Using satellite images to assess the state of arable fields on the example of the East Kazakhstan region

This article proposes a methodology for assessing the state of arable fields based on the use of Sentinel 2 satellite data. The essence of this methodology is cluster analysis of NDVI vegetation index profiles for a number of years, as well as expert analysis of the obtained results. The proposed method for assessing the state of arable fields has been tested on the example of arable lands of East Kazakhstan Agricultural Experimental Station LLP. This method can be used to optimize crop placement.

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Data-fusion of satellite and ground sensors for river hydro-morphodynamics monitoring

10.5194/egusphere-egu2020-20381 ◽

2020 ◽

Author(s):

Maria Nicolina Papa ◽

Michael Nones ◽

Carmela Cavallo ◽

Massimiliano Gargiulo ◽

Giuseppe Ruello

Keyword(s):

Spatial Resolution ◽

Satellite Data ◽

Satellite Images ◽

Sampling Rate ◽

Ecological Status ◽

Multispectral Images ◽

Sar Images ◽

Shoreline Changes ◽

Fluvial Morphology ◽

Sentinel 2

Changes in fluvial morphology, such as the migration of channels and sandbars, are driven by many factors e.g. water, woody debris and sediment discharges, vegetation and management practice. Nowadays, increased anthropic pressure and climate change are accelerating the natural morphologic dynamics. Therefore, the monitoring of river changes and the assessment of future trends are necessary for the identification of the optimal management practices, aiming at the improvement of river ecological status and the mitigation of hydraulic risk. Satellite data can provide an effective and cost-effective tool for the monitoring of river morphology and its temporal evolution.The main idea of this work is to understand which remote sensed data, and particularly which space and time resolutions, are more adapt for the observation of sandbars evolution in relatively large rivers. To this purpose, multispectral and Synthetic Aperture Radar (SAR) archive data, with different spatial resolution, were used. Preference was given to satellite data freely available. Moreover, the observations extracted by the satellite data were compared with ground data recorded by a fixed camera.The study case is a sandy bar (area about 0.4 km2 and maximum width about 350 m) in a lowland reach of the Po River (Italy), characterized by frequent and relevant morphological changes. The bar shoreline changes were captured by a fixed video camera, installed on a bridge and operating for almost two years (July 2017 - November 2018). To this purpose, we used: Sentinel-2 multispectral images with a spatial resolution of 10 m, Sentinel-1 SAR images with a resolution of 5 x 20 m and CosmoSkyMed SAR images with a resolution of 5 m. It is worth noting that the Sentinel data of the Copernicus Programme are freely available while the CosmoSkyMed data of the Italian Space Agency (ASI) are freely distributed for scientific purpose after the successful participation to an open call. In order to validate the results provided by Sentinel and CosmoSkyMed data, we used very high resolution multispectral images (about 50 cm).Multispectral images are easily interpreted, but are affected by the presence of cloud cover. For instance, in this analysis, the expendable multispectral images were equal to about 50% of the total archive. On the other hand, the SAR images provide information also in the presence of clouds and at night-time, but they have the drawback of more complex processing and interpretation. The shorelines extracted from the satellite images were compared with those extracted from photographic images, taken on the same day of the satellite acquisition. Other comparisons were made between different satellite images acquired with a temporal mismatch of maximum two days.The results of the comparisons showed that the Sentinel-1 and Sentinel-2 data were both adequate for the shoreline changes observation. Due to the higher resolution, the CosmoSkyMed data provided better results. SAR data and multispectral data allowed for automatic extraction of the bar shoreline, with different degree of processing burden. The fusion of data from different satellites gave the opportunity of highly increase the sampling rate.

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Monitoring Olive Oil Mill Wastewater Disposal Sites Using Sentinel-2 and PlanetScopeSatellite Images: Case Studies in Tunisia and Greece

Agronomy ◽

10.3390/agronomy12010090 ◽

2021 ◽

Vol 12 (1) ◽

pp. 90

Author(s):

Wissal Issaoui ◽

Dimitrios D. Alexakis ◽

Imen Hamdi Nasr ◽

Athanasios V. Argyriou ◽

Evangelos Alevizos ◽

...

Keyword(s):

Olive Oil ◽

Satellite Images ◽

Vegetation Index ◽

Satellite Image ◽

Olive Mill Wastewater ◽

Support Vector ◽

Inorganic Particles ◽

Mediterranean Countries ◽

Sentinel 2 ◽

Olive Mill

Mediterranean countries are known worldwide for their significant contribution to olive oil production, which generates large amounts of olive mill wastewater (OMW) that degrades land and water environments near the disposal sites. OMW consists of organic substances with high concentrations of phenolic compounds along with inorganic particles. The aim of this study is to assess the effectiveness of satellite image analysis techniques using multispectral satellite data with high (PlanetScope, 3 × 3 m) and medium (Sentinel-2, 10 × 10 m) spatial resolution to detect Olive Mill Wastewater (OMW) disposal sites, both in the SidiBouzid region (Tunisia) and in the broader Rethymno region on the island of Crete, (Greece). Documentation of the sites was carried out by collecting spectral signatures of OMW at temporal periods. The study integrates the application of a variety of spectral vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), in order to evaluate their efficiency in detecting OMW disposal areas. Furthermore, a set of image-processing methods was applied on satellite images to improve the monitoring of OMW ponds including the false-color composites (FCC), the Principal Component Analysis (PCA), and image fusion. Finally, different classification algorithms, such as the ISODATA, the maximum likelihood (ML), and the Support Vector Machine (SVM) were applied to both satellite images in order to assist in the overall approach to effectively detect the sites. The results obtained from different approaches were compared, evaluating the efficiency of Sentinel-2 and PlanetScope images to detect and monitor OMW disposal areas under different morphological environments.

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Use of Sentinel-2 Satellite Data for Windthrows Monitoring and Delimiting: The Case of “Vaia” Storm in Friuli Venezia Giulia Region (North-Eastern Italy)

Remote Sensing ◽

10.3390/rs13081530 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1530

Author(s):

Valentina Olmo ◽

Enrico Tordoni ◽

Francesco Petruzzellis ◽

Giovanni Bacaro ◽

Alfredo Altobelli

Keyword(s):

Satellite Data ◽

Vegetation Index ◽

Standard Procedure ◽

Abies Alba ◽

Recovery Process ◽

Short Wave ◽

Short Period ◽

North Eastern ◽

Friuli Venezia Giulia ◽

Sentinel 2

On the 29th of October 2018, a storm named “Vaia” hit North-Eastern Italy, causing the loss of 8 million m3 of standing trees and creating serious damage to the forested areas, with many economic and ecological implications. This event brought up the necessity of a standard procedure for windthrow detection and monitoring based on satellite data as an alternative to foresters’ fieldwork. The proposed methodology was applied in Carnic Alps (Friuli Venezia Giulia, NE Italy) in natural stands dominated by Picea abies and Abies alba. We used images from the Sentinel-2 mission: 1) to test vegetation indices performance in monitoring the vegetation dynamics in the short period after the storm, and 2) to create a windthrow map for the whole Friuli Venezia Giulia region. Results showed that windthrows in forests have a significant influence on visible and short-wave infrared (SWIR) spectral bands of Sentinel-2, both in the short and the long-term timeframes. NDWI8A and NDWI were the best indices for windthrow detection (R2 = 0.80 and 0.77, respectively) and NDVI, PSRI, SAVI and GNDVI had an overall good performance in spotting wind-damaged areas (R2 = 0.60–0.76). Moreover, these indices allowed to monitor post-Vaia forest die-off and showed a dynamic recovery process in cleaned sites. The NDWI8A index, employed in the vegetation index differencing (VID) change detection technique, delimited damaged areas comparable to the estimations provided by Regional Forest System (2545 ha and 3183 ha, respectively). Damaged forests detected by NDWI8A VID ranged from 500 m to 1500 m a.s.l., mainly covering steep slopes in the south and east aspects (42% and 25%, respectively). Our results suggested that the NDWI8A VID method may be a cost-effective and accurate way to produce windthrow maps, which could limit the risks associated with fieldwork and may provide a valuable tool to plan tree removal interventions in a more efficient way.

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SiDroForest Siberian Drone-mapped Forest inventory

10.5194/egusphere-egu21-15106 ◽

2021 ◽

Author(s):

Femke van Geffen ◽

Birgit Heim ◽

Ulrike Herzschuh ◽

Luidmila Pestryakova ◽

Evgenii Zakharov ◽

...

Keyword(s):

Machine Learning ◽

Satellite Data ◽

Forest Structure ◽

Vegetation Index ◽

Point Clouds ◽

Training Data ◽

Data Repository ◽

Surface Model ◽

Marine Research ◽

Sentinel 2

To gain a better understanding of global carbon storage and albedo feedback mechanisms it is important to have insights into high latitude vegetation change. Boreal forest compositions are changing in response to changes in climate, which in turn can lead to feedbacks in regional and global climate through altered carbon cycles and albedo dynamics. Circumpolar boreal forests represent close to 30% of all forested area on the planet, between 900 and 1,200 million ha. These forests are located primarily in Alaska, Canada, and Russia. Due to the remote location of these forests and the short seasons without snow, data collected on the boreal vegetation is limited.&#160;The proposed dataset is an attempt to remedy data scarcity whilst providing adjusted data for machine learning practices.We present a dataset containing diverse formats of forest structure information that covers two important vegetation transition zones in Siberia: the Evergreen - Summergreen transition zone in Central Yakutia and the northern treeline in Chukotka (NE Siberia).This dataset contains data from the locations covered by fieldwork was performed by the Alfred Wegener Institute for Polar and Marine research, (AWI) and the North-Eastern Federal University of Yakutsk&#8203; (NEFU). The fieldwork upscaled through the addition of Red Green Blue(RGB) UAV (Unmanned Aerial Vehicle) camera data and Sentinel-2 satellite data cropped to a 5 km radius around the fieldwork sites. The dataset is created with the aim of providing ground truth validation and training data to be used in various vegetation related machine learning tasks .The dataset contains:1.Labelled individual trees per 30x30 m plot assigned in field work with additional data on species, height, crown width, and biomass.2.Structure from Motion (SfM)point clouds that provide 3D information about the forest structure, included generated Canopy Height Model (CHM), Digital Elevation Model (DEM) and a Digital Surface Model (DSM) per 50x50 m.3.Multispectral Sentinel-2 satellite data (10 m ) cropped to a 5km radius with generated a NDVI(normalized difference vegetation index), available in three seasons: Early Summer, Peak Summer and Late Summer.4.Extracted tree crowns with species information and a synthetically generated large (10.000 samples) dataset for training machine leaning algorithms.The dataset will be made publicly available on&#160;the data repository&#160;PANGAEA.

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Sentinel-2 Data in an Evaluation of the Impact of the Disturbances on Forest Vegetation

Remote Sensing ◽

10.3390/rs12121914 ◽

2020 ◽

Vol 12 (12) ◽

pp. 1914 ◽

Cited By ~ 1

Author(s):

Josef Lastovicka ◽

Pavel Svec ◽

Daniel Paluba ◽

Natalia Kobliuk ◽

Jan Svoboda ◽

...

Keyword(s):

Bark Beetle ◽

Satellite Data ◽

National Park ◽

Vegetation Index ◽

Vegetation Indices ◽

Forest Vegetation ◽

Forest Recovery ◽

Vegetation Changes ◽

Tasseled Cap ◽

Sentinel 2

In this article, we investigated the detection of forest vegetation changes during the period of 2017 to 2019 in the Low Tatras National Park (Slovakia) and the Sumava National Park (Czechia) using Sentinel-2 data. The evaluation was based on a time-series analysis using selected vegetation indices. The case studies represented five different areas according to the type of the forest vegetation degradation (one with bark beetle calamity, two areas with forest recovery mode after a bark beetle calamity, and two areas without significant disturbances). The values of the trajectories of the vegetation indices (normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI)) and the orthogonal indices (tasseled cap greenness (TCG) and tasseled cap wetness (TCW)) were analyzed and validated by in situ data and aerial photographs. The results confirm the abilities of the NDVI, the NDMI and the TCW to distinguish disturbed and undisturbed areas. The NDMI vegetation index was particularly useful for the detection of the disturbed forest and forest recovery after bark beetle outbreaks and provided relevant information regarding the health of the forest (the individual stages of the disturbances and recovery mode). On the contrary, the TCG index demonstrated only limited abilities. The TCG could distinguish healthy forest and the gray-attack disturbance phase; however, it was difficult to use this index for detecting different recovery phases and to distinguish recovery phases from healthy forest. The areas affected by the disturbances had lower values of NDVI and NDMI indices (NDVI quartile range Q2–Q3: 0.63–0.71; NDMI Q2–Q3: 0.10–0.19) and the TCW index had negative values (Q2–Q3: −0.06–−0.05)). The analysis was performed with a cloud-based tool—Sentinel Hub. Cloud-based technologies have brought a new dimension in the processing and analysis of satellite data and allowed satellite data to be brought to end-users in the forestry sector. The Copernicus program and its data from Sentinel missions have evoked new opportunities in the application of satellite data. The usage of Sentinel-2 data in the research of long-term forest vegetation changes has a high relevance and perspective due to the free availability, distribution, and well-designed spectral, temporal, and spatial resolution of the Sentinel-2 data for monitoring forest ecosystems.

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Land cover monitoring as part of a survey on wetland ecosystem conservation in the Negovan village area using remote sensing tools

Glasnik Sumarskog fakulteta ◽

10.2298/gsf1919175r ◽

2019 ◽

pp. 175-188

Author(s):

Kameliya Radeva ◽

Emiliya Velizarova ◽

Adlin Dancheva

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Satellite Data ◽

Vegetation Index ◽

Remote Sensing Data ◽

Vegetation Period ◽

Wetland Ecosystem ◽

Temporal Space ◽

Temporal Intervals ◽

Sentinel 2

The main purpose of the present survey is to apply remote sensing data to the investigation of different components of a wetland ecosystem, situated in the area of the village of Negovan (Sofia region), such as soil, vegetation and water, and their variation for certain temporal intervals including the vegetation period. This survey represents the process of interim ecological monitoring (IEM) implementation on the studied ecosystem. Data for the current condition of different ecosystem components - soil, vegetation and water components, and their variations within the selected time period of 5 years (2014-2018) have been obtained. Specific relations among wetland actual components conditions such as soil wetness and vegetation vs climate factors within the respective temporal intervals of wetland monitoring process have been established. Aerospace data with different temporal, space and spectral resolution, satellite data from Sentinel 2, MSI and aerophoto with a very high resolution have been used. The results for ?Brightness?, ?Greenness? and ?Wetness? components obtained on the basis of orthogonalization of satellite data from Sentinel 2 have been introduced. The results reflect the value of Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI 2), Normalized Difference Greenness Index (NDGI) and Normalized Difference Water Index (NDWI), which are of great importance for the relationship between soil health indexes and ecosystem sustainability. Thematic maps are generated based on the results obtained by surveying land cover components. Data received for the current condition of Negovan wetland ecosystem and established variations of different parameters, including soil component could be used while assessing wetland ecosystem services.

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Analyzing the Effects of Hyperspectral ZhuHai-1 Band Combinations on LAI Estimation Based on the PROSAIL Model

Sensors ◽

10.3390/s21051869 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1869

Author(s):

Yangyang Zhang ◽

Jian Yang ◽

Lin Du

Keyword(s):

Satellite Data ◽

Vegetation Index ◽

Hyperspectral Data ◽

Quantitative Detection ◽

Inversion Method ◽

Vegetation Monitoring ◽

Area Index ◽

Band Combination ◽

Band Combinations ◽

Sentinel 2

Leaf area index (LAI) is a key biophysical variable to characterize vegetation canopy. Accurate and quantitative LAI estimation is significant for monitoring vegetation growth status. ZhuHai-1 (ZH-1), which is a commercial remote sensing micro-nano satellite, provides a possibility for quantitative detection of vegetation with high spatial and spectral resolution. However, the band characteristics of ZH-1 are closely related to the accuracy of vegetation monitoring. In this study, a simulation dataset containing 32 bands of ZH-1 was generated by using the PROSAIL model, which was used to analyze the performance of 32 bands for LAI estimation by using the hybrid inversion method. Meanwhile, the effect of different band combinations on LAI estimation was discussed based on sensitivity analysis and the correlation between bands. Then, the optimal band combination from ZH-1 hyperspectral satellite data for LAI estimation was obtained. LAI estimation was performed based on the selected optimal band combination of ZH-1 satellite images in Xiantao city, Hubei province, and compared with the Sentinel-2 normalized difference vegetation index (NDVI) values and LAI product. The results demonstrated that the obtained LAI map based on the optimal band combination of ZH-1 was generally consistent with the overall distribution of Sentinel-2 NDVI and the LAI product, but had a moderate correlation with Sentinel-2 LAI (R = 0.60), which may not favorably indicate the validity of indirect validation. However, the method of this study on the analysis of hyperspectral data bands has application potential to provide a reference for selecting appropriate bands of hyperspectral satellite data to estimate LAI and improve the application of hyperspectral data such as ZH-1 in vegetation monitoring.

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Estimation of Wheat Yields at the Intra-Plot Scale by Combining Multi-Temporal Landsat-8 and Sentinel-2 Images

Proceedings ◽

10.3390/iecg2019-06220 ◽

2019 ◽

Vol 24 (1) ◽

pp. 14

Author(s):

Remy Fieuzal ◽

Vincent Bustillo ◽

David Collado ◽

Gerard Dedieu

Keyword(s):

Relative Error ◽

Satellite Images ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Yield Variability ◽

Landsat 8 ◽

Optical Images ◽

Multi Temporal ◽

Input Variables ◽

Sentinel 2

The objective of this study is to address the capabilities of multi-temporal optical images to estimate the fine-scale yield variability of wheat over a study site located in southwestern France. The methodology is based on the use of Landsat-8 and Sentinel-2 satellite images acquired after the sowing and before the harvest of the crop throughout four successive agricultural seasons, the reflectance constituting the input variables of a statistical algorithm (random forest). The best performances are obtained when the NDVI (Normalized Difference Vegetation Index) is combined with the previous yield maps, the agricultural season 2014 showing the lower level of performances with a R² of 0.44 and a RMSE (Root Mean Square Error) of 8.13 q.h−1 (corresponding to a relative error of 12.9%), the three other years being associated with values of R² close or upper of 0.60 and RMSE lower than 7 q.h−1 (corresponding to a relative error inferior to 11.3%).

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Assessment of the impact of ungoverned spaces on insurgency in Borno State, Nigeria

Ghana Journal of Geography ◽

10.4314/gjg.v13i2.2 ◽

2021 ◽

Vol 13 (2) ◽

Author(s):

Sylvanus Helda Bernard ◽

Mwanret Gideon Daful

Keyword(s):

Population Density ◽

Satellite Data ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Population Data ◽

Information Criterion ◽

The State ◽

High Incidence ◽

The Impact ◽

Vegetal Cover

This study examines the relationship between ungoverned spaces and insurgency in the Borno State, Nigeria. The aim is to understand the influence of geographical variables on the activities of insurgence. The study used satellite data, population data and data on insurgency attack in the study area. Normalized Difference Vegetation Index, percentage rise in slope analysis and reclassification were used for the satellite data processing. Geographically Weighted Regression (GWR) models was employed for data analysis. The findings revealed that LGAs in the central and the southern parts of the state recorded the highest number of insurgency attacks. The central and far northern part of the state has more vegetal cover, which has influenced the high incidence of insurgency attack observed. In addition, the very high incidence of insurgency attack (145) observed in Gwoza LGA, is largely attributed to the presence of the Gwoza Mountain, which is one of the main strong holds of the insurgents in Borno State. The GWR analysis reveals that the performance of the model with the population density was much better than the other variables with a corrected Akaike Information Criterion (AICc) value of 273.15, R-Squared values of 0.0323, 0.0224, 0.0203 and 0.8901 for the undulating terrain, vegetation, combination of vegetation and undulating terrain, and population density respectively. Thus, the study concludes that vegetal cover and population density have more influence on insurgency attack in the study area. Hence, the need for policy makers and security establishments to properly monitor the forested areas.

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DURANKULAK LAKE ACTUAL STATE AND MONITORING USING SENTINEL 2 SATELLITE DATA

10.32006/eeep.2019.2.5358 ◽

2019 ◽

pp. 53-58

Author(s):

Iva Ivanova ◽

Iliyana Gigova ◽

Temenuzhka Spassova ◽

Nataliya Stankova

Keyword(s):

Satellite Data ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Orthogonal Transformation ◽

Transformation Model ◽

Actual State ◽

The European Union ◽

Ramsar Convention ◽

Natural Habitats ◽

Sentinel 2

Durankulak Lake is one of the most important wetlands in Bulgaria and Europe. It is included in the Ramsar Convention and is recognized as an important bird area of world importance. The subject of protection within the protected zone is the condition of the natural habitats and the habitats of the species, including the natural species composition, the typical species and the conditions of the environment. Remote sensing methods provide opportunities for characterization and monitoring of the wetland on various scales that have not been done so far. In the present study satellite multispectral images from the European Union Copernicus Satellite Program, Sentinel 2 are used for assessment and monitoring of the actual state of the lake. Based on these satellite images, the boundaries of the protected wetland are derived. An index classification of the wetland was made. Normalized Difference Vegetation Index (NDVI) is used to classify sites within the protected area. Sentinel-2 satellite data to implement the orthogonal transformation model called Tasseled Cap Transformation (TCT) has also been used. The model is an effective method for classifying and analyzing of the processes related to the dynamics of changes, affecting the main components of the earth's surface: soil, water and vegetation. The spring survey of 2019 was selected for the present study. The results will show successful mapping and monitoring of the wetland, which will give a real idea of the state of the Durankulak Lake and the need to take conservation measures to protect it. Key words: monitoring, satellite data, wetlands, habitats

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