Coladello et al., 2020 - Google Patents
Macrophytes' abundance changes in eutrophicated tropical reservoirs exemplified by Salto Grande (Brazil): Trends and temporal analysis exploiting Landsat remotely …Coladello et al., 2020
- Document ID
- 9546075323871882127
- Author
- Coladello L
- Galo M
- Shimabukuro M
- Ivánová I
- Awange J
- Publication year
- Publication venue
- Applied geography
External Links
Snippet
River damming for electric power production generally triggers a set of anthropic activities that strongly impact on aquatic ecosystem, especially in small reservoirs located in urbanized and industrialized areas. Among the possible adverse effects is the over …
- 230000002123 temporal effect 0 title abstract description 41
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
- G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
- G06K9/00657—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas of vegetation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/20—Drawing from basic elements, e.g. lines or circles
- G06T11/206—Drawing of charts or graphs
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30587—Details of specialised database models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/05—Geographic models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/06—Investment, e.g. financial instruments, portfolio management or fund management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
- G06T3/40—Scaling the whole image or part thereof
- G06T3/4053—Super resolution, i.e. output image resolution higher than sensor resolution
- G06T3/4061—Super resolution, i.e. output image resolution higher than sensor resolution by injecting details from a different spectral band
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Wagner et al. | Using the U‐net convolutional network to map forest types and disturbance in the Atlantic rainforest with very high resolution images | |
Vogeler et al. | Extracting the full value of the Landsat archive: Inter-sensor harmonization for the mapping of Minnesota forest canopy cover (1973–2015) | |
Zhu et al. | Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative | |
Ghosh et al. | Random forest classification of urban landscape using Landsat archive and ancillary data: Combining seasonal maps with decision level fusion | |
Samuel-Rosa et al. | Do more detailed environmental covariates deliver more accurate soil maps? | |
Reschke et al. | Continuous field mapping of Mediterranean wetlands using sub-pixel spectral signatures and multi-temporal Landsat data | |
Lucas et al. | Mapping forest growth and degradation stage in the Brigalow Belt Bioregion of Australia through integration of ALOS PALSAR and Landsat-derived foliage projective cover data | |
Mahdavi et al. | A dynamic classification scheme for mapping spectrally similar classes: Application to wetland classification | |
Hentati et al. | Evaluation of sedimentation vulnerability at small hillside reservoirs in the semi-arid region of Tunisia using the Self-Organizing Map | |
Coladello et al. | Macrophytes’ abundance changes in eutrophicated tropical reservoirs exemplified by Salto Grande (Brazil): Trends and temporal analysis exploiting Landsat remotely sensed data | |
Gemitzi et al. | Land cover and vegetation carbon stock changes in Greece: A 29-year assessment based on CORINE and Landsat land cover data | |
Kumar et al. | Change detection techniques for land cover change analysis using spatial datasets: A review | |
Shafi et al. | Leveraging machine learning and remote sensing to monitor long-term spatial-temporal wetland changes: Towards a national RAMSAR inventory in Pakistan | |
Sharma et al. | Unravelling net primary productivity dynamics under urbanization and climate change in the western Himalaya | |
Mainali et al. | Convolutional neural network for high-resolution wetland mapping with open data: Variable selection and the challenges of a generalizable model | |
Cortes et al. | Estimation of above-ground forest biomass using Landsat ETM+, Aster GDEM and Lidar | |
Wang et al. | Regression-based surface water fraction mapping using a synthetic spectral library for monitoring small water bodies | |
Lemenkova | Using open-source software GRASS GIS for analysis of the environmental patterns in Lake Chad, Central Africa | |
Pham et al. | Multi-spectral remote sensing and GIS-based analysis for decadal land use land cover changes and future prediction using random forest tree and artificial neural network | |
Mallinis et al. | Local-scale fuel-type mapping and fire behavior prediction by employing high-resolution satellite imagery | |
McMahon et al. | A river runs through it: Robust automated mapping of riparian woodlands and land surface phenology across dryland regions | |
Peng et al. | Assessing the potential of multi-source remote sensing data for cropland soil organic matter mapping in hilly and mountainous areas | |
Kagabo et al. | Assessing the impact of Land Use Land Cover changes on land surface temperature over Kigali, Rwanda in the past three decades | |
Varma et al. | Forecasting Land-Use and Land-Cover Change using Hybrid CNN-LSTM Model | |
Freyberg et al. | Idaho Wildfires: Assessing Drought and Fire Conditions, Trends, and Susceptibility to Inform State Mitigation Efforts and Bolster Monitoring Protocol in North-Central Idaho |