15 results on '"RANDOM forest algorithms"'
Search Results
2. Auditing the spatial and temporal changes in urban cropland in Harare metropolitan Province, Zimbabwe.
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Musosa, Lenon, Shekede, Munyaradzi D, Gwitira, Isaiah, Chirisa, Innocent, Tevera, Daniel, and Matamanda, Abraham R
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URBAN agriculture , *FARMS , *RANDOM forest algorithms , *REMOTE sensing , *AUDITING ,DEVELOPING countries - Abstract
Urban agriculture continues to play a key role in addressing food insecurity and poverty across developing countries. This study used Landsat 5 imagery to map the spatial and temporal distribution of cropland in Harare Metropolitan in Zimbabwe using the Random Forest classifier in Google Earth Engine. Results show that the area under cultivation fluctuated considerably between 1985 and 2019 and several factors account for this trend. The results of this study illustrate the increasing importance of remote sensing in assessing cropland in urban areas that provides insights into the contribution of urban agriculture to food security. [ABSTRACT FROM AUTHOR]
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- 2024
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3. Extraction of tobacco planting area by using time-series of remote sensing data and the random forest algorithm.
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Xie, Huaming, Zhang, Weiqing, Wu, Qianjiao, Zhang, Ting, Zhou, Chukun, and Chen, Zixian
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RANDOM forest algorithms , *REMOTE sensing , *DATA extraction , *TOBACCO , *SMOKING statistics , *SUPPORT vector machines - Abstract
Efficiently obtaining tobacco planting area is significant for rationally allocating tobacco resources and realizing the balance between supply and demand. However, tobacco fields are characterized by fragmentation in hilly areas, which brings various challenges to extracting the tobacco planting area. A 16-square-kilometre tobacco planting region in Xuancheng City, Anhui Province, China, was selected as the study area in this paper. First, the single-temporal full-feature sets (STFFS), a time-series full-feature set (TFFS) and a time-series optimal-feature set (TOFS) were constructed from multi-period Sentinel-2 images, respectively. Then, we applied Random Forest (RF), Support Vector Machine (SVM), Neural Network Classification (NNC) and Maximum Likelihood Classification (MLC) to classify the feature sets and compare the classification accuracy. The experimental results demonstrate that: (1) The best growth stage of tobacco remote sensing recognition is the mulching film phase of the spherical plant stage (ST1 period). (2) The classification accuracy indicates that TOFS outperforms the STFFS. (3) TOFS can still maintain the same classification accuracy as TFFS with fewer feature dimensions. (4) The RF classifier has great stability and robustness, which achieve an overall accuracy (OA) of 90.65%, 93.50%, and 93.09% based on the feature sets of ST1 period, TFFS, and TOFS, respectively. [ABSTRACT FROM AUTHOR]
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- 2024
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4. Assessing the effectiveness of UAV data for accurate coastal dune habitat mapping.
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Cruz, Charmaine, O'Connell, Jerome, McGuinness, Kevin, Martin, James R., Perrin, Philip M., and Connolly, John
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SAND dunes ,MACHINE learning ,HABITATS ,RANDOM forest algorithms ,GROWING season ,REMOTE sensing - Abstract
Coastal dunes are considered some of the most threatened and vulnerable habitats in the European Union. Mapping the spatial distribution of these habitats is an essential task for their conservation. Advances in Unoccupied Aerial Vehicles (UAVs) facilitate the flexible acquisition of high-resolution imagery for identifying detailed spatial distributions of habitats within dune systems. This study aimed to assess the effectiveness of UAV remote sensing for mapping these habitat types. Specifically, we determined the impact of temporally acquired UAV-derived spectral and topographic information on classification accuracy. The work combined the multi-temporal UAV imagery with field observation data and used the Random Forest machine learning algorithm to classify dune habitats. Results showed that using multi-temporal UAV imagery increased classification accuracy compared to using uni-temporal UAV imagery (92.37% vs. 84.09%, respectively). Also, including topographic information consistently improved accuracy, regardless of the number of image sets used (the highest accuracy increased from 84.81% to 92.57% for a uni-temporal model). Temporal analyses showed that the data acquired in the middle period of the growing season were better than those acquired in the early or late periods. The methodology presented here demonstrates the potential of using UAV data for detailed mapping and monitoring of habitat types. [ABSTRACT FROM AUTHOR]
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- 2023
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5. A large-scale disturbance mapping ensemble through data-driven regionalization.
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Bueno, Inacio Thomaz, Hird, Jennifer, McDermid, Gregory John, Galvão, Lênio Soares, and Acerbi Júnior, Fausto Weimar
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VEGETATION mapping , *VEGETATION monitoring , *RANDOM forest algorithms , *ECOLOGICAL disturbances , *COMPUTING platforms - Abstract
Mapping and monitoring disturbances in vegetation over large areas demand reliable approaches and accurate end-user maps. Methods and algorithms have been developed to meet satisfactory disturbance map accuracies, and the combination of multiple approaches has shown promise as a reliable alternative to any single method. However, extracting meaningful disturbance information from these combined methods is still challenging. Data variance from environmental conditions and disturbance drivers leads to spatial-temporal heterogeneity in land surfaces over large areas, which results in mapping errors. We evaluated the effectiveness of ensemble classification and data-driven regionalization for mapping vegetation disturbances at a broad scale. Using Google's Earth Engine cloud computing platform, our ensemble approach combines multispectral LandTrendr outputs reflecting preliminary disturbance information in a Random Forest model to map disturbances in Minas Gerais, Brazil. We then applied an unsupervised clustering technique to perform data-driven regionalization of our study area using several sources of environmental and anthropogenic information and analysed gains and losses in map accuracies. Our results indicated gains in accuracy by the ensemble method compared to non-ensemble methods of disturbance mapping, which ranged from 7.3 to 29.9% in overall accuracy at the 5% significance level. Data-driven regionalization addressed complexities arising from variability in vegetation types, local climate, and topography across our study area, identifying climate and seasonal metrics as important variables for reducing uncertainties in vegetation disturbance maps. The integration of these techniques has revealed significant potential for increasing map accuracy and has provided important insights into the development of disturbance mapping methods in heterogeneous environments. [ABSTRACT FROM AUTHOR]
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- 2023
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6. Random forest method for analysis of remote sensing inversion of aboveground biomass and grazing intensity of grasslands in Inner Mongolia, China.
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Wang, Shuai, Tuya, Hasi, Zhang, Shengwei, Zhao, Xingyu, Liu, Zhiqiang, Li, Ruishen, and Lin, Xi
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RANDOM forest algorithms , *FOREST biomass , *REMOTE sensing , *GRASSLANDS , *GRAZING , *GRASSLAND restoration , *BIOMASS - Abstract
The quantification of grassland above-ground biomass (AGB) and grazing intensity (GI) and their distribution in space is of great significance to grassland management and eco-conservation. Remote-sensing technology is widely applied, but it is difficult to measure accurately when monitoring GI. In this study, the neural network, random forest and statistical function models of the relationship between Landsat NDVI and AGB were constructed by field survey and literature data collection in Inner Mongolia grassland, China. By comparing the accuracy among the three models, we constructed a remote-sensing retrieving model of grass AGB. We also estimated the grassland AGB during the peak growing season (August) for Inner Mongolia. Frequency histograms were then made to identify AGB thresholds under four GI levels (light or ungrazed, moderate grazing, overgrazing and extreme grazing) for each of three grassland types (meadow steppe, typical steppe and desert steppe). This study shows that the random forest model simulates grass AGB more accurately than other models. The spatial distribution of AGB in Inner Mongolia grasslands showed a tendency of decreasing from southeast to northwest, with an increasing trend in the last 10 years. The four GI levels in 2021 accounted for 18%, 25%, 36% and 21% of the grasslands in Inner Mongolia, respectively, and over the last 10 years the GI first improved and then deteriorated. This study provides a guideline to remote monitoring for grassland AGB and GI, and supplies scientific support for sustainable management and grassland restoration of large-scale grasslands. [ABSTRACT FROM AUTHOR]
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- 2023
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7. Large dam candidate region identification from multi-source remote sensing images via a random forest and spatial analysis approach.
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Jing, Min, Li, Ning, Li, SiCong, Ji, Chen, and Cheng, Liang
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RANDOM forest algorithms , *REMOTE sensing , *DAMS , *ACQUISITION of data , *MICROWAVE remote sensing - Abstract
The extraction of large dam candidate regions is critical for broad-scale efforts to rapidly detect large-area dams. The framework proposed in this paper attempts to combine random forest classification models and spatial analysis methods with large dam candidate area extraction methods for large-scale areas. First, we studied the combination of optical, microwave, texture, and topographic features of the dam and constructed a multisource remote sensing and topographic feature vector of the dam. Secondly, we constructed random forest classifiers in different study areas and evaluate their performance. Then we explored the geographic characteristics of the dams and their relationships with other features. Finally, we introduced the spatial analysis method to constrain the large dam candidate area. The proposed framework was tested in a total area of 968,533 km2 in five countries and achieved promising results, which constrained the candidate area to less than 1.06% of the total area. We calculated the completeness rate of large dams using the multi-source dam datasets. The framework achieved a completeness rate of more than 97.62%. Our results show that the entire framework is reliable for automated and fast large dam candidate area acquisition based on data from open remote sensing products. [ABSTRACT FROM AUTHOR]
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- 2023
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8. Combining PlanetScope and Sentinel-2 images with environmental data for improved wheat yield estimation.
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Farmonov, Nizom, Amankulova, Khilola, Szatmári, József, Urinov, Jamol, Narmanov, Zafar, Nosirov, Jakhongir, and Mucsi, László
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WHEAT , *REMOTE-sensing images , *RANDOM forest algorithms , *CROP yields , *COMBINES (Agricultural machinery) - Abstract
Satellite images are widely used for crop yield estimation, but their coarse spatial resolution means that they often fail to provide detailed information at the field scale. Recently, a new generation of high-resolution satellites and CubeSat platforms has been launched. In this study, satellite data sources including PlanetScope and Sentinel-2 were combined with topographic and climatic variables, and the improvement in wheat yield estimation was evaluated. Wheat yield data from a combine harvester were used to train and validate a yield estimation model based on random forest regression. Nine vegetation indices (NDVI, GNDVI, MSAVI2, MTVI2, MTCI, reNDVI, SAVI, EVI and WDVI) and spectral bands were tested. During the model training, the Sentinel-2 data realized a slightly higher estimation accuracy than the PlanetScope data. However, combining environmental data with the PlanetScope data realized the highest estimation accuracy. For the validated models, adding the topographic and climatic datasets to the satellite data sources improved the estimation accuracy, and the results were slightly better with the Sentinel-2 data than with the PlanetScope data. Observation data of the milk and dough stages provided the highest estimation accuracy of the wheat yield at 210–225 days after sowing. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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9. Rapid method for yearly LULC classification using Random Forest and incorporating time-series NDVI and topography: a case study of Thanh Hoa province, Vietnam.
- Author
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Trong Dieu Hien Le, Luan Hong Pham, Quang Toan Dinh, Nguyen Thi Thuy Hang, and Thi Anh Thu Tran
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RANDOM forest algorithms , *NORMALIZED difference vegetation index , *SUPERVISED learning , *MACHINE learning , *VEGETATION patterns , *TOPOGRAPHY , *TIME series analysis - Abstract
Land-use and land-cover (LULC) mapping in the complex area is a challenging task due to the mixed vegetation patterns, and rough mountains with fast-flowing rivers. In Vietnam, LULC update is not frequently. In this study, we applied a supervised machine learning (Random forest—RF) approach to mapping LULC in Thanh Hoa province, Vietnam from 2011 to 2015 utilizing multitemporal Normalized Difference Vegetation Index (NDVI) data from MODIS, combined with topographic features. Random forest classification (RFC) reached a total prediction accuracy of 91% and Kappa coefficient (K) of 0.89 across eight LULCs. Besides, the results showed that the features extracted from time-series NDVI comprising the mean of yearly NDVI, the sum of NDVI, and the topography were the important variables controlling the LULC classification. For similar studies on the distribution of LULC, the method proposed in this study could be helpful. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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10. Accuracy comparison of various remote sensing data in lithological classification based on random forest algorithm.
- Author
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Yantao Xi, Mohamed Taha, Abdallah M., Anqi Hu, and Xianbin Liu
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RANDOM forest algorithms , *REMOTE sensing , *MULTISPECTRAL imaging , *PRINCIPAL components analysis , *DATA mapping , *CLASSIFICATION - Abstract
This study compares the capability of Landsat-8, Sentinel-2, and ASTER multispectral data in mapping lithological units in the Bukadaban Peak, China, by evaluating the performance of the Random Forest (RF) classifier. Moreover, the study assesses the importance of remote sensing original bands and the bands derived from enhancement techniques such as Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) to the classification process. Results revealed that RF selected PC8, PC9, PC12, and MNF1 as the most important features in the Sentinel-2 dataset. Several MNF bands of ASTER were more important than the original and PC bands. The original bands were the strongest predictors in Landsat-8 dataset, while PC2, PC5, and MNF5 were relatively significant. Sentinal-2 and ASTER datasets achieved very similar classification accuracy but outperformed Landsat-8 dataset. ASTER dataset yielded the highest overall accuracy 81.8%, which is 0.18% higher than Sentinel-2 and 3.86% higher than Landsat-8. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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11. Digital mapping of soil organic carbon stocks in the forest lands of Dominican Republic.
- Author
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Duarte, Efraín, Zagal, Erick, Barrera, Juan A., Dube, Francis, Casco, Fabio, and Hernández, Alexander J.
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DIGITAL soil mapping ,FORESTS & forestry ,MULTISPECTRAL imaging ,CARBON in soils ,REMOTE sensing ,RANDOM forest algorithms ,TOPSOIL ,FOREST soils - Abstract
Mapping the spatial distribution of soil organic carbon (SOC) in lands covered by tropical forests is important to understand the relationship and dynamics of SOC in this type of ecosystem. In this study, the Random Forest (RF) algorithm was used to map SOC stocks of topsoil (0–15 cm) in forest lands of the Dominican Republic. The methodology was developed using geospatial datasets available in the Google Earth Engine (GEE) platform combined with a set of 268 soil samples. Twenty environmental covariates were analyzed, including climate, topography, and vegetation. The results indicate that Model A (combining all 20 covariates) was only marginally better than Model B (combining topographic and climatic covariates), and Model C (only combining multispectral remote sensing data derived from Landsat 8 OLI images). Model A and Model B yielded SOC mean values of 110.35 and 110.87 Mg C ha
−1 , respectively. Model A reported the lowest prediction error and uncertainty with an R2 of 0.83, an RMSE of 35.02 Mg C ha−1 . There was a strong dependence of SOC stocks on multispectral remote sensing data. Therefore, multispectral remote sensing proved accurate to map SOC stocks in forest ecosystems in the region. [ABSTRACT FROM AUTHOR]- Published
- 2022
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12. Estimating feature extraction changes of Berkelah Forest, Malaysia from multisensor remote sensing data using and object-based technique.
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Rozali, Syaza, Abd Latif, Zulkiflee, Adnan, Nor Aizam, Hussin, Yousif, Blackburn, Alan, and Pradhan, Biswajeet
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REMOTE sensing , *SUPPORT vector machines , *MULTISPECTRAL imaging , *RAIN forests , *RANDOM forest algorithms , *FEATURE extraction , *LANDSAT satellites - Abstract
The study involves an object-based segmentation method to extract feature changes in tropical rainforest cover using Landsat image and airborne LiDAR (ALS). Disturbance event that are represents the changes are examined by the classification of multisensor data; that is a highly accurate ALS with different resolutions of multispectral Landsat image. Disturbance Index (DI) derived from Tasseled Cap Transformation, Normalized Difference Vegetation Index (NDVI), and the ALS height are the variables for object-based segmentation process. The classification is categorized into two classes; disturbed and non-disturbed forest cover using Nearest Neighbor (NN), Random Forest (RF) and Support Vector Machine (SVM). The overall accuracy ranging from 88% to 96% and kappa ranging from 0.79 to 0.91. Mcnemar's test p-value (<0.05) is applied to check the classification for each method used which is RF 0.03 and SVM 0.01. The accuracy increases when the integration of ALS in Landsat image (SpectralLandsat; and SpectralLandsat + HeightALS). [ABSTRACT FROM AUTHOR]
- Published
- 2022
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13. Retrospective analysis and version improvement of the satellite-based drought composite index. A semi-arid Tensift-Morocco application.
- Author
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Hanadé Houmma, Ismaguil, El Mansouri, Loubna, Hadria, Rachid, Emran, Anas, and Chehbouni, Abdelghani
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RANDOM forest algorithms , *RETROSPECTIVE studies , *AGRICULTURAL statistics , *SOIL moisture , *REMOTE sensing - Abstract
This paper aims to offer an improved version of the new composite drought monitoring index (CDMI) and to test its applicability in the context of Tensift watershed in Morocco. A synergistic approach incorporating the remote sensing techniques, hydrometeorological data, simulated data and agricultural statistics was used for this purpose. After assessing the performance of CDMI, estimated Soil Moisture Anomaly Indicator (ISMA) was processed, validated and incorporated to the composite model. Random Forest algorithm was used to determine the weight of composite model components. Apart from comparative mapping, Pearson's correlation statistical analysis, linear regression and dependency tests were used to assess the performance of the improved composite model (CDMIa_RF). The result show that CDMIa_RF is better correlated with several indices such as: the Standardized Precipitation Index (SPI), (R2=0.74); Hydrological Drought Index (R2=0.70); grain productivity (R2=0.70), CDMI (R2=0.95), Vegetation Health Index (VHI), (R2=0.87), and Normalized Vegetation Supply Water Index (NVSWI), (R2= 0.85). [ABSTRACT FROM AUTHOR]
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- 2022
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14. Estimating soil organic carbon levels in cultivated soils from satellite image using parametric and data-driven methods.
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Koparan, M. H., Rekabdarkolaee, H. M., Sood, K., Westhoff, S. M., Reese, C. L., and Malo, D. D.
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REMOTE-sensing images , *SOILS , *CARBON in soils , *REGRESSION trees , *RANDOM forest algorithms , *PRAIRIES - Abstract
Soil organic carbon (SOC) is one of the key soil components for cultivated soils. SOC is regularly monitored and mapped to improve the quality, health, and productivity of the soil. However, traditional SOC-level monitoring is expensive for land managers and farmers. Estimating SOC using satellite imagery provides an easy, efficient, and cost-effective way to monitor surface SOC levels. The objective of this study was to estimate the surface SOC distribution in selected soils of Major Land Resource Areas (MLRA), 102A (Rolling Till Plain, Brookings County, SD), and 103 (Central Iowa and Minnesota Till Prairies, Lac qui Parle County, MN), using satellite imagery with different resolutions (Landsat 8 and PlanetScope). The dominant soils in the study area are Haplustolls, Calciustolls, and Endoaquolls, which are formed in silty sediments, local silty alluvium, and till. Landsat 8 and PlanetScope spectral bands were used to develop SOC prediction models. Parametric and data-driven methods were employed to predict the SOC. Multiple linear regression and Linear Spatial Mixed Model (LSMM) were used on the Landsat 8 and PlanetScope data. In addition to the parametric models, Regression Trees and Random Forest were also employed on both satellite data. The results showed that reduced LSMM provided the lowest RMSE, which are 0.401 and 0.367 for Landsat 8 and PlanetScope, respectively. Furthermore, the random forest has the highest RPD and RPIQ for Landsat 8 (RPD of 2.67 and RPIQ of 2.49) and PlanetScope (RPD of 2.85 and RPIQ of 3.7). In all cases, models obtained from PlanetScope are better than those obtained from Landsat 8. [ABSTRACT FROM AUTHOR]
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- 2022
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15. Species level classification of Mediterranean sparse forests-maquis formations using Sentinel-2 imagery.
- Author
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Demirbaş Çağlayan, Semiha, Leloglu, Ugur Murat, Ginzler, Christian, Psomas, Achilleas, Zeydanlı, Uğur S., Bilgin, C. Can, and Waser, Lars T.
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GUERRILLAS , *RANDOM forest algorithms , *VEGETATION dynamics , *FEATURE selection , *REMOTE sensing , *ECOSYSTEMS - Abstract
Essential forest ecosystem services can be assessed by better understanding the diversity of vegetation, specifically those of Mediterranean region. A species level classification of maquis would be useful in understanding vegetation structure and dynamics, which would be an indicator of degradation or succession in the region. Although remote sensing was regularly used for classification in the region, maquis are simply represented as one to three categories based on density or height. To fill this gap, we test the capability of Sentinel-2 imagery, together with selected ancillary variables, for an accurate mapping of the dominant maquis formations. We applied Recursive Feature Selection procedure and used a Random Forest classifier. The algorithm is tested using ground truth collected from site and reached 78% and 93% overall accuracy at species level and physiognomic level, respectively. Our results suggest species level characterization of dominant maquis is possible with Sentinel-2 spatial resolution. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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