Assessing lodging damage of jute crop due to super cyclone Amphan using multi-temporal Sentinel-1 and Sentinel-2 data over parts of West Bengal, India

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.

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Multi-Temporal Sentinel-2 Data Analysis for Smallholding Forest Cut Control

Remote Sensing ◽

10.3390/rs13152983 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2983

Author(s):

Alberto López-Amoedo ◽

Xana Álvarez ◽

Henrique Lorenzo ◽

Juan Luis Rodríguez

Keyword(s):

Rural Environment ◽

Accurate Estimation ◽

Land Use Management ◽

Nw Spain ◽

Management Plans ◽

Ndvi Time Series ◽

Multi Temporal ◽

And Control ◽

Potential Use ◽

Sentinel 2

Land fragmentation and small plots are the main features of the rural environment of Galicia (NW Spain). Smallholding limits land use management, representing a drawback in local forest planning. This study analyzes the potential use of multitemporal Sentinel-2 images to detect and control forest cuts in very small pine and eucalyptus plots located in southern Galicia. The proposed approach is based on the analysis of Sentinel-2 NDVI time series in 4231 plots smaller than 3 ha (average 0.46 ha). The methodology allowed us to detect cuts, allocate cut dates and quantify plot areas due to different cutting cycles in an uneven-aged stand. An accuracy of approximately 95% was achieved when the whole plot was cut, with an 81% accuracy for partial cuts. The main difficulty in detecting and dating cuts was related to cloud cover, which affected the multitemporal analysis. In conclusion, the proposed methodology provides an accurate estimation of cutting date and area, helping to improve the monitoring system in sustainable forest certifications to ensure compliance with forest management plans.

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Sentinel-2 Multi-Temporal Data for Rice Crop Classification in Nepal

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9323771 ◽

2020 ◽

Author(s):

Tina Baidar ◽

Ruben Fernandez-Beltran ◽

Filiberto Pla

Keyword(s):

Rice Crop ◽

Temporal Data ◽

Multi Temporal ◽

Crop Classification ◽

Sentinel 2

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ANALISIS TEMPORAL PERUBAHAN HUTAN MANGROVE MENGGUNAKAN CITRA SATELIT SENTINEL-2 (Studi Kasus di Pulau Tanakeke, Kabupaten Takalar)

Seminar Nasional Geomatika ◽

10.24895/sng.2020.0-0.1193 ◽

2021 ◽

pp. 777

Author(s):

Andi Tenri Waru ◽

Athar Abdurrahman Bayanuddin ◽

Ferman Setia Nugroho ◽

Nita Rukminasari

Keyword(s):

Random Forest ◽

Google Earth ◽

Multi Temporal ◽

Google Earth Engine ◽

Sentinel 2

Pulau Tanakeke merupakan salah satu pulau dengan hutan mangrove yang luas di pesisir Sulawesi Selatan. Hutan mangrove ini menjadi ekosistem penting bagi masyarakat sekitar karena nilai ekologi maupun ekonominya. Namun, dalam kurun waktu sekitar tahun 1980-2000, keberadaan mangrove tersebut terancam oleh perubahan penggunaan lahan dan juga pemanfaatan yang berlebihan. Penelitian ini bertujuan untuk menganalisis perubahan temporal luas dan tingkat kerapatan hutan mangrove di Pulau Tanakeke antara tahun 2016 dan 2019. Metode analisis perubahan luasan hutan mangrove menggunakan data citra satelit Sentinel-2 multi temporal berdasarkan hasil klasifikasi hutan mangrove dengan menggunakan random forest pada platform Google Earth Engine. Akurasi keseluruhan hasil klasifikasi hutan mangrove tahun 2016 dan 2019 sebesar 91% dan 98%. Berdasarkan hasil analisis spasial diperoleh perubahan penurunan luasan mangrove yang signifikan dari 800,21 ha menjadi 640,15 ha. Kerapatan mangrove di Pulau Tanakeke sebagian besar tergolong kategori dalam kerapatan tinggi.

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Assessing the spatial variation of cropping intensity using multi-temporal Sentinel-2 data by rule-based classification

Environment Development and Sustainability ◽

10.1007/s10668-021-01885-0 ◽

2021 ◽

Author(s):

Argha Ghosh ◽

Manoj K. Nanda ◽

Debolina Sarkar

Keyword(s):

Spatial Variation ◽

Rule Based ◽

Cropping Intensity ◽

Multi Temporal ◽

Sentinel 2

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Landsat-8 and Sentinel-2 burned area mapping - A combined sensor multi-temporal change detection approach

Remote Sensing of Environment ◽

10.1016/j.rse.2019.111254 ◽

2019 ◽

Vol 231 ◽

pp. 111254 ◽

Cited By ~ 31

Author(s):

David P. Roy ◽

Haiyan Huang ◽

Luigi Boschetti ◽

Louis Giglio ◽

Lin Yan ◽

...

Keyword(s):

Change Detection ◽

Temporal Change ◽

Burned Area ◽

Landsat 8 ◽

Detection Approach ◽

Burned Area Mapping ◽

Multi Temporal ◽

Combined Sensor ◽

Sentinel 2

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Mapping the Distribution of Coffee Plantations from Multi-Resolution, Multi-Temporal, and Multi-Sensor Data Using a Random Forest Algorithm

Remote Sensing ◽

10.3390/rs12233933 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3933

Author(s):

Anggun Tridawati ◽

Ketut Wikantika ◽

Tri Muji Susantoro ◽

Agung Budi Harto ◽

Soni Darmawan ◽

...

Keyword(s):

Random Forest ◽

Remote Sensing Data ◽

Agroforestry System ◽

Classification Model ◽

Sensor Data ◽

Kappa Statistics ◽

Random Forest Algorithm ◽

Coffee Plantations ◽

Multi Temporal ◽

Sentinel 2

Indonesia is the world’s fourth largest coffee producer. Coffee plantations cover 1.2 million ha of the country with a production of 500 kg/ha. However, information regarding the distribution of coffee plantations in Indonesia is limited. This study aimed to assess the accuracy of classification model and determine its important variables for mapping coffee plantations. The model obtained 29 variables which derived from the integration of multi-resolution, multi-temporal, and multi-sensor remote sensing data, namely, pan-sharpened GeoEye-1, multi-temporal Sentinel 2, and DEMNAS. Applying a random forest algorithm (tree = 1000, mtry = all variables, minimum node size: 6), this model achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy of 79.333%, 0.774, 92.000%, and 90.790%, respectively. In addition, 12 most important variables achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy 79.333%, 0.774, 91.333%, and 84.570%, respectively. Our results indicate that random forest algorithm is efficient in mapping coffee plantations in an agroforestry system.

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Combination of Multi-Temporal Sentinel 2 Images and Aerial Image Based Canopy Height Models for Timber Volume Modelling

Forests ◽

10.3390/f10090746 ◽

2019 ◽

Vol 10 (9) ◽

pp. 746 ◽

Cited By ~ 4

Author(s):

Johannes Schumacher ◽

Margret Rattay ◽

Melanie Kirchhöfer ◽

Petra Adler ◽

Gerald Kändler

Keyword(s):

Forest Inventory ◽

Point Clouds ◽

Support Vector ◽

Timber Volume ◽

Volume Model ◽

Broadleaf Tree ◽

Multi Temporal ◽

Conifer Trees ◽

Volume Models ◽

Sentinel 2

Multi-temporal Sentinel 2 optical images and 3D photogrammetric point clouds can be combined to enhance the accuracy of timber volume models on large spatial scale. Information on the proportion of broadleaf and conifer trees improves timber volume models obtained from 3D photogrammetric point clouds. However, the broadleaf-conifer information cannot be obtained from photogrammetric point clouds alone. Furthermore, spectral information of aerial images is too inconsistent to be used for automatic broadleaf-conifer classification over larger areas. In this study we combined multi-temporal Sentinel 2 optical satellite images, 3D photogrammetric point clouds from digital aerial stereo photographs, and forest inventory plots representing an area of 35,751 km2 in south-west Germany for (1) modelling the percentage of broadleaf tree volume (BL%) using Sentinel 2 time series and (2) modelling timber volume per hectare using 3D photogrammetric point clouds. Forest inventory plots were surveyed in the same years and regions as stereo photographs were acquired (2013–2017), resulting in 11,554 plots. Sentinel 2 images from 2016 and 2017 were corrected for topographic and atmospheric influences and combined with the same forest inventory plots. Spectral variables from corrected multi-temporal Sentinel 2 images were calculated, and Support Vector Machine (SVM) regressions were fitted for each Sentinel 2 scene estimating the BL% for corresponding inventory plots. Variables from the photogrammetric point clouds were calculated for each inventory plot and a non-linear regression model predicting timber volume per hectare was fitted. Each SVM regression and the timber volume model were evaluated using ten-fold cross-validation (CV). The SVM regression models estimating the BL% per Sentinel 2 scene achieved overall accuracies of 68%–75% and a Root Mean Squared Error (RMSE) of 21.5–26.1. The timber volume model showed a RMSE% of 31.7%, a mean bias of 0.2%, and a pseudo-R2 of 0.64. Application of the SVM regressions on Sentinel 2 scenes covering the state of Baden-Württemberg resulted in predictions of broadleaf tree percentages for the entire state. These predicted values were used as additional predictor in the timber volume model, allowing for predictions of timber volume for the same area. Spatially high-resolution information about growing stock is of great practical relevance for forest management planning, especially when the timber volume of a smaller unit is of interest, for example of a forest stand or a forest district where not enough terrestrial inventory plots are available to make reliable estimations. Here, predictions from remote-sensing based models can be used. Furthermore, information about broadleaf and conifer trees improves timber volume models and reduces model errors and, thereby, prediction uncertainties.

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