High-Resolution Vegetation Mapping in Japan by Combining Sentinel-2 and Landsat 8 Based Multi-Temporal Datasets through Machine Learning and Cross-Validation Approach

Accurate information about the available standing biomass on pastures is critical for the adequate management of grazing and its promotion to farmers. In this paper, machine learning models are developed to predict available biomass expressed as compressed sward height (CSH) from readily accessible meteorological, optical (Sentinel-2) and radar satellite data (Sentinel-1). This study assumed that combining heterogeneous data sources, data transformations and machine learning methods would improve the robustness and the accuracy of the developed models. A total of 72,795 records of CSH with a spatial positioning, collected in 2018 and 2019, were used and aggregated according to a pixel-like pattern. The resulting dataset was split into a training one with 11,625 pixellated records and an independent validation one with 4952 pixellated records. The models were trained with a 19-fold cross-validation. A wide range of performances was observed (with mean root mean square error (RMSE) of cross-validation ranging from 22.84 mm of CSH to infinite-like values), and the four best-performing models were a cubist, a glmnet, a neural network and a random forest. These models had an RMSE of independent validation lower than 20 mm of CSH at the pixel-level. To simulate the behavior of the model in a decision support system, performances at the paddock level were also studied. These were computed according to two scenarios: either the predictions were made at a sub-parcel level and then aggregated, or the data were aggregated at the parcel level and the predictions were made for these aggregated data. The results obtained in this study were more accurate than those found in the literature concerning pasture budgeting and grassland biomass evaluation. The training of the 124 models resulting from the described framework was part of the realization of a decision support system to help farmers in their daily decision making.

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

Remote Sensing ◽

10.3390/rs12111876 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1876 ◽

Cited By ~ 1

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.

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A Machine Learning Approach for Mapping Forest Vegetation in Riparian Zones in an Atlantic Biome Environment Using Sentinel-2 Imagery

Remote Sensing ◽

10.3390/rs12244086 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4086

Author(s):

Danielle Elis Garcia Furuya ◽

João Alex Floriano Aguiar ◽

Nayara V. Estrabis ◽

Mayara Maezano Faita Pinheiro ◽

Michelle Taís Garcia Furuya ◽

...

Keyword(s):

Machine Learning ◽

Environmental Planning ◽

Riparian Zone ◽

Learning Algorithms ◽

Vegetation Mapping ◽

Forest Vegetation ◽

Machine Learning Algorithms ◽

Riparian Zones ◽

Support Vector ◽

Sentinel 2

Riparian zones consist of important environmental regions, specifically to maintain the quality of water resources. Accurately mapping forest vegetation in riparian zones is an important issue, since it may provide information about numerous surface processes that occur in these areas. Recently, machine learning algorithms have gained attention as an innovative approach to extract information from remote sensing imagery, including to support the mapping task of vegetation areas. Nonetheless, studies related to machine learning application for forest vegetation mapping in the riparian zones exclusively is still limited. Therefore, this paper presents a framework for forest vegetation mapping in riparian zones based on machine learning models using orbital multispectral images. A total of 14 Sentinel-2 images registered throughout the year, covering a large riparian zone of a portion of a wide river in the Pontal do Paranapanema region, São Paulo state, Brazil, was adopted as the dataset. This area is mainly composed of the Atlantic Biome vegetation, and it is near to the last primary fragment of its biome, being an important region from the environmental planning point of view. We compared the performance of multiple machine learning algorithms like decision tree (DT), random forest (RF), support vector machine (SVM), and normal Bayes (NB). We evaluated different dates and locations with all models. Our results demonstrated that the DT learner has, overall, the highest accuracy in this task. The DT algorithm also showed high accuracy when applied on different dates and in the riparian zone of another river. We conclude that the proposed approach is appropriated to accurately map forest vegetation in riparian zones, including temporal context.

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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 Rice Paddy Based on Machine Learning with Sentinel-2 Multi-Temporal Data: Model Comparison and Transferability

Remote Sensing ◽

10.3390/rs12101620 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1620 ◽

Cited By ~ 1

Author(s):

Weichun Zhang ◽

Hongbin Liu ◽

Wei Wu ◽

Linqing Zhan ◽

Jing Wei

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Rice Paddy ◽

Temporal Data ◽

Model Transferability ◽

Local Classification ◽

Multi Temporal ◽

Sentinel 2

Rice is an important agricultural crop in the Southwest Hilly Area, China, but there has been a lack of efficient and accurate monitoring methods in the region. Recently, convolutional neural networks (CNNs) have obtained considerable achievements in the remote sensing community. However, it has not been widely used in mapping a rice paddy, and most studies lack the comparison of classification effectiveness and efficiency between CNNs and other classic machine learning models and their transferability. This study aims to develop various machine learning classification models with remote sensing data for comparing the local accuracy of classifiers and evaluating the transferability of pretrained classifiers. Therefore, two types of experiments were designed: local classification experiments and model transferability experiments. These experiments were conducted using cloud-free Sentinel-2 multi-temporal data in Banan District and Zhongxian County, typical hilly areas of Southwestern China. A pure pixel extraction algorithm was designed based on land-use vector data and a Google Earth Online image. Four convolutional neural network (CNN) algorithms (one-dimensional (Conv-1D), two-dimensional (Conv-2D) and three-dimensional (Conv-3D_1 and Conv-3D_2) convolutional neural networks) were developed and compared with four widely used classifiers (random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM) and multilayer perceptron (MLP)). Recall, precision, overall accuracy (OA) and F1 score were applied to evaluate classification accuracy. The results showed that Conv-2D performed best in local classification experiments with OA of 93.14% and F1 score of 0.8552 in Banan District, OA of 92.53% and F1 score of 0.8399 in Zhongxian County. CNN-based models except Conv-1D provided more desirable performance than non-CNN classifiers. Besides, among the non-CNN classifiers, XGBoost received the best result with OA of 89.73% and F1 score of 0.7742 in Banan District, SVM received the best result with OA of 88.57% and F1 score of 0.7538 in Zhongxian County. In model transferability experiments, almost all CNN classifiers had low transferability. RF and XGBoost models have achieved acceptable F1 scores for transfer (RF = 0.6673 and 0.6469, XGBoost = 0.7171 and 0.6709, respectively).

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High-resolution digital mapping of soil organic carbon and soil total nitrogen using DEM derivatives, Sentinel-1 and Sentinel-2 data based on machine learning algorithms

The Science of The Total Environment ◽

10.1016/j.scitotenv.2020.138244 ◽

2020 ◽

Vol 729 ◽

pp. 138244 ◽

Cited By ~ 2

Author(s):

Tao Zhou ◽

Yajun Geng ◽

Jie Chen ◽

Jianjun Pan ◽

Dagmar Haase ◽

...

Keyword(s):

Machine Learning ◽

High Resolution ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Total Nitrogen ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Soil Total Nitrogen ◽

Digital Mapping ◽

Sentinel 2

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Mapping Winter Wheat with Combinations of Temporally Aggregated Sentinel-2 and Landsat-8 Data in Shandong Province, China

Remote Sensing ◽

10.3390/rs12122065 ◽

2020 ◽

Vol 12 (12) ◽

pp. 2065 ◽

Cited By ~ 1

Author(s):

Feng Xu ◽

Zhaofu Li ◽

Shuyu Zhang ◽

Naitao Huang ◽

Zongyao Quan ◽

...

Keyword(s):

Winter Wheat ◽

Google Earth ◽

Shandong Province ◽

Sensor Data ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Multi Temporal ◽

Wheat Development ◽

Development Phases ◽

Sentinel 2

Winter wheat is one of the major cereal crops in China. The spatial distribution of winter wheat planting areas is closely related to food security; however, mapping winter wheat with time-series finer spatial resolution satellite images across large areas is challenging. This paper explores the potential of combining temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data available via the Google Earth Engine (GEE) platform for mapping winter wheat in Shandong Province, China. First, six phenological median composites of Landsat-8 OLI and Sentinel-2 MSI reflectance measures were generated by a temporal aggregation technique according to the winter wheat phenological calendar, which covered seedling, tillering, over-wintering, reviving, jointing-heading and maturing phases, respectively. Then, Random Forest (RF) classifier was used to classify multi-temporal composites but also mono-temporal winter wheat development phases and mono-sensor data. The results showed that winter wheat could be classified with an overall accuracy of 93.4% and F1 measure (the harmonic mean of producer’s and user’s accuracy) of 0.97 with temporally aggregated Landsat-8 and Sentinel-2 data were combined. As our results also revealed, it was always good to classify multi-temporal images compared to mono-temporal imagery (the overall accuracy dropped from 93.4% to as low as 76.4%). It was also good to classify Landsat-8 OLI and Sentinel-2 MSI imagery combined instead of classifying them individually. The analysis showed among the mono-temporal winter wheat development phases that the maturing phase’s and reviving phase’s data were more important than the data for other mono-temporal winter wheat development phases. In sum, this study confirmed the importance of using temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data combined and identified key winter wheat development phases for accurate winter wheat classification. These results can be useful to benefit on freely available optical satellite data (Landsat-8 OLI and Sentinel-2 MSI) and prioritize key winter wheat development phases for accurate mapping winter wheat planting areas across China and elsewhere.

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An improved rule-based approach to map burned areas using Landsat and Sentinel-2 images

10.5194/egusphere-egu2020-13168 ◽

2020 ◽

Author(s):

Symeon Kanaropoulos ◽

Nikos Koutsias

Keyword(s):

Spectral Properties ◽

Threshold Value ◽

Landsat 8 ◽

Rule Based ◽

Spectral Signatures ◽

Burned Areas ◽

Multi Temporal ◽

Rule Based Approach ◽

Spectral Patterns ◽

Sentinel 2

This study presents an improvement of an old rule-based semi-automatic method to map burned areas by using multi-temporal Landsat and Seninel-2 images. The rule-based approach consists of a set of rules developed based on spectral properties of burned areas as compared to the pre-fire unburned vegetation and to the spectral signatures of other land cover types found in post-fire satellite scene. Actually, the spectral properties based on which the rules have been developed are presented in two graphs, one that corresponds to spectral signatures plots and the second that corresponds to the histogram data plots. The spectral patterns based on which the rule-based approach has been developed are not always the same. For example, depending on the type of the fire-affected vegetation (e.g. dry vegetation instead of green) the spectral pattern of the SWIR channel that correspond to channel 7 in Landsat 4-7 and 8 is not valid. Instead, there is a similar spectral behaviour but in the SWIR channel that correspond to channel 5 in Landsat 4-7, or channel 6 in Landsat 8. Additionally, the threshold value of 0.10-0.25 of the second rule seems not to be sufficient to cover all variability since there are cases that this value should be higher. Two characteristic examples of the insufficiencies found on the old-rules are concerned in the current analysis, one that presents limitations concerning the rule 5 (Serifos) and one that represents limitations concerning the rule 2 (Portugal). In this study we present a further improvement of the method and also its application to several cases spread out in Greek islands using both Landsat and Sentinel-2 images.

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Experiments on Machine Learning Techniques for Soil Classification Using Sentinel-2 Products

10.5194/egusphere-egu2020-11080 ◽

2020 ◽

Author(s):

Victor Bacu ◽

Teodor Stefanut ◽

Dorian Gorgan

Keyword(s):

Machine Learning ◽

High Resolution ◽

Satellite Data ◽

Field Measurements ◽

Soil Classification ◽

Machine Learning Techniques ◽

Classification Models ◽

Spatial Features ◽

Learning Techniques ◽

Sentinel 2

Agricultural management relies on good, comprehensive and reliable information on the environment and, in particular, the characteristics of the soil. The soil composition, humidity and temperature can fluctuate over time, leading to migration of plant crops, changes in the schedule of agricultural work, and the treatment of soil by chemicals. Various techniques are used to monitor soil conditions and agricultural activities but most of them are based on field measurements. Satellite data opens up a wide range of solutions based on higher resolution images (i.e. spatial, spectral and temporal resolution). Due to this high resolution, satellite data requires powerful computing resources and complex algorithms. The need for up-to-date and high-resolution soil maps and direct access to this information in a versatile and convenient manner is essential for pedology and agriculture experts, farmers and soil monitoring organizations.Unfortunately, the satellite image processing and interpretation are very particular to each area, time and season, and must be calibrated by the real field measurements that are collected periodically. In order to obtain a fairly good accuracy of soil classification at a very high resolution, without using interpolation methods of an insufficient number of measurements, the prediction based on artificial intelligence techniques could be used. The use of machine learning techniques is still largely unexplored, and one of the major challenges is the scalability of the soil classification models toward three main directions: (a) adding new spatial features (i.e. satellite wavelength bands, geospatial parameters, spatial features); (b) scaling from local to global geographical areas; (c) temporal complementarity (i.e. build up the soil description by samples of satellite data acquired along the time, on spring, on summer, in another year, etc.).The presentation analysis some experiments and highlights the main issues on developing a soil classification model based on Sentinel-2 satellite data, machine learning techniques and high-performance computing infrastructures. The experiments concern mainly on the features and temporal scalability of the soil classification models. The research is carried out using the HORUS platform [1] and the HorusApp application [2], [3], which allows experts to scale the computation over cloud infrastructure.&#160;References:[1] Gorgan D., Rusu T., Bacu V., Stefanut T., Nandra N., &#8220;Soil Classification Techniques in Transylvania Area Based on Satellite Data&#8221;. World Soils 2019 Conference, 2 - 3 July 2019, ESA-ESRIN, Frascati, Italy (2019).[2] Bacu V., Stefanut T., Gorgan D., &#8220;Building soil classification maps using HorusApp and Sentinel-2 Products&#8221;, Proceedings of the Intelligent Computer Communication and Processing Conference &#8211; ICCP, in IEEE press (2019).[3] Bacu V., Stefanut T., Nandra N., Rusu T., Gorgan D., &#8220;Soil classification based on Sentinel-2 Products using HorusApp application&#8221;, Geophysical Research Abstracts, Vol. 21, EGU2019-15746, 2019, EGU General Assembly (2019).

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