Monitoring long-term forest dynamics with scarce data: a multi-date classification implementation in the Ecuadorian Amazon

Santos, Fabián; Meneses, Pablo; Hostert, Patrick

doi:10.1080/22797254.2018.1533793

Cited by 9 publications

(5 citation statements)

References 77 publications

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“…We collected a set of land-cover and land-cover change maps generated biannually for the period 1990–2014 in the NEA. They were generated for previous research to monitor long-term forest dynamics with scarce data [70], reporting an overall accuracy above 70%. Specifically, this approach uses the Landsat surface reflectance time-series product [71] to reduce it into cloud-free biennial composites.…”

Section: 3 Methodsmentioning

confidence: 99%

A geographically weighted random forest approach for evaluate forest change drivers in the Northern Ecuadorian Amazon

2019

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The Tropical Andes region includes biodiversity hotspots of high conservation priority whose management strategies depend on the analysis of forest dynamics drivers (FDDs). These depend on complex social and ecological interactions that manifest on different space–time scales and are commonly evaluated through regression analysis of multivariate datasets. However, processing such datasets is challenging, especially when time series are used and inconsistencies in data collection complicate their integration. Moreover, regression analysis in FDD characterization has been criticized for failing to capture spatial variability; therefore, alternatives such as geographically weighted regression (GWR) have been proposed, but their sensitivity to multicollinearity has not yet been solved. In this scenario, we present an innovative methodology that combines techniques to: 1) derive remote sensing time series products; 2) improve census processing with dasymetric mapping; 3) combine GWR and random forest (RF) to derive local variables importance; and 4) report results based in a clustering and hypothesis testing. We applied this methodology in the northwestern Ecuadorian Amazon, a highly heterogeneous region characterized by different active fronts of deforestation and reforestation, within the time period 2000–2010. Our objective was to identify linkages between these processes and validate the potential of the proposed methodology. Our findings indicate that land-use intensity proxies can be extracted from remote sensing time series, while intercensal analysis can be facilitated by calculating population density maps. Moreover, our implementation of GWR with RF achieved accurate predictions above the 74% using the out-of-bag samples, demonstrating that derived RF features can be used to construct hypothesis and discuss forest change drivers with more detailed information. In the other hand, our analysis revealed contrasting effects between deforestation and reforestation for variables related to suitability to agriculture and accessibility to its facilities, which is also reflected according patch size, land cover and population dynamics patterns. This approach demonstrates the benefits of integrating remote sensing–derived products and socioeconomic data to understand coupled socioecological systems more from a local than a global scale.

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Section: 3 Methodsmentioning

confidence: 99%

A geographically weighted random forest approach for evaluate forest change drivers in the Northern Ecuadorian Amazon

2019

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“…However, Refs. [71][72][73] reported that the area assessed has suffered from deforestation in recent years.…”

Section: Global Product Comparisonmentioning

confidence: 99%

Evaluating the Applicability of Global LULC Products and an Author-Generated Phenology-Based Map for Regional Analysis: A Case Study in Ecuador’s Ecoregions

Rugel

Ochoa

Menéndez

et al. 2023

Land

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An accurate and detailed understanding of land-use change affected by anthropogenic actions is key to environmental policy decision-making and implementation. Although global land cover products have been widely used to monitor and analyse land use/land cover (LULC) change, the feasibility of using these products at the regional level needs to be assessed due to the limitation and biases of generalised models from around the world. The main objective of the present study was to generate regional LULC maps of three target areas located in the main ecoregions of Ecuador at a resolution of 10 m using Google Earth Engine (GEE) cloud-based computing. Our approach is based on (1) Single Date Classification (SDC) that processes Sentinel-2 data into fuzzy rule-driven thematic classes, (2) rule refinement using Visible Infrared Imaging Radiometer Suite (VIIRS) data, and (3) phenology-based synthesis (PBS) classification that combines SDC into LULC based on the occurrence rule. Our results show that the three target areas were classified with an overall accuracy of over 80%. In addition, cross-comparison between the global land cover products and our LULC product was performed and we found discrepancies and inaccuracies in the global products due to the characteristics of the target areas that included a dynamic landscape. Our LULC product supplements existing official statistics and showcases the effectiveness of phenology-based mapping in managing land use by providing precise and timely data to support agricultural policies and ensure food security.

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“…In recent years, multi-sensor data fusion technology has been widely used in the military and civilian fields [23]. In general, according to the different levels of processing of information sources, multi-sensor data fusion has generally been divided into data-layer fusion, feature-layer fusion, and decision-layer fusion [24].…”

Section: Multi-sensor Data Fusion Technologymentioning

confidence: 99%

A novel approach to tool condition monitoring based on multi-sensor data fusion imaging and an attention mechanism

Zeng

Liu

2021

Meas. Sci. Technol.

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Tool wear does great damage to product quality and machining efficiency. In this paper, a new tool condition monitoring (TCM) method based on a multi-sensor data fusion imaging and attention mechanism is proposed to indirectly measure and monitor tool wear. First, the multi-sensor signals collected during the operation of the machine tool are encoded and fused. A novel triangular matrix of angle summation method is proposed to fuse multi-sensor time-series signals at the data layer and to image them as two-dimensional images. The method used here effectively retains the detailed characteristics of the data, and also effectively retains the internal time relationship of the signal. In addition, a deep residual network with a convolution block attention module is used to extract the deep features from the encoded image and identify the wear stage of the tool. The proposed deep-learning network is then applied to TCM. It selects information in the channel and spatial domains based on its attention mechanism, and increases network depth by combining residual blocks to realize deep feature extraction. Compared with traditional machine learning methods, this end-to-end deep learning model does not rely on feature engineering, which requires a lot of expert knowledge and artificial experience. In our experimental study, a public data set was used to train the model, and the results showed that the accuracy of the model proposed in this paper was as high as 94%. To further verify its feasibility and effectiveness, the proposed model was also compared with other methods.

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Monitoring long-term forest dynamics with scarce data: a multi-date classification implementation in the Ecuadorian Amazon

Cited by 9 publications

References 77 publications

A geographically weighted random forest approach for evaluate forest change drivers in the Northern Ecuadorian Amazon

A geographically weighted random forest approach for evaluate forest change drivers in the Northern Ecuadorian Amazon

Evaluating the Applicability of Global LULC Products and an Author-Generated Phenology-Based Map for Regional Analysis: A Case Study in Ecuador’s Ecoregions

A novel approach to tool condition monitoring based on multi-sensor data fusion imaging and an attention mechanism

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