Collect Earth: Land Use and Land Cover Assessment through Augmented Visual Interpretation

Bey, Adia; Díaz, Alfonso Sánchez-Paus; Maniatis, Danae; Marchi, Giulio; Mollicone, Danilo; Ricci, Stefano; Bastin, Jean-François; Moore, Rebecca; Federici, Sandro; Rezende, Marcelo; Patriarca, Carlo; Turia, Ruth; Gamoga, Gewa; Abe, Hitofumi; Kaidong, Elizabeth; Miceli, Gino

doi:10.3390/rs8100807

Cited by 220 publications

(130 citation statements)

References 27 publications

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“…At present, we are only beginning to realize this potential, with current applications largely focused on land-cover and land-use mapping and change, as well as open-surface water mapping (e.g., [10,20,21]). One area of study and monitoring that could benefit immensely from this new shift in the geospatial sciences is wetland mapping.…”

Section: The Need For Comprehensive Wetland Mapping and Monitoring Prmentioning

confidence: 99%

Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping

et al. 2017

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Modern advances in cloud computing and machine-leaning algorithms are shifting the manner in which Earth-observation (EO) data are used for environmental monitoring, particularly as we settle into the era of free, open-access satellite data streams. Wetland delineation represents a particularly worthy application of this emerging research trend, since wetlands are an ecologically important yet chronically under-represented component of contemporary mapping and monitoring programs, particularly at the regional and national levels. Exploiting Google Earth Engine and R Statistical software, we developed a workflow for predicting the probability of wetland occurrence using a boosted regression tree machine-learning framework applied to digital topographic and EO data. Working in a 13,700 km 2 study area in northern Alberta, our best models produced excellent results, with AUC (area under the receiver-operator characteristic curve) values of 0.898 and explained-deviance values of 0.708. Our results demonstrate the central role of high-quality topographic variables for modeling wetland distribution at regional scales. Including optical and/or radar variables into the workflow substantially improved model performance, though optical data performed slightly better. Converting our wetland probability-of-occurrence model into a binary Wet-Dry classification yielded an overall accuracy of 85%, which is virtually identical to that derived from the Alberta Merged Wetland Inventory (AMWI): the contemporary inventory used by the Government of Alberta. However, our workflow contains several key advantages over that used to produce the AMWI, and provides a scalable foundation for province-wide monitoring initiatives.

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Section: The Need For Comprehensive Wetland Mapping and Monitoring Prmentioning

confidence: 99%

Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping

et al. 2017

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“…Therefore, optical data from different periods have been used to assess long-term LULCC. In order to obtain complete spatial coverage of the Kilombero catchment and to account for intra-annual water and land use dynamics, all available Landsat scenes from a three-year epoch around 1994, 2004, and 2014 were subjected to the calculation of temporal metrics [45]. This method follows the assumption that images of dry periods with low water tables as well as rainy season images with high water tables are included.…”

Section: Lulc Changes At the Catchment Scalementioning

confidence: 99%

“…Similar methodologies have been used in other wetland monitoring projects [44], and the latest version of it is currently being applied over 25 wetlands covering a wide range of biogeographic regions. Validation was performed using 400 randomly distributed points in CollectEarth [45]. We calculated LULC area estimations for each class for each time step, following the recommendations developed by Olofsson et al (2014) [46].…”

Section: Lulc Changes In the Kilombero Floodplainmentioning

confidence: 99%

Sustainability in the Food-Water-Ecosystem Nexus: The Role of Land Use and Land Cover Change for Water Resources and Ecosystems in the Kilombero Wetland, Tanzania

et al. 2017

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Land Use Land Cover Change (LULCC) has a significant impact on water resources and ecosystems in sub-Saharan Africa (SSA). On the basis of three research projects we aim to describe and discuss the potential, uncertainties, synergies and science-policy interfaces of satellite-based integrated research for the Kilombero catchment, comprising one of the major agricultural utilized floodplains in Tanzania. LULCC was quantified at the floodplain and catchment scale analyzing Landsat 5 and Sentinel 2 satellite imagery applying different adapted classification methodologies. LULC maps at the catchment scale serve as spatial input for the distributed, process-based ecohydrological model SWAT (Soil Water Assessment Tool) simulating the changes in the spatial and temporal water balance in runoff components caused by LULCC. The results reveal that over the past 26 years LULCC has significantly altered the floodplain and already shows an impact on the ecosystem by degrading the existing wildlife corridors. On the catchment scale the anomalies of the water balance are still marginal, but with the expected structural changes of the catchment there is an urgent need to increase the public awareness and knowledge of decision makers regarding the effect of the relationship between LULCC, water resources and environmental degradation.

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“…In the past decades, researchers are inclined to employ supervised classification methods to obtain LULC maps [18]. However, despite the need for substantial expert inputs, these algorithms are not able to account for the variation in imaging dates, image quality, vegetation growth, weather conditions and cloud cover [19].…”

Section: Remote-sensing For Analyzing Lulc Changementioning

confidence: 99%

Mapping Typical Urban LULC from Landsat Imagery without Training Samples or Self-Defined Parameters

Wang

Zhong

et al. 2017

Remote Sensing

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Abstract:Land use/land cover (LULC) change is one of the most important indicators in understanding the interactions between humans and the environment. Traditionally, when LULC maps are produced yearly, most existing remote-sensing methods have to collect ground reference data annually, as the classifiers have to be trained individually in each corresponding year. This study presented a novel strategy to map LULC classes without training samples or assigning parameters. First of all, several novel indices were carefully selected from the index pool, which were able to highlight certain LULC very well. Following this, a common unsupervised classifier was employed to extract the LULC from the associated index image without assigning thresholds. Finally, a supervised classification was implemented with samples automatically collected from the unsupervised classification outputs. Results illustrated that the proposed method could achieve satisfactory performance, reaching similar accuracies to traditional approaches. Findings of this study demonstrate that the proposed strategy is a simple and effective alternative to mapping urban LULC. With the proposed strategy, the budget and time required for remote-sensing data processing could be reduced dramatically.

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Collect Earth: Land Use and Land Cover Assessment through Augmented Visual Interpretation

Cited by 220 publications

References 27 publications

Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping

Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping

Sustainability in the Food-Water-Ecosystem Nexus: The Role of Land Use and Land Cover Change for Water Resources and Ecosystems in the Kilombero Wetland, Tanzania

Mapping Typical Urban LULC from Landsat Imagery without Training Samples or Self-Defined Parameters

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