Delineating fragmented grassland patches in the tropical region using multi-seasonal SAR and Optical Satellite Images

2020 ◽  
Author(s):  
Abhishek Samrat ◽  
M. S. Devy ◽  
Ganesh
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
High Resolution ◽  
Land Cover ◽  
South Asia ◽  
Spatial Resolution ◽  
Post Monsoon ◽  
Habitat Matrix ◽  
Semi Arid ◽  
Sentinel 2

Globally grasslands are declining and are in highly degraded conditions. In south Asia grasslands are neglected and treated as wastelands. They remain unprotected, highly fragmented, and poorly understood which has led to a loss of unique biodiversity and livelihoods. Mapping grasslands accurately is a challenge and current maps based on optical remote sensing often over- or underestimate grasslands in south Asia due to a prevalant complex landscape matrix, small patch sizes, and obscuring monsoonal clouds. Synthetic Aperture Radar (SAR) fused with moderate spatial resolution has been used to delineate grasslands but, high-resolution, freely available ESA’s sentinel-1(SAR) and -2(optical) provides an opportunity to map small and fragmented patches that were not possible earlier with the publically available moderate or medium spatial resolution remote sensing dataset. Further, high resolution imageries require high computing power which is often limited with stand alone machines. Here we demonstrate that using cloud computing and optimal use of multi-seasonal imagery one can obtain a highly accurate land cover/use classification for a complex habitat matrix. We used freely accessible cloud computing platforms like Google Earth Engine (GEE) and land cover/use classification of sentinel-1 and -2. We compared the accuracy of grassland delineation between 1) seasonal (pre, during, and post-monsoon) sentinel-1, 2) post-monsoon sentinel-2, and 3) combined sentinel-1 and -2. We tested this method at two sites in a highly fragmented habitat matrix in semi-arid areas of Western and Southern India. The classification result has shown the overall accuracy of for the combined image was higher than only sentinel-2 and sentinel-1 alone for both sites. Grasslands habitat accuracy was also consistent with combined image classification across the sites. Our results identified newer grassland areas that coarse landuse management maps used by the government did not. The computation was done on a basic laptop and processing completed very quick. We, therefore, suggest that this novel approach of using cloud computing and optimal use of resource-hungry (computation and storage) high-resolution ESA’s sentinel-1 and -2 data, can be used to identify major land classes and small patchy grassland in the semi-arid regions of Asia and has the potential to map at continent level.

scholarly journals Effects of multi-temporal high-resolution remote sensing products on simulated hydrometeorological variables in a cultivated area (southwestern France)

2017 ◽  
Author(s):  
Jordi Etchanchu ◽  
Vincent Rivalland ◽  
Simon Gascoin ◽  
Jérôme Cros ◽  
Aurore Brut ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Land Surface ◽  
Agricultural Landscapes ◽  
Annual Precipitation ◽  
Surface Model ◽  
Area Index ◽  
The Impact ◽  
Sentinel 2

Abstract. Agricultural landscapes often include a patchwork of crop fields whose seasonal evolution is dependent on specific crop rotation patterns and phenologies. This temporal and spatial heterogeneity affects surface hydrometeorological processes as simulated by land surface and distributed hydrological models. Sentinel-2 mission satellite remote sensing products allow for the monitoring of land cover and vegetation dynamics at unprecedented spatial resolutions and revisit frequencies (20 m and 5 days, respectively) that are fully compatible with such heterogeneous agricultural landscapes. Here, we evaluate the impact of Sentinel-2-like remote sensing data on the simulation of surface water and energy flux via the ISBA-SURFEX land surface model. The study area is a 24 km by 24 km agricultural zone in southwestern France. An initial reference simulation was conducted from 2006–2010 using the ECOCLIMAP-II database. This global numerical land ecosystem database was created at a 1 km resolution and includes an ecosystem classification with a consistent set of land surface parameters required for the model, such as the Leaf Area Index (LAI) and albedo measures. The LAI of ECOCLIMAP is climatologic and derived from a 2000–2005 analysis of MODIS satellite products. This low resolution induces that several vegetation covers can be mixed in a model cell. The climatic construction of LAI dynamics also suggests that there is no interannual variability in the vegetation cycle. A second simulation was performed by forcing the same model with annual land cover maps and monthly LAI values derived from a series of 105 8 m-resolution Formosat-2 images for the same period. Both simulations were conducted at the parcel scale, i.e., a computation unit covers an area of connected pixels of the same vegetation type (a crop field, forest patch, etc.). To evaluate our simulations, we used in situ measurements of evapotranspiration and latent and sensible heat flux from two eddy covariance stations in the study area. Our results show that the use of Formosat-2 high-resolution products significantly improves simulated evapotranspiration results with respect to ECOCLIMAP-II, especially when a surface is covered with summer crops (the correlation coefficient with monthly measurements is increased by roughly 0.3 and the root mean square error is decreased by roughly 31 %). This finding is attributable to a better description of LAI evolution processes reflected by Formosat-2 data, which further modify soil water content and drainage levels of deep soil reservoirs. Effects on annual drainage patterns remain small but significant, i.e., an increase roughly equivalent to 4 % of annual precipitation levels from Formosat-2 data in comparison to reference values. In smaller proportions, runoff is also increased by roughly 1 % of annual precipitation when using Formosat-2 data. This study illustrates the potential for the Sentinel-2 mission to better represent effects of crop management on water budgeting for large, anthropized river basins.

scholarly journals Testing Accuracy of Land Cover Classification Algorithms in the Qilian Mountains Based on GEE Cloud Platform

2021 ◽  
Vol 13 (24) ◽  
pp. 5064
Author(s):  
Yanpeng Yang ◽  
Dong Yang ◽  
Xufeng Wang ◽  
Zhao Zhang ◽  
Zain Nawaz
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Spatial Resolution ◽  
Qilian Mountains ◽  
Classification Algorithms ◽  
Cloud Platform ◽  
Remote Sensing Classification ◽  
The Qilian Mountains ◽  
Sentinel 2

The Qilian Mountains (QLM) are an important ecological barrier in western China. High-precision land cover data products are the basic data for accurately detecting and evaluating the ecological service functions of the QLM. In order to study the land cover in the QLM and performance of different remote sensing classification algorithms for land cover mapping based on the Google Earth Engine (GEE) cloud platform, the higher spatial resolution remote sensing images of Sentinel-1 and Sentinel-2; digital elevation data; and three remote sensing classification algorithms, including the support vector machine (SVM), the classification regression tree (CART), and the random forest (RF) algorithms, were used to perform supervised classification of Sentinel-2 images of the QLM. Furthermore, the results obtained from the classification process were compared and analyzed by using different remote sensing classification algorithms and feature-variable combinations. The results indicated that: (1) the accuracy of the classification results acquired by using different remote sensing classification algorithms were different, and the RF had the highest classification accuracy, followed by the CART and the SVM; (2) the different feature variable combinations had different effects on the overall accuracy (OA) of the classification results and the performance of the identification and classification of the different land cover types; and (3) compared with the existing land cover products for the QLM, the land cover maps obtained in this study had a higher spatial resolution and overall accuracy.

scholarly journals Mapping Land Use from High Resolution Satellite Images by Exploiting the Spatial Arrangement of Land Cover Objects

2020 ◽  
Vol 12 (24) ◽  
pp. 4158
Author(s):  
Mengmeng Li ◽  
Alfred Stein
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Spatial Arrangement ◽  
Remote Sensing Images ◽  
Land Use Types ◽  
High Resolution Satellite Images

Spatial information regarding the arrangement of land cover objects plays an important role in distinguishing the land use types at land parcel or local neighborhood levels. This study investigates the use of graph convolutional networks (GCNs) in order to characterize spatial arrangement features for land use classification from high resolution remote sensing images, with particular interest in comparing land use classifications between different graph-based methods and between different remote sensing images. We examine three kinds of graph-based methods, i.e., feature engineering, graph kernels, and GCNs. Based upon the extracted arrangement features and features regarding the spatial composition of land cover objects, we formulated ten land use classifications. We tested those on two different remote sensing images, which were acquired from GaoFen-2 (with a spatial resolution of 0.8 m) and ZiYuan-3 (of 2.5 m) satellites in 2020 on Fuzhou City, China. Our results showed that land use classifications that are based on the arrangement features derived from GCNs achieved the highest classification accuracy than using graph kernels and handcrafted graph features for both images. We also found that the contribution to separating land use types by arrangement features varies between GaoFen-2 and ZiYuan-3 images, due to the difference in the spatial resolution. This study offers a set of approaches for effectively mapping land use types from (very) high resolution satellite images.

scholarly journals Automated Production of a Land Cover/Use Map of Europe Based on Sentinel-2 Imagery

2020 ◽  
Vol 12 (21) ◽  
pp. 3523
Author(s):  
Radek Malinowski ◽  
Stanisław Lewiński ◽  
Marcin Rybicki ◽  
Ewa Gromny ◽  
Małgorzata Jenerowicz ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Data Processing ◽  
Spatial Resolution ◽  
Large Scale ◽  
Observation Data ◽  
European Continent ◽  
Continental Scale ◽  
Land Cover Maps ◽  
Sentinel 2

Up-to-date information about the Earth’s surface provided by land cover maps is essential for numerous environmental and land management applications. There is, therefore, a clear need for the continuous and reliable monitoring of land cover and land cover changes. The growing availability of high resolution, regularly collected remote sensing data can support the increasing number of applications that require high spatial resolution products that are frequently updated (e.g., annually). However, large-scale operational mapping requires a highly-automated data processing workflow, which is currently lacking. To address this issue, we developed a methodology for the automated classification of multi-temporal Sentinel-2 imagery. The method uses a random forest classifier and existing land cover/use databases as the source of training samples. In order to demonstrate its operability, the method was implemented on a large part of the European continent, with CORINE Land Cover and High-Resolution Layers as training datasets. A land cover/use map for the year 2017 was produced, composed of 13 classes. An accuracy assessment, based on nearly 52,000 samples, revealed high thematic overall accuracy (86.1%) on a continental scale, and average overall accuracy of 86.5% at country level. Only low-frequency classes obtained lower accuracies and we recommend that their mapping should be improved in the future. Additional modifications to the classification legend, notably the fusion of thematically and spectrally similar vegetation classes, increased overall accuracy to 89.0%, and resulted in ten, general classes. A crucial aspect of the presented approach is that it embraces all of the most important elements of Earth observation data processing, enabling accurate and detailed (10 m spatial resolution) mapping with no manual user involvement. The presented methodology demonstrates possibility for frequent and repetitive operational production of large-scale land cover maps.

scholarly journals High-Resolution Reconstruction of the Maximum Snow Water Equivalent Based on Remote Sensing Data in a Mountainous Area

2020 ◽  
Vol 12 (3) ◽  
pp. 460 ◽  
Author(s):  
Mingyu Liu ◽  
Chuan Xiong ◽  
Jinmei Pan ◽  
Tianxing Wang ◽  
Jiancheng Shi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Snow Cover ◽  
Spatial Resolution ◽  
Snow Water Equivalent ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Mountainous Areas ◽  
Snow Water ◽  
Sentinel 2

Currently, the accurate estimation of the maximum snow water equivalent (SWE) in mountainous areas is an important topic. In this study, in order to improve the accuracy and spatial resolution of SWE reconstruction in alpine regions, the Sentinel-2(MSI) and Landsat 8(OLI) satellite data with the spatial resolution of tens of meters are used instead of the Moderate-resolution Imaging Spectroradiometer (MODIS) data so that the pixel mixing problem is avoided. Meanwhile, geostationary satellite-based and topographic-corrected incoming shortwave radiation is used in the restricted degree-day model to improve the accuracy of radiation inputs. The seasonal maximum SWE accumulation of a river basin in the winter season of 2017–2018 is estimated. The spatial and temporal characteristics of SWE at a fine spatial and temporal resolution are then analyzed. And the results of reconstruction model with different input parameters are compared. The results showed that the average maximum SWE of the study area in 2017–2018 was 377.83 mm and the accuracy of snow cover, air temperature and the radiation parameters all affects the maximum SWE distribution on magnitude, elevation and aspect. Although the accuracy of other forcing parameters still needs to be improved, the estimation of the local maximum snow water equivalent in mountainous areas benefits from the application of high-resolution Sentinel-2 and Landsat 8 data. The joint usage of high-resolution remote sensing data from different satellites can greatly improve the temporal and spatial resolution of snow cover and the spatial resolution of SWE estimation. This method can provide more accurate and detailed SWE for hydrological models, which is of great significance to hydrology and water resources research.

scholarly journals How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?

2020 ◽  
Vol 12 (3) ◽  
pp. 417 ◽  
Author(s):  
Xin Zhang ◽  
Liangxiu Han ◽  
Lianghao Han ◽  
Liang Zhu
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Land Cover ◽  
Object Detection ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Sensing Data

Land cover information plays an important role in mapping ecological and environmental changes in Earth’s diverse landscapes for ecosystem monitoring. Remote sensing data have been widely used for the study of land cover, enabling efficient mapping of changes of the Earth surface from Space. Although the availability of high-resolution remote sensing imagery increases significantly every year, traditional land cover analysis approaches based on pixel and object levels are not optimal. Recent advancement in deep learning has achieved remarkable success on image recognition field and has shown potential in high spatial resolution remote sensing applications, including classification and object detection. In this paper, a comprehensive review on land cover classification and object detection approaches using high resolution imagery is provided. Through two case studies, we demonstrated the applications of the state-of-the-art deep learning models to high spatial resolution remote sensing data for land cover classification and object detection and evaluated their performances against traditional approaches. For a land cover classification task, the deep-learning-based methods provide an end-to-end solution by using both spatial and spectral information. They have shown better performance than the traditional pixel-based method, especially for the categories of different vegetation. For an objective detection task, the deep-learning-based object detection method achieved more than 98% accuracy in a large area; its high accuracy and efficiency could relieve the burden of the traditional, labour-intensive method. However, considering the diversity of remote sensing data, more training datasets are required in order to improve the generalisation and the robustness of deep learning-based models.

scholarly journals Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

2021 ◽  
Vol 13 (21) ◽  
pp. 4483
Author(s):  
W. Gareth Rees ◽  
Jack Tomaney ◽  
Olga Tutubalina ◽  
Vasily Zharko ◽  
Sergey Bartalev
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Boreal Forest ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Fundamental Parameter ◽  
Sensing Data ◽  
Growing Stock ◽  
Stock Volume ◽  
Sentinel 2

Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data.

scholarly journals Leveraging machine learning to produce a cost-effective national building height map of Ireland

2021 ◽  
Author(s):  
Eoghan Keany ◽  
Geoffrey Bessardon ◽  
Emily Gleeson
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Urban Areas ◽  
Cost Effective ◽  
Land Cover Classification ◽  
National Scale ◽  
Building Height ◽  
Height Data

<p>To represent surface thermal, turbulent and humidity exchanges, Numerical Weather Prediction (NWP) systems require a land-cover classification map to calculate sur-face parameters used in surface flux estimation. The latest land-cover classification map used in the HARMONIE-AROME configuration of the shared ALADIN-HIRLAMNWP system for operational weather forecasting is ECOCLIMAP-SG (ECO-SG). The first evaluation of ECO-SG over Ireland suggested that sparse urban areas are underestimated and instead appear as vegetation areas (1). While the work of (2) on land-cover classification helps to correct the horizontal extent of urban areas, the method does not provide information on the vertical characteristics of urban areas. ECO-SG urban classification implicitly includes building heights (3), and any improvement to ECO-SG urban area extent requires a complementary building height dataset.</p><p>Openly accessible building height data at a national scale does not exist for the island of Ireland. This work seeks to address this gap in availability by extrapolating the preexisting localised building height data across the entire island. The study utilises information from both the temporal and spatial dimensions by creating band-wise temporal aggregation statistics from morphological operations, for both the Sentinel-1A/B and Sentinel-2A/B constellations (4). The extrapolation uses building height information from the Copernicus Urban Atlas, which contains regional coverage for Dublin at 10 m x10 m resolution (5). Various regression models were then trained on these aggregated statistics to make pixel-wise building height estimates. These model estimates were then evaluated with an adjusted RMSE metric, with the most accurate model chosen to map the entire country. This method relies solely on freely available satellite imagery and open-source software, providing a cost-effective mapping service at a national scale that can be updated more frequently, unlike expensive once-off private mapping services. Furthermore, this process could be applied by these services to reduce costs by taking a small representative sample and extrapolating the rest of the area. This method can be applied beyond national borders providing a uniform map that does not depends on the different private service practices facilitating the updates of global or continental land-cover information used in NWP.</p><p> </p><p>(1) G. Bessardon and E. Gleeson, “Using the best available physiography to improve weather forecasts for Ireland,” in Challenges in High-Resolution Short Range NWP at European level including forecaster-developer cooperation, European Meteorological Society, 2019.</p><p>(2) E. Walsh, et al., “Using machine learning to produce a very high-resolution land-cover map for Ireland, ” Advances in Science and Research,  (accepted for publication).</p><p>(3) CNRM, "Wiki - ECOCLIMAP-SG" https://opensource.umr-cnrm.fr/projects/ecoclimap-sg/wiki</p><p>(4) D. Frantz, et al., “National-scale mapping of building height using sentinel-1 and sentinel-2 time series,” Remote Sensing of Environment, vol. 252, 2021.</p><p>(5) M. Fitrzyk, et al., “Esa Copernicus sentinel-1 exploitation activities,” in IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 2019.</p>

scholarly journals Melt Pond Retrieval Based on the LinearPolar Algorithm Using Landsat Data

2021 ◽  
Vol 13 (22) ◽  
pp. 4674
Author(s):  
Yuqing Qin ◽  
Jie Su ◽  
Mingfeng Wang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Relative Error ◽  
Large Scale ◽  
The Arctic ◽  
Landsat Data ◽  
Melt Pond ◽  
Melt Ponds ◽  
Very High ◽  
Sentinel 2

The formation and distribution of melt ponds have an important influence on the Arctic climate. Therefore, it is necessary to obtain more accurate information on melt ponds on Arctic sea ice by remote sensing. The present large-scale melt pond products, especially the melt pond fraction (MPF), still require verification, and using very high resolution optical satellite remote sensing data is a good way to verify the large-scale retrieval of MPF products. Unlike most MPF algorithms using very high resolution data, the LinearPolar algorithm using Sentinel-2 data considers the albedo of melt ponds unfixed. In this paper, by selecting the best band combination, we applied this algorithm to Landsat 8 (L8) data. Moreover, Sentinel-2 data, as well as support vector machine (SVM) and iterative self-organizing data analysis technique (ISODATA) algorithms, are used as the comparison and verification data. The results show that the recognition accuracy of the LinearPolar algorithm for melt ponds is higher than that of previous algorithms. The overall accuracy and kappa coefficient results achieved by using the LinearPolar algorithm with L8 and Sentinel-2A (S2), the SVM algorithm, and the ISODATA algorithm are 95.38% and 0.88, 94.73% and 0.86, and 92.40%and 0.80, respectively, which are much higher than those of principal component analysis (PCA) and Markus algorithms. The mean MPF (10.0%) obtained from 80 cases from L8 data based on the LinearPolar algorithm is much closer to Sentinel-2 (10.9%) than the Markus (5.0%) and PCA algorithms (4.2%), with a mean MPF difference of only 0.9%, and the correlation coefficients of the two MPFs are as high as 0.95. The overall relative error of the LinearPolar algorithm is 53.5% and 46.4% lower than that of the Markus and PCA algorithms, respectively, and the root mean square error (RMSE) is 30.9% and 27.4% lower than that of the Markus and PCA algorithms, respectively. In the cases without obvious melt ponds, the relative error is reduced more than that of those with obvious melt ponds because the LinearPolar algorithm can identify 100% of dark melt ponds and relatively small melt ponds, and the latter contributes more to the reduction in the relative error of MPF retrieval. With a wider range and longer time series, the MPF from Landsat data are more efficient than those from Sentinel-2 for verifying large-scale MPF products or obtaining long-term monitoring of a fixed area.

Sign in / Sign up

Export Citation Format

Share Document