Estimating Proportion of Vegetation Cover at the Vicinity of Archaeological Sites Using Sentinel-1 and -2 Data, Supplemented by Crowdsourced OpenStreetMap Geodata

Monitoring vegetation cover is an essential parameter for assessing various natural and anthropogenic hazards that occur at the vicinity of archaeological sites and landscapes. In this study, we used free and open access to Copernicus Earth Observation datasets. In particular, the proportion of vegetation cover is estimated from the analysis of Sentinel-1 radar and Sentinel-2 optical images, upon their radiometric and geometric corrections. Here, the proportion of vegetation based on the Radar Vegetation Index and the Normalized Difference Vegetation Index is estimated. Due to the medium resolution of these datasets (10 m resolution), the crowdsourced OpenStreetMap service was used to identify fully and non-vegetated pixels. The case study is focused on the western part of Cyprus, whereas various open-air archaeological sites exist, such as the archaeological site of “Nea Paphos” and the “Tombs of the Kings”. A cross-comparison of the results between the optical and the radar images is presented, as well as a comparison with ready products derived from the Sentinel Hub service such as the Sentinel-1 Synthetic Aperture Radar Urban and Sentinel-2 Scene classification data. Moreover, the proportion of vegetation cover was evaluated with Google Earth red-green-blue free high-resolution optical images, indicating that a good correlation between the RVI and NDVI can be generated only over vegetated areas. The overall findings indicate that Sentinel-1 and -2 indices can provide a similar pattern only over vegetated areas, which can be further elaborated to estimate temporal changes using integrated optical and radar Sentinel data. This study can support future investigations related to hazard analysis based on the combined use of optical and radar sensors, especially in areas with high cloud-coverage.

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A Long-term Spatial and Temporal Analysis of NDVI Changes in Java Island Using Google Earth Engine

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/936/1/012038 ◽

2021 ◽

Vol 936 (1) ◽

pp. 012038

Author(s):

Benedict ◽

Lalu Muhamad Jaelani

Keyword(s):

Vegetation Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Image Data ◽

Temporal Analysis ◽

Google Earth ◽

Vegetation Density ◽

Angle Sensor ◽

Study Results ◽

Google Earth Engine

Abstract Java is Indonesia’s and the world’s most populous island. The increase in population on the island of Java reduces the area of forest and other vegetation covers. Landslides, floods, and other natural disasters are caused by reduced vegetation cover. Furthermore, it has the potential to lead to the extinction of flora and fauna. The Normalized Difference Vegetation Index (NDVI) can be used to monitor the vegetation cover. This study analyzes the NDVI changes value from 2005 to 2020 using Terra and Aqua MODIS image data processed using Google Earth Engine. Processing was carried out in some stages: down-setting, performing NDVI processing, calculating monthly average NDVI, calculating annual average NDVI, and analyzing. From the study results, the NDVI value of Terra and Aqua MODIS data has a solid but imperfect correlation coefficient due to differences in orbital time which causes differences in solar zenith angle, sensor viewing angle, and azimuth angle. Then from this study, it was found that overall, changes in vegetation density cover on the island of Java decreased, which was indicated by the NDVI decline rate of -0.00047/year. The most significant decrease in NDVI value occurred in the period 2015–2016, covering an area of 13994.630 km2, and the most significant increase in NDVI occurred in the period 2010–2011, covering an area of 2256.101 km2.

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Combined Use of Multi-Temporal Landsat-8 and Sentinel-2 Images for Wheat Yield Estimates at the Intra-Plot Spatial Scale

Agronomy ◽

10.3390/agronomy10030327 ◽

2020 ◽

Vol 10 (3) ◽

pp. 327 ◽

Cited By ~ 4

Author(s):

Remy Fieuzal ◽

Vincent Bustillo ◽

David Collado ◽

Gerard Dedieu

Keyword(s):

Relative Error ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Wheat Yield ◽

Coefficient Of Determination ◽

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 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 Normalized Difference Vegetation Index (NDVI) is combined with the yield maps collected during the crop rotation, the agricultural season 2014 showing the lower level of performances with a coefficient of determination (R2) of 0.44 and a root mean square error (RMSE) of 8.13 quintals by hectare (q.h−1) (corresponding to a relative error of 12.9%), the three other years being associated with values of R2 close or upper to 0.60 and RMSE lower than 7 q.h−1 (corresponding to a relative error inferior to 11.3%). Moreover, the proposed approach allows estimating the crop yield throughout the agricultural season, by using the successive images acquired from the sowing to the harvest. In such cases, early and accurate yield estimates are obtained three months before the end of the crop cycle. At this phenological stage, only a slight decrease in performance is observed compared to the statistic obtained just before the harvest.

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Mapping Winter Wheat with Optical and SAR Images Based on Google Earth Engine in Henan Province, China

Remote Sensing ◽

10.3390/rs14020284 ◽

2022 ◽

Vol 14 (2) ◽

pp. 284

Author(s):

Changchun Li ◽

Weinan Chen ◽

Yilin Wang ◽

Yu Wang ◽

Chunyan Ma ◽

...

Keyword(s):

Winter Wheat ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Growth Period ◽

Google Earth ◽

Henan Province ◽

Sar Images ◽

Modis Ndvi ◽

Optical Images ◽

Google Earth Engine

The timely and accurate acquisition of winter wheat acreage is crucial for food security. This study investigated the feasibility of extracting the spatial distribution map of winter wheat in Henan Province by using synthetic aperture radar (SAR, Sentinel-1A) and optical (Sentinel-2) images. Firstly, the SAR images were aggregated based on the growth period of winter wheat, and the optical images were aggregated based on the moderate resolution imaging spectroradiometer normalized difference vegetation index (MODIS-NDVI) curve. Then, five spectral features, two polarization features, and four texture features were selected as feature variables. Finally, the Google Earth Engine (GEE) cloud platform was employed to extract winter wheat acreage through the random forest (RF) algorithm. The results show that: (1) aggregated images based on the growth period of winter wheat and sensor characteristics can improve the mapping accuracy and efficiency; (2) the extraction accuracy of using only SAR images was improved with the accumulation of growth period. The extraction accuracy of using the SAR images in the full growth period reached 80.1%; and (3) the identification effect of integrated images was relatively good, which makes up for the shortcomings of SAR and optical images and improves the extraction accuracy of winter wheat.

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Analysis of vegetation cover and built-up areas in the Percut watershed landscape, North Sumatra Province using sentinel-2 imagery

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/912/1/012089 ◽

2021 ◽

Vol 912 (1) ◽

pp. 012089

Author(s):

B Slamet ◽

O K H Syahputra ◽

H Kurniawan ◽

M Saraan ◽

M M Harahap

Keyword(s):

Vegetation Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Health Condition ◽

Land Conversion ◽

Water Index ◽

The North ◽

Normalized Difference Water Index ◽

North Sumatra ◽

Sentinel 2

Abstract Changes in land cover have an impact on the health condition of a watershed. This research was conducted by utilizing Sentinel-2 imagery for the recording period 2020 and 2021. Three indices were used in this study, namely, the Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI). NDBI analysis indicates there is an increase in the built-up area of 2,092.62 hectares which means land conversion. NDWI classification shows an increase in the wetness area of 308.58 hectares, mainly occurring in the downstream part of the watershed, located to the north. There is an increase in the area of non-vegetated areas reaching 288.96 hectares in the Percut watershed based on the results of the NDVI analysis.

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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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Development of a New Phenology Algorithm for Fine Mapping of Cropping Intensity in Complex Planting Areas Using Sentinel-2 and Google Earth Engine

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10090587 ◽

2021 ◽

Vol 10 (9) ◽

pp. 587

Author(s):

Yan Guo ◽

Haoming Xia ◽

Li Pan ◽

Xiaoyang Zhao ◽

Rumeng Li ◽

...

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Growth Cycle ◽

Google Earth ◽

Henan Province ◽

Cropping Intensity ◽

Water Index ◽

Google Earth Engine ◽

Sentinel 2

Cropping intensity is a key indicator for evaluating grain production and intensive use of cropland. Timely and accurately monitoring of cropping intensity is of great significance for ensuring national food security and improving the level of national land management. In this study, we used all Sentinel-2 images on the Google Earth Engine cloud platform, and constructed an improved peak point detection method to extract the cropping intensity of a heterogeneous planting area combined with crop phenology. The crop growth cycle profiles were extracted from the multi-temporal normalized difference vegetation index (NDVI) and land surface water index (LSWI) datasets. Results show that by 2020, the area of single cropping, double cropping, and triple cropping in the Henan Province are 52,236.9 km2, 74,334.1 km2, and 1927.1 km2, respectively; the corresponding producer accuracies are 86.12%, 93.72%, and 91.41%, respectively; the corresponding user accuracies are 88.99%, 92.29%, and 71.26%, respectively. The overall accuracy is 90.95%, and the Kappa coefficient is 0.81. Using the sown area in the statistical yearbook data of cities in the Henan Province to verify the extraction results of this paper, the R2 is 0.9717, and the root mean square error is 1715.9 km2. This study shows that using all the Sentinel-2 data, the phenology algorithm, and cloud computing technology has great potential in producing a high spatio-temporal resolution dataset for crop remote sensing monitoring and agricultural policymaking in complex planting areas.

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INVESTIGATING THE PERFORMANCE OF RANDOM FOREST AND SUPPORT VECTOR REGRESSION FOR ESTIMATION OF CLOUD-FREE NDVI USING SENTINEL-1 SAR DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2020-1379-2020 ◽

2020 ◽

Vol XLIII-B3-2020 ◽

pp. 1379-1383

Author(s):

J. D. Mohite ◽

S. A. Sawant ◽

A. Pandit ◽

S. Pappula

Keyword(s):

Random Forest ◽

Support Vector Regression ◽

Cloud Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Incidence Angle ◽

Google Earth ◽

Support Vector ◽

Optical Remote Sensing ◽

Sentinel 2

Abstract. The current study focuses on the estimation of cloud-free Normalized Difference Vegetation Index (NDVI) using the Synthetic Aperture Radar (SAR) observations obtained from Sentinel-1 (A and B) sensor. South-West Summer Monsoon over the Indian sub-continent lasts for four months (mid-June to mid-October). During this time, optical remote sensing observations are affected by dense cloud cover. Therefore, there is a need for methodology to estimate state of vegetation during the cloud cover. The crops considered in this study are Paddy (Rice) from Punjab and Haryana, whereas Cotton, Turmeric, and Banana from Andhra Pradesh, India. We have considered, observations of Sentinel-1 and Sentinel-2 sensors with the same overpass day and non-cloudy pixels for each crop. We used Google Earth Engine to extract surface reflectance for the Sentinel-2 and Ground Range Detected (GRD) backscatter for Sentinel-1. The Red and NIR bands of Sentinel 2 were used to estimate NDVI. Sentinel-1 based VV, and VH backscatter was used for estimation of Normalized Ratio Procedure between Bands (NRPB). Regression analysis was performed by using NDVI as an independent variable, and VV, VH, NRPB, and radar incidence angle as dependant variables. We evaluated the performance of Linear regression with tuned Support Vector Regression (SVR) as well as tuned Random Forest Regression (RFR) using the independent data. Results showed that the RFR produced the lowest RMSE for all the crops in the study. The average RMSE using the RFR was 0.08, 0.09, 0.11, and 0.10 for Rice, Cotton, Banana, and Turmeric, respectively. Similarly, we have obtained R2 values of 0.79, 0.76, 0.69, and 0.71 for the same crops using the RFR. A model with 80 trees produced the best results for Rice and Cotton, whereas the model with 90 trees produced the best results for Banana and Turmeric. Analysis with NDVI threshold of 0.25 showed improved R2 and RMSE. We found that for grown crop canopy, SAR based NDVI estimates are reasonably matching with the optical NDVI. A good agreement was observed between the actual and estimated NDVI using the tuned RFR model.

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Using machine learning to model the distribution of Vegetation Types across Norway

10.5194/egusphere-egu21-15211 ◽

2021 ◽

Author(s):

Lasse Torben Keetz ◽

Anders Bryn ◽

Peter Horvath ◽

Olav Skarpaas ◽

Lena Merete Tallaksen ◽

...

Keyword(s):

Machine Learning ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Cross Validation ◽

Google Earth ◽

Vegetation Types ◽

Environmental Predictors ◽

Distribution Models ◽

Data Set ◽

Sentinel 2

<p>Machine learning (ML) provides a powerful set of tools that can improve the accuracy of distribution models by automatically representing underlying ecological relationships empirically captured from large sets of data. However, more recent methodological advances in ML that have been less frequently applied, e.g. in the field of deep learning, may yield additional potential for Distribution Modeling. In this project, we use two ML algorithms, Random Forest (RF) and multi-layer feed-forward artificial neural networks (ANN), to predict the occurrences of Vegetation Types (VT) across Norway. Accurate predictions may support environmental management or the validation of earth system models. The VT data (derived from the AR18x18 data set; n=31 classes) covers the entire spatial scope in 0.9 ha plots on a systematically sampled 18 km grid (n = 1,081 plots, n = 22,154 observations). It was obtained through a field-based survey by a group of trained experts between 2004 and 2014. We use the cloud-based platform "Google Earth Engine" to generate a set of remotely sensed predictor variables based on SENTINEL-2 satellite imagery (i.e. surface reflectance from 12 spectral bands and six vegetation indices). These are then combined with ancillary environmental rasters used previously to model Norwegian VT distribution, e.g. representing climate, land cover, or geological properties (n=55, before one-hot encoding). Preliminary results suggest that in both modeling approaches, the generated SENTINEL-2 variables, particularly the Normalized Difference Vegetation Index (NDVI), have the highest predictive power as measured by permutation importance. The mean overall accuracy using 5-fold cross-validation shows only minor differences between the two methods (approx. 0.45 for ANN vs. 0.44 for RF; the respective F1-scores are 0.35 for ANN and 0.34 for RF; most frequent class baseline accuracy = 0.136). The modeling challenges we currently face include a class imbalance in the VT data set, reconciling the different spatial resolutions of the environmental predictors, and discrepancies in the timing of data acquisition. The next steps in the project will be to incorporate spatial cross-validation into the workflow and to analyze the differences between the ML methods in detail (e.g. regarding the ability to model rare VTs or differences in variable importance). Moreover, we will evaluate the possibility to include additional satellite data sources. This work is a contribution to the Strategic Research Initiative &#8216;Land Atmosphere Interaction in Cold Environments&#8217; (LATICE) of the University of Oslo.</p>

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Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10040251 ◽

2021 ◽

Vol 10 (4) ◽

pp. 251

Author(s):

Christina Ludwig ◽

Robert Hecht ◽

Sven Lautenbach ◽

Martin Schorcht ◽

Alexander Zipf

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Open Data ◽

Green Spaces ◽

Data Sets ◽

Urban Green ◽

Urban Green Spaces ◽

Urban Quality Of Life ◽

Shafer Theory ◽

Sentinel 2

Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets, the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data for urban green space mapping is high but limited due to their respective uncertainties. Sentinel-2 imagery cannot distinguish public from private green spaces and its spatial resolution of 10 m fails to capture fine-grained urban structures, while in OSM green spaces are not mapped consistently and with the same level of completeness everywhere. To address these limitations, we propose to fuse these data sets under explicit consideration of their uncertainties. The Sentinel-2 derived Normalized Difference Vegetation Index was fused with OSM data using the Dempster–Shafer theory to enhance the detection of small vegetated areas. The distinction between public and private green spaces was achieved using a Bayesian hierarchical model and OSM data. The analysis was performed based on land use parcels derived from OSM data and tested for the city of Dresden, Germany. The overall accuracy of the final map of public urban green spaces was 95% and was mainly influenced by the uncertainty of the public accessibility model.

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Urban Fire Severity and Vegetation Dynamics in Southern California

Remote Sensing ◽

10.3390/rs13010019 ◽

2020 ◽

Vol 13 (1) ◽

pp. 19

Author(s):

Lauren E. H. Mathews ◽

Alicia M. Kinoshita

Keyword(s):

Vegetation Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Current Knowledge ◽

Fire Severity ◽

Satellite Image ◽

Burn Severity ◽

Native Vegetation ◽

Riparian Areas ◽

The Impact

A combination of satellite image indices and in-field observations was used to investigate the impact of fuel conditions, fire behavior, and vegetation regrowth patterns, altered by invasive riparian vegetation. Satellite image metrics, differenced normalized burn severity (dNBR) and differenced normalized difference vegetation index (dNDVI), were approximated for non-native, riparian, or upland vegetation for traditional timeframes (0-, 1-, and 3-years) after eleven urban fires across a spectrum of invasive vegetation cover. Larger burn severity and loss of green canopy (NDVI) was detected for riparian areas compared to the uplands. The presence of invasive vegetation affected the distribution of burn severity and canopy loss detected within each fire. Fires with native vegetation cover had a higher severity and resulted in larger immediate loss of canopy than fires with substantial amounts of non-native vegetation. The lower burn severity observed 1–3 years after the fires with non-native vegetation suggests a rapid regrowth of non-native grasses, resulting in a smaller measured canopy loss relative to native vegetation immediately after fire. This observed fire pattern favors the life cycle and perpetuation of many opportunistic grasses within urban riparian areas. This research builds upon our current knowledge of wildfire recovery processes and highlights the unique challenges of remotely assessing vegetation biophysical status within urban Mediterranean riverine systems.

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