scholarly journals Evaluation of Sentinel-1 and 2 Time Series for Land Cover Classification of Forest–Agriculture Mosaics in Temperate and Tropical Landscapes

2019 ◽  
Vol 11 (8) ◽  
pp. 979 ◽  
Author(s):  
Audrey Mercier ◽  
Julie Betbeder ◽  
Florent Rumiano ◽  
Jacques Baudry ◽  
Valéry Gond ◽  
...  

Monitoring forest–agriculture mosaics is crucial for understanding landscape heterogeneity and managing biodiversity. Mapping these mosaics from remotely sensed imagery remains challenging, since ecological gradients from forested to agricultural areas make characterizing vegetation more difficult. The recent synthetic aperture radar (SAR) Sentinel-1 (S-1) and optical Sentinel-2 (S-2) time series provide a great opportunity to monitor forest–agriculture mosaics due to their high spatial and temporal resolutions. However, while a few studies have used the temporal resolution of S-2 time series alone to map land cover and land use in cropland and/or forested areas, S-1 time series have not yet been investigated alone for this purpose. The combined use of S-1 & S-2 time series has been assessed for only one or a few land cover classes. In this study, we assessed the potential of S-1 data alone, S-2 data alone, and their combined use for mapping forest–agriculture mosaics over two study areas: a temperate mountainous landscape in the Cantabrian Range (Spain) and a tropical forested landscape in Paragominas (Brazil). Satellite images were classified using an incremental procedure based on an importance rank of the input features. The classifications obtained with S-2 data alone (mean kappa index = 0.59–0.83) were more accurate than those obtained with S-1 data alone (mean kappa index = 0.28–0.72). Accuracy increased when combining S-1 and 2 data (mean kappa index = 0.55–0.85). The method enables defining the number and type of features that discriminate land cover classes in an optimal manner according to the type of landscape considered. The best configuration for the Spanish and Brazilian study areas included 5 and 10 features, respectively, for S-2 data alone and 10 and 20 features, respectively, for S-1 data alone. Short-wave infrared and VV and VH polarizations were key features of S-2 and S-1 data, respectively. In addition, the method enables defining key periods that discriminate land cover classes according to the type of images used. For example, in the Cantabrian Range, winter and summer were key for S-2 time series, while spring and winter were key for S-1 time series.

Author(s):  
C. Dubois ◽  
M. M. Mueller ◽  
C. Pathe ◽  
T. Jagdhuber ◽  
F. Cremer ◽  
...  

Abstract. In this study, we analyze Sentinel-1 time series data to characterize the observed seasonality of different land cover classes in eastern Thuringia, Germany and to identify multi-temporal metrics for their classification. We assess the influence of different polarizations and different pass directions on the multi-temporal backscatter profile. The novelty of this approach is the determination of phenological parameters, based on a tool that has been originally developed for optical imagery. Furthermore, several additional multitemporal metrics are determined for the different classes, in order to investigate their separability for potential multi-temporal classification schemes. The results of the study show a seasonality for vegetation classes, which differs depending on the considered class: whereas pastures and broad-leaved forests show a decrease of the backscatter in VH polarization during summer, an increase of the backscatter in VH polarization is observed for coniferous forest. The observed seasonality is discussed together with meteorological information (precipitation and air temperature). Furthermore, a dependence of the backscatter of the pass direction (ascending/descending) is observed particularly for the urban land cover classes. Multi-temporal metrics indicate a good separability of principal land cover classes such as urban, agricultural and forested areas, but further investigation and use of seasonal parameters is needed for a distinct separation of specific forest sub-classes such as coniferous and deciduous.


GEOGRAFIA ◽  
2018 ◽  
Vol 42 (3) ◽  
pp. 129-143
Author(s):  
Clóvis CECHIM JÚNIOR ◽  
João Francisco Gonçalves ANTUNES ◽  
Jerry Adriani JOHANN ◽  
Júlio César Dalla Mora ESQUERDO

The main land use and land cover (LULC) changes that a given area passes over the time can be evaluated by using spatial-temporal analysis of satellites images. Then, it is possible to identify the LULC changes, as well as the main causes of environmental impacts. The objective of this paper was to analyze the LULC changes of the main agricultural lands cultivated in the Alto Paraguai Basin (BAP). This paper focused on the summer crops (soybean and corn) and the analysis of agricultural expansion. The results, considering a16-year comparison, showed an increase of 40.60% in the expansion of agricultural areas. The evaluation of the accuracy showed the efficiency of the methodology of agricultural mapping, presenting a Kappa Index of 0.85 for the 2000/2001 and 0.86 for the 2015/2016 crop seasons


Author(s):  
G. Kamdem De Teyou ◽  
Y. Tarabalka ◽  
I. Manighetti ◽  
R. Almar ◽  
S. Tripodi

Abstract. Many Earth observation programs such as Landsat, Sentinel, SPOT, and Pleiades produce huge volume of medium to high resolution multi-spectral images every day that can be organized in time series. These time series are a great opportunity to detect and measure the space and time changes of anthropogenic and natural features. In this work, we thus exploit both temporal and spatial information provided by these images to generate land cover maps. For this purpose, we combine a fully convolutional neural network with a convolutional long short-term memory. Implementation details of the proposed spatio-temporal neural network architecture are provided. Experimental results show that the temporal information provided by time series images allows increasing the accuracy of land cover classification, thus producing up-to-date maps that can help in identifying changes on earth in both time and space.


2020 ◽  
pp. 488-494
Author(s):  
Giovanna M. Aita ◽  
Young Hwan Moon

Xylooligosaccharides (XOS) is a group of emerging prebiotics that selectively stimulate the growth of advantageous gastrointestinal bacteria benefitting the host’s gut health and functionality. XOS can achieve positive biological effects at low daily doses and low caloric content, properties that are the same or more desirable than the already established prebiotics. XOS are present in plants in very low amounts so there is a great opportunity to isolate XOS with varying degrees of polymerization from the hemicellulose (xylan) fraction of lignocellulosic materials (e.g., bagasse), a source that offers both economic and environmental advantages. In this study, the recovery of XOS by the combined use of activated carbon adsorption, water washing and ethanol desorption from diluted acid pretreated energy cane bagasse hydrolysates was evaluated. The recovered XOS was tested for its prebiotic activity on Bifidobacterium adolescentis ATCC 15703. The final product of extracted XOS from energy cane bagasse (XOS EC Bagasse crude sample) had a purity of 93%, which was comparable to the purities observed with two commercially available XOS prebiotics, CPA (89%) and CPB (93%). XOS EC Bagasse crude sample exhibited prebiotic properties by stimulating the growth of B. adolescentis ATCC 15703 and by producing lactic acid, which were comparable to those observed with the commercial prebiotics.


Land ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 807
Author(s):  
Simone Valeri ◽  
Laura Zavattero ◽  
Giulia Capotorti

In promoting biodiversity conservation and ecosystem service capacity, landscape connectivity is considered a critical feature to counteract the negative effects of fragmentation. Under a Green Infrastructure (GI) perspective, this is especially true in rural and peri-urban areas where a high degree of connectivity may be associated with the enhancement of agriculture multifunctionality and sustainability. With respect to GI planning and connectivity assessment, the role of dispersal traits of tree species is gaining increasing attention. However, little evidence is available on how to select plant species to be primarily favored, as well as on the role of landscape heterogeneity and habitat quality in driving the dispersal success. The present work is aimed at suggesting a methodological approach for addressing these knowledge gaps, at fine scales and for peri-urban agricultural landscapes, by means of a case study in the Metropolitan City of Rome. The study area was stratified into Environmental Units, each supporting a unique type of Potential Natural Vegetation (PNV), and a multi-step procedure was designed for setting priorities aimed at enhancing connectivity. First, GI components were defined based on the selection of the target species to be supported, on a fine scale land cover mapping and on the assessment of land cover type naturalness. Second, the study area was characterized by a Morphological Spatial Pattern Analysis (MSPA) and connectivity was assessed by Number of Components (NC) and functional connectivity metrics. Third, conservation and restoration measures have been prioritized and statistically validated. Notwithstanding the recognized limits, the approach proved to be functional in the considered context and at the adopted level of detail. Therefore, it could give useful methodological hints for the requalification of transitional urban–rural areas and for the achievement of related sustainable development goals in metropolitan regions.


2021 ◽  
Vol 13 (15) ◽  
pp. 2967
Author(s):  
Nicola Acito ◽  
Marco Diani ◽  
Gregorio Procissi ◽  
Giovanni Corsini

Atmospheric compensation (AC) allows the retrieval of the reflectance from the measured at-sensor radiance and is a fundamental and critical task for the quantitative exploitation of hyperspectral data. Recently, a learning-based (LB) approach, named LBAC, has been proposed for the AC of airborne hyperspectral data in the visible and near-infrared (VNIR) spectral range. LBAC makes use of a parametric regression function whose parameters are learned by a strategy based on synthetic data that accounts for (1) a physics-based model for the radiative transfer, (2) the variability of the surface reflectance spectra, and (3) the effects of random noise and spectral miscalibration errors. In this work we extend LBAC with respect to two different aspects: (1) the platform for data acquisition and (2) the spectral range covered by the sensor. Particularly, we propose the extension of LBAC to spaceborne hyperspectral sensors operating in the VNIR and short-wave infrared (SWIR) portion of the electromagnetic spectrum. We specifically refer to the sensor of the PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission, and the recent Earth Observation mission of the Italian Space Agency that offers a great opportunity to improve the knowledge on the scientific and commercial applications of spaceborne hyperspectral data. In addition, we introduce a curve fitting-based procedure for the estimation of column water vapor content of the atmosphere that directly exploits the reflectance data provided by LBAC. Results obtained on four different PRISMA hyperspectral images are presented and discussed.


2017 ◽  
Vol 9 (11) ◽  
pp. 1095 ◽  
Author(s):  
Emmihenna Jääskeläinen ◽  
Terhikki Manninen ◽  
Johanna Tamminen ◽  
Marko Laine

2017 ◽  
Vol 10 (2) ◽  
pp. 32 ◽  
Author(s):  
Dengqiu Li ◽  
Dengsheng Lu ◽  
Ming Wu ◽  
Xuexin Shao ◽  
Jinhong Wei

Sign in / Sign up

Export Citation Format

Share Document