed free of charge, to detect leaf-cutting ant nests in Tectona grandis plantations in Brazil, using partial least squares discriminant analysis (PLS-DA). The regression model adjusted by PLS-DA selected three principal components with a cross-validation error of 0.275 to map and predict the presence of leaf-cutting ant nests in these plantations. The most important bands and vegetation indices were selected using the main variables in the projection (VIP) and predicted pixels with the presence or absence of leaf-cutting ant nests with an accuracy of 72.3% on an independent validation data set. The study indicates that Landsat-8 OLI images have the potential to detect and map leaf-cutting ant nests in T. grandis plantations.

Examining the Deep Belief Network for Subpixel Unmixing with Medium Spatial Resolution Multispectral Imagery in Urban Environments

Mixed pixels in medium spatial resolution imagery create major challenges in acquiring accurate pixel-based land use and land cover information. Deep belief network (DBN), which can provide joint probabilities in land use and land cover classification, may serve as an alternative tool to address this mixed pixel issue. Since DBN performs well in pixel-based classification and object-based identification, examining its performance in subpixel unmixing with medium spatial resolution multispectral image in urban environments would be of value. In this study, (1) we examined DBN’s ability in subpixel unmixing with Landsat imagery, (2) explored the best-fit parameter setting for the DBN model and (3) evaluated its performance by comparing DBN with random forest (RF), support vector machine (SVM) and multiple endmember spectral mixture analysis (MESMA). The results illustrated that (1) DBN performs well in subpixel unmixing with a mean absolute error (MAE) of 0.06 and a root mean square error (RMSE) of 0.0077. (2) A larger sample size (e.g., greater than 3000) can provide stable and high accuracy while two-RBM-layer and 50 batch sizes are the best parameters for DBN in this study. Epoch size and learning rate should be decided by specific applications since there is not a consistent pattern in our experiments. Finally, (3) DBN can provide comparable results compared to RF, SVM and MESMA. We concluded that DBN can be viewed as an alternative method for subpixel unmixing with Landsat imagery and this study provides references for other scholars to use DBN in subpixel unmixing in urban environments.

A dataset of 30 m annual vegetation phenology indicators (1985–2015) in urban areas of the conterminous United States

Abstract. Medium-resolution satellite observations show great potential for characterizing seasonal and annual dynamics of vegetation phenology in urban domains from local to regional and global scales. However, most previous studies were conducted using coarse-resolution data, which are inadequate for characterizing the spatiotemporal dynamics of vegetation phenology in urban domains. In this study, we produced an annual vegetation phenology dataset in urban ecosystems for the conterminous United States (US), using all available Landsat images on the Google Earth Engine (GEE) platform. First, we characterized the long-term mean seasonal pattern of phenology indicators of the start of season (SOS) and the end of season (EOS), using a double logistic model. Then, we identified the annual variability of these two phenology indicators by measuring the difference of dates when the vegetation index in a specific year reaches the same magnitude as its long-term mean. The derived phenology indicators agree well with in situ observations from the PhenoCam network and Harvard Forest. Comparing with results derived from the moderate-resolution imaging spectroradiometer (MODIS) data, our Landsat-derived phenology indicators can provide more spatial details. Also, we found the temporal trends of phenology indicators (e.g., SOS) derived from Landsat and MODIS are consistent overall, but the Landsat-derived results from 1985 offer a longer temporal span compared to MODIS from 2001 to present. In general, there is a spatially explicit pattern of phenology indicators from the north to the south in cities in the conterminous US, with an overall advanced SOS in the past 3 decades. The derived phenology product in the US urban domains at the national level is of great use for urban ecology studies for its medium spatial resolution (30 m) and long temporal span (30 years). The data are available at https://doi.org/10.6084/m9.figshare.7685645.v5.

DETECTION OF SHALLOW WATER AREA WITH MACHINE LEARNING ALGORITHMS

Abstract. Remote sensing techniques has been widely used for detecting water bodies in especially wetlands. Different classification methods and water indices has used for this purpose and there are numerous studies for detecting water bodies. However, detecting shallow water area is difficult comparing with deep water bodies because of the mixed pixels. Akgol Wetland is chosen as study area to detect shallow water. For this purpose, Sentinel 2 satellite image, which gives more accurate results thanks to higher spatial resolution than the images having medium spatial resolution, is used. In this study, two classification approaches were applied on Sentinel 2 image to detect shallow water area. In the first approach, effectiveness of indices was determined and classification of spectral bands with indices shows higher accuracy than classification of only spectral bands by using support vector machine classification method. In the second approach, support vector machine recursive feature elimination method used for the most effective features in the first approach. Besides overall accuracy of only spectral bands is obtained as 88.10%, spectral bands and indices’ accuracy was obtained as 91.84%.

Implementing an object-based multi-index protocol for mapping surface glacier facies from Chandra-Bhaga basin, Himalaya

Surface glacier facies are superficial expressions of a glacier that are distinguishable based on differing spectral and structural characteristics according to their age and inter-mixed impurities. Increasing bodies of literature suggest that the varying properties of surface glacier facies differentially influence the melt of the glacier, thus affecting the mass balance. Incorporating these variations into distributed mass balance modelling can improve the perceived accuracy of these models. However, detecting and subsequently mapping these facies with a high degree of accuracy is a necessary precursor to such complex modelling. The variations in the reflectance spectra of various glacier facies permit multiband imagery to exploit band ratios for their effective extraction. However, coarse and medium spatial resolution multispectral imagery can delimit the efficacy of band ratioing by muddling the minor spatial and spectral variations of a glacier. Very high-resolution imagery, on the other hand, creates distortions in the conventionally obtained information extracted through pixel-based classification. Therefore, robust and adaptable methods coupled with higher resolution data products are necessary to effectively map glacier facies. This study endeavours to identify and isolate glacier facies on two unnamed glaciers in the Chandra-Bhaga basin, Himalayas, using an established object-based multi-index protocol. Exploiting the very high resolution offered by WorldView-2 and its eight spectral bands, this study implements customized spectral index ratios via an object-based environment. Pixel-based supervised classification is also performed using three popular classifiers to comparatively gauge the classification accuracies. The object-based multi-index protocol delivered the highest overall accuracy of 86.67%. The Minimum Distance classifier yielded the lowest overall accuracy of 62.50%, whereas, the Mahalanobis Distance and Maximum Likelihood classifiers yielded overall accuracies of 77.50% and 70.84% respectively. The results outline the superiority of the object-based method for extraction of glacier facies. Forthcoming studies must refine the indices and test their applicability in wide ranging scenarios.

Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring

Continuous monitoring of crop growth status using time-series remote sensing image is essential for crop management and yield prediction. The growing season of summer corn in the North China Plain with the period of rain and hot, which makes the acquisition of cloud-free satellite imagery very difficult. Therefore, we focused on developing image datasets with both a high temporal resolution and medium spatial resolution by harmonizing the time-series of MOD09GA Normalized Difference Vegetation Index (NDVI) images and 30-m-resolution GF-1 WFV images using the improved Kalman filter model. The harmonized images, GF-1 images, and Landsat 8 images were then combined and used to monitor the summer corn growth from 5th June to 6th October, 2014, in three counties of Hebei Province, China, in conjunction with meteorological data and MODIS Evapotranspiration Data Set. The prediction residuals ( Δ P R K ) in NDVI between the GF-1 observations and the harmonized images was in the range of −0.2 to 0.2 with Gauss distribution. Moreover, the obtained phenological curves manifested distinctive growth features for summer corn at field scales. Changes in NDVI over time were more effectively evaluated and represented corn growth trends, when considered in conjunction with meteorological data and MODIS Evapotranspiration Data Set. We observed that the NDVI of summer corn showed a process of first decreasing and then rising in the early growing stage and discuss how the temperature and moisture of the environment changed with the growth stage. The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.

Sequestro de carbono

A Bacia Hidrográfica do Rio Canoinhas (BHRC) ocupa uma superfície de 1.663km², representando cerca de 16% da área do Planalto Norte Catarinense (PNC). Esta região era originalmente coberta por ecossistemas naturais com predomínio da Floresta Ombrófila Mista (FOM), uma das formações do bioma Mata Atlântica. A Mata de Araucárias apresenta elevados níveis de biodiversidade e alta capacidade de produção de biomassa florestal, sendo que sua ampla abrangência resulta em grandes reservatórios de carbono. O objetivo deste estudo foi verificar a distribuição espacial do estoque de carbono na BHRC. Para isto foram gerados mapas de cobertura e uso dos solos da BHRC, utilizando imagens satelitais de média resolução espacial dos anos de 2006, ano de instauração da Lei da Mata Atlântica, e de 2014. A determinação dos estoques de carbono das coberturas foi realizada por meio de revisão de literatura científica. Finalmente, estes materiais foram utilizados para gerar mapas de distribuição espacial de estoque de carbono. Como resultados se verificou que as áreas com vegetação nativa aumentaram 7.290 hectares, representando uma variação de 11%. As superfícies de produção florestal com espécies exóticas aumentaram 950 hectares (8,5%), com diminuição no uso de espécies do gênero Pinus, e aumento de 519% nas áreas com espécies do gênero Eucalyptus. Com base nas informações geradas se pode evidenciar o papel dos dispositivos legais para conservação da vegetação nativa, bem como identificar as principais áreas de armazenamento de carbono na BHRC. O uso de técnicas de sensoriamento remoto se apresenta como uma ferramenta importante para análises espaciais de Serviços Ecossistêmicos (SE).
 Palavras chave: Serviços Ecossistêmicos. Sensoriamento Remoto. Mata de Araucárias.

Automatic extraction of built-up area from ZY3 multi-view satellite imagery: Analysis of 45 global cities

Accurate delineation of global built-up area (BUA) is fundamental to a better understanding of human development and the impacts on global environmental change. Existing global datasets of human settlement were mostly generated at medium and coarse spatial resolutions, including BUA and other impervious surfaces. With multiple high-resolution satellite constellations now available (e.g., ZiYuan-3 (ZY3), SPOT-5/6/7, Cartosat-1/2, and WorldView-2/3), identifying the global BUA explicitly from the complex landscapes becomes possible. In this study, a novel method was proposed for automated extraction of BUA at the global scale, by fusing a series of building features. Specifically, two planar features, the Morphological Building Index (MBI) and Harris corner detector, were employed to characterize the structure and corner attributes of buildings. Moreover, two multi-angular built-up indices (MABIs), i.e., Ratio Multi-angular Built-up Index (RMABI) and Normalized Difference Multi-angular Built-up Index (NDMABI), were proposed to represent the vertical properties of buildings based on multi-view images, which can further complement the planar features. 45 global cities were selected to validate the performance of the proposed method with images acquired by the ZY3 satellite constellation. The results show that the fusion of MBI and Harris corner can achieve satisfactory accuracy, i.e., 91.12%, 88.85%, 82.82% and 0.85, for the average overall accuracy (OA), user's accuracy (UA), producer's accuracy (PA), and F1-score, respectively, for all the test cities. After fusing the MABIs with the planar features, the average OA, UA, PA and F values of the final results were 92.00%, 86.20%, 89.14% and 0.87 for the RMABI, and 91.83%, 85.51%, 89.62% and 0.87 for the NDMABI, respectively. In particular, addition of the MABIs can further reduce the omission errors where medium/high rise buildings with low local contrast exist. We compared our results with two existing state-of-the-art global BUA products, Global Human Settlement Layer (GHSL) and Global Urban Footprint (GUF), which further corroborated the effectiveness of our method.

Comparison of Multi-Resolution Optical Landsat-8, Sentinel-2 and Radar Sentinel-1 Data for Automatic Lineament Extraction: A Case Study of Alichur Area, SE Pamir

Lineament mapping, which is an important part of any structural geological investigation, is made more efficient and easier by the availability of optical as well as radar remote sensing data, such as Landsat and Sentinel with medium and high spatial resolutions. However, the results from these multi-resolution data vary due to their difference in spatial resolution and sensitivity to soil occupation. The accuracy and quality of extracted lineaments depend strongly on the spatial resolution of the imagery. Therefore, the aim of this study was to compare the optical Landsat-8, Sentinel-2A, and radar Sentinel-1A satellite data for automatic lineament extraction. The framework of automatic approach includes defining the optimal parameters for automatic lineament extraction with a combination of edge detection and line-linking algorithms and determining suitable bands from optical data suited for lineament mapping in the study area. For the result validation, the extracted lineaments are compared against the manually obtained lineaments through the application of directional filtering and edge enhancement as well as to the lineaments digitized from the existing geological maps of the study area. In addition, a digital elevation model (DEM) has been utilized for an accuracy assessment followed by the field verification. The obtained results show that the best correlation between automatically extracted lineaments, manual interpretation, and the preexisting lineament map is achieved from the radar Sentinel-1A images. The tests indicate that the radar data used in this study, with 5872 and 5865 lineaments extracted from VH and VV polarizations respectively, is more efficient for structural lineament mapping than the Landsat-8 and Sentinel-2A optical imagery, from which 2338 and 4745 lineaments were extracted respectively.