scholarly journals Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data

2018 ◽  
Vol 10 (6) ◽  
pp. 946 ◽  
Author(s):  
Yanan Liu ◽  
Weishu Gong ◽  
Xiangyun Hu ◽  
Jianya Gong
Keyword(s):  
Random Forest ◽  
Forest Type ◽  
Landsat 8 ◽  
Multi Temporal ◽  
Sentinel 2A

scholarly journals Greenhouse Crop Identification from Multi-Temporal Multi-Sensor Satellite Imagery Using Object-Based Approach: A Case Study from Almería (Spain)

2018 ◽  
Vol 10 (11) ◽  
pp. 1751 ◽  
Author(s):  
Abderrahim Nemmaoui ◽  
Manuel A. Aguilar ◽  
Fernando J. Aguilar ◽  
Antonio Novelli ◽  
Andrés García Lorca
Keyword(s):  
Time Series ◽  
Binary Classification ◽  
Free Access ◽  
Landsat 8 ◽  
Horticultural Crops ◽  
Command Line Tool ◽  
Multi Temporal ◽  
Tree Models ◽  
Sentinel 2A

A workflow headed up to identify crops growing under plastic-covered greenhouses (PCG) and based on multi-temporal and multi-sensor satellite data is developed in this article. This workflow is made up of four steps: (i) data pre-processing, (ii) PCG segmentation, (iii) binary pre-classification between greenhouses and non-greenhouses, and (iv) classification of horticultural crops under greenhouses regarding two agronomic seasons (autumn and spring). The segmentation stage was carried out by applying a multi-resolution segmentation algorithm on the pre-processed WorldView-2 data. The free access AssesSeg command line tool was used to determine the more suitable multi-resolution algorithm parameters. Two decision tree models mainly based on the Plastic Greenhouse Index were developed to perform greenhouse/non-greenhouse binary classification from Landsat 8 and Sentinel-2A time series, attaining overall accuracies of 92.65% and 93.97%, respectively. With regards to the classification of crops under PCG, pepper in autumn, and melon and watermelon in spring provided the best results (Fβ around 84% and 95%, respectively). Data from the Sentinel-2A time series showed slightly better accuracies than those from Landsat 8.

scholarly journals SYNERGETIC USE OF OPTICAL, MICROWAVE AND THERMAL SATELLITE DATA FOR NON-PARAMETRIC ESTIMATION OF WHEAT GRAIN YIELD

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 195-199
Author(s):  
K. K. Choudhary ◽  
V. Pandey ◽  
C. S. Murthy ◽  
M. K. Poddar
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Grain Yield ◽  
Crop Yield ◽  
Satellite Data ◽  
Parametric Estimation ◽  
Landsat 8 ◽  
Wheat Grain ◽  
Random Forest Regression ◽  
Multi Temporal

<p><strong>Abstract.</strong> Crop yield maps are very crucial inputs for different practical applications like crop production estimation, pay-out of crop insurance, yield gap analysis etc. Satellite derived vegetation indices across different electromagnetic region has the ability to explain the variation in crop yield and can be used for prediction of yield before harvesting. This study utilised indices derived from multi-temporal Optical, Thermal and Radar data for developing model for Wheat (Triticum aestivum) grain yield using Machine learning approaches i.e., Random Forest Regression (RFR). Time series of Sentinel-2 derived Normalized difference vegetation index (NDVI), Normalized difference water Index (NDWI), Landsat-8 derived GPP using LST-EVI relationship (Temparature-Greeness model) and Sentinel-1 derived cross-polarization backscatter ratio (&amp;sigma;VH/&amp;sigma;VV) were used as predictor for wheat yield estimation. Actual grain yield measurements at ground were carried out at the end of the season over 178 locations. Seventy five percent of ground yield data were used for training of the model and rest twenty five percent data were used for its validation. All the datasets were grouped into ten fortnightly datasets ranging from November 2017 to March 2018. Through the random forest regression using time-series of NDVI alone, wheat grain yields were estimated with an RMSE of 9.8&amp;thinsp;Q&amp;thinsp;ha<sup>&amp;minus;1</sup>. Subsequently by adding the multi-temporal NDWI, GPP and σVH/σVV led to the improvement of RMSE to 8.7, 7.6 and 7.4&amp;thinsp;Q&amp;thinsp;ha<sup>&amp;minus;1</sup> respectively. Variable importance based on the out of box error showed the significance of NDVI, NDWI and GPP during Dec-Jan and &amp;sigma;VH/&amp;sigma;VV during Feb for wheat grain estimation. It was concluded that the RFR algorithm together with the indices from optical, thermal and microwave satellite data can able to produced significantly accurate estimates of wheat grain yield.</p>

scholarly journals Modeling and Multi-Temporal Characterization of Total Suspended Matter by the Combined Use of Sentinel 2-MSI and Landsat 8-OLI Data: The Pertusillo Lake Case Study (Italy)

2020 ◽  
Vol 12 (13) ◽  
pp. 2147 ◽  
Author(s):  
Emanuele Ciancia ◽  
Alessandra Campanelli ◽  
Teodosio Lacava ◽  
Angelo Palombo ◽  
Simone Pascucci ◽  
...  
Keyword(s):  
Suspended Matter ◽  
Total Suspended Matter ◽  
Water Level Fluctuations ◽  
Landsat 8 ◽  
Combined Use ◽  
Multi Temporal ◽  
Water Constituents ◽  
Western Side ◽  
Sentinel 2A ◽  
Regional Authorities

The total suspended matter (TSM) variability plays a crucial role in a lake’s ecological functioning and its biogeochemical cycle. Sentinel-2A MultiSpectral Instrument (MSI) and Landsat 8 Operational Land Instrument (OLI) data offer unique opportunities for investigating certain in-water constituents (e.g., TSM and chlorophyll-a) owing to their spatial resolution (10–60 m). In this framework, we assessed the potential of MSI–OLI combined data in characterizing the multi-temporal (2014–2018) TSM variability in Pertusillo Lake (Basilicata region, Southern Italy). We developed and validated a customized MSI-based TSM model (R2 = 0.81) by exploiting ground measurements acquired during specific measurement campaigns. The model was then exported as OLI data through an intercalibration procedure (R2 = 0.87), allowing for the generation of a TSM multi-temporal MSI–OLI merged dataset. The analysis of the derived multi-year TSM monthly maps showed the influence of hydrological factors on the TSM seasonal dynamics over two sub-regions of the lake, the west and east areas. The western side is more influenced by inflowing rivers and water level fluctuations, the effects of which tend to longitudinally decrease, leading to less sediment within the eastern sub-area. The achieved results can be exploited by regional authorities for better management of inland water quality and monitoring systems.

Accuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets

2019 ◽  
Vol 35 (10) ◽  
pp. 1088-1108 ◽  
Author(s):  
Lamin R. Mansaray ◽  
Fumin Wang ◽  
Jingfeng Huang ◽  
Lingbo Yang ◽  
Adam Sheka Kanu
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Support Vector ◽  
Landsat 8 ◽  
Sentinel 2A

scholarly journals A Multi-Channel Algorithm for Mapping Volcanic Thermal Anomalies by Means of Sentinel-2 MSI and Landsat-8 OLI Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2876 ◽  
Author(s):  
Francesco Marchese ◽  
Nicola Genzano ◽  
Marco Neri ◽  
Alfredo Falconieri ◽  
Giuseppe Mazzeo ◽  
...  
Keyword(s):  
Near Infrared ◽  
Short Wave ◽  
Global Scale ◽  
Landsat 8 ◽  
Thermal Anomalies ◽  
Volcanic Plumes ◽  
Processing Times ◽  
Multi Temporal ◽  
Sentinel 2A ◽  
Sentinel 2

The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively onboard Sentinel-2A/2B and Landsat 8 satellites, thanks to their features especially in terms of spatial/spectral resolution, represents two important instruments for investigating thermal volcanic activity from space. In this study, we used data from those sensors to test an original multichannel algorithm, which aims at mapping volcanic thermal anomalies at a global scale. The algorithm, named Normalized Hotspot Indices (NHI), combines two normalized indices, analyzing near infrared (NIR) and short wave infrared (SWIR) radiances, to identify hotspot pixels in daylight conditions. Results, achieved studying a number of active volcanoes located in different geographic areas and characterized by a different eruptive behavior, demonstrated the NHI capacity in mapping both subtle and more intense volcanic thermal anomalies despite some limitations (e.g., missed detections because of clouds/volcanic plumes). In addition, the study shows that the performance of NHI might be further increased using some additional spectral/spatial tests, in view of a possible usage of this algorithm within a known multi-temporal scheme of satellite data analysis. The low processing times and the straight forth exportability to data from other sensors make NHI, which is sensitive even to other high temperature sources, suited for mapping hot volcanic targets integrating information provided by current and well-established satellite-based volcanoes monitoring systems.

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

scholarly journals Classifying Forest Type in the National Forest Inventory Context with Airborne Hyperspectral and Lidar Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1863
Author(s):  
Caileigh Shoot ◽  
Hans-Erik Andersen ◽  
L. Monika Moskal ◽  
Chad Babcock ◽  
Bruce D. Cook ◽  
...  
Keyword(s):  
Random Forest ◽  
Forest Inventory ◽  
Forest Type ◽  
Vegetation Indices ◽  
National Forest Inventory ◽  
Classification Algorithm ◽  
Lidar Data ◽  
National Forest ◽  
Interior Alaska ◽  
Type Information

Forest structure and composition regulate a range of ecosystem services, including biodiversity, water and nutrient cycling, and wood volume for resource extraction. Forest type is an important metric measured in the US Forest Service Forest Inventory and Analysis (FIA) program, the national forest inventory of the USA. Forest type information can be used to quantify carbon and other forest resources within specific domains to support ecological analysis and forest management decisions, such as managing for disease and pests. In this study, we developed a methodology that uses a combination of airborne hyperspectral and lidar data to map FIA-defined forest type between sparsely sampled FIA plot data collected in interior Alaska. To determine the best classification algorithm and remote sensing data for this task, five classification algorithms were tested with six different combinations of raw hyperspectral data, hyperspectral vegetation indices, and lidar-derived canopy and topography metrics. Models were trained using forest type information from 632 FIA subplots collected in interior Alaska. Of the thirty model and input combinations tested, the random forest classification algorithm with hyperspectral vegetation indices and lidar-derived topography and canopy height metrics had the highest accuracy (78% overall accuracy). This study supports random forest as a powerful classifier for natural resource data. It also demonstrates the benefits from combining both structural (lidar) and spectral (imagery) data for forest type classification.

scholarly journals Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests

2020 ◽  
Vol 12 (11) ◽  
pp. 1876 ◽  
Author(s):  
Katsuto Shimizu ◽  
Tetsuji Ota ◽  
Nobuya Mizoue ◽  
Hideki Saito
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Forest Monitoring ◽  
Series Data ◽  
Landsat 8 ◽  
Multi Temporal ◽  
Sentinel 2

Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3%) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5%, 35.2%, and 34.2% for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4% more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest monitoring.

scholarly journals Non-Binary Snow Index for Multi-Component Surfaces

2021 ◽  
Vol 13 (14) ◽  
pp. 2777
Author(s):  
Mario Arreola-Esquivel ◽  
Carina Toxqui-Quitl ◽  
Maricela Delgadillo-Herrera ◽  
Alfonso Padilla-Vivanco ◽  
Gabriel Ortega-Mendoza ◽  
...  
Keyword(s):  
Spectral Characteristics ◽  
Ice Cover ◽  
Landsat 8 ◽  
Water Index ◽  
Bare Land ◽  
Snow Water ◽  
Precision Assessment ◽  
Normalized Difference Snow Index ◽  
Sentinel 2A ◽  
Water Vegetation

A Non-Binary Snow Index for Multi-Component Surfaces (NBSI-MS) is proposed to map snow/ice cover. The NBSI-MS is based on the spectral characteristics of different Land Cover Types (LCTs), such as snow, water, vegetation, bare land, impervious, and shadow surfaces. This index can increase the separability between NBSI-MS values corresponding to snow from other LCTs and accurately delineate the snow/ice cover in non-binary maps. To test the robustness of the NBSI-MS, regions in Greenland and France–Italy where snow interacts with highly diversified geographical ecosystems were examined. Data recorded by Landsat 5 TM, Landsat 8 OLI, and Sentinel-2A MSI satellites were used. The NBSI-MS performance was also compared against the well-known Normalized Difference Snow Index (NDSI), NDSII-1, S3, and Snow Water Index (SWI) methods and evaluated based on Ground Reference Test Pixels (GRTPs) over non-binarized results. The results show that the NBSI-MS achieved an overall accuracy (OA) ranging from 0.99 to 1 with kappa coefficient values in the same range as the OA. The precision assessment confirmed the performance superiority of the proposed NBSI-MS method for removing water and shadow surfaces over the compared relevant indices.

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