Geocarto International

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ISSN / EISSN : 1010-6049 / 1752-0762
Published by: Informa UK Limited (10.1080)
Total articles ≅ 2,628
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Latest articles in this journal

Published: 5 July 2022
Geocarto International pp 1-30; https://doi.org/10.1080/10106049.2022.2097482

Abstract:
In the present study, the temporal and spatial dynamics of the post-fire recovery of different Mediterranean vegetation types during the three years after the fire event were analyzed, according to different fire severity categories, integrating the use of Synthetic Aperture Satellite Radar (SAR) (Sentinel-1) and optical (Sentinel-2) image time series. The results showed that Mediterranean forest species and shrub/herbaceous species are adapted to fire, with high efficiency in restoring the vegetation cover. Differently, the ecological vulnerability of non-native eucalyptus plantations was found in a lower recovery trend during the observation period. The use of optical short-wave infrared (SWIR) and SAR C-band-based data revealed that some ecological characteristics, such as the woody biomass and structure, recovered at slower rates, comparing to those suggested by using near-infrared (NIR) and red-edge data. An optimized burn recovery ratio (BRR) was proposed to estimate and map the spatial distribution of the degree of vegetation recovery.
, Amanda Maishella, Wahyu Lazuardi, Faaris Hizba Muhammad
Published: 5 July 2022
Geocarto International pp 1-20; https://doi.org/10.1080/10106049.2022.2096122

Abstract:
Seagrass percent cover is a crucial and influential component of the biophysical characteristics of seagrass beds and is a key parameter for monitoring seagrass conditions. Therefore, the availability of seagrass percent cover maps greatly assists in sustainable coastal ecosystem management. This research aimed to assess the consistency of PlanetScope imagery for seagrass percent cover mapping using two study areas, namely Parang Island and Labuan Bajo, Indonesia. Assessing the consistency of the PlanetScope imagery performance in seagrass percent cover mapping helps understand the effects of variations in the image quality on its performance in monitoring changes in seagrass cover. Percent cover maps were derived using object-based image analysis (image segmentation and random forest) and pixel-based random forest algorithm. Accuracy assessment and consistency analysis were conducted on the basis of the following three approaches: overall accuracy consistency, agreement percentage and consistent pixel locations. Results show that PlanetScope images can fairly consistently map seagrass percent cover for a specific area across different dates. However, these images produced different levels of accuracy when used for mapping in seagrass meadows with various characteristics and benthic cover complexities. The mapping accuracy (OA–overall accuracy) and consistency (AP–agreement percentage) in patchy seagrass meadows (Parang Island, mean OA 18.4%–38.6%, AP 44.1%–70.3%) are different from those in continuous seagrass meadows (Labuan Bajo, OA 43.0%–56.2%, and AP 41.8%–55.8%). Moreover, PlanetScope images are consistent when used for mapping seagrasses with low and high percent covers but strive to obtain good consistency for medium percent cover due to the combination of seagrass and non-seagrass in a pixel. Furthermore, images with relatively similar image acquisition conditions (i.e. winds, aerosol optical depth, signal-to-noise ratio, and sunglint intensity) produce better consistency. The OA is related to the image acquisition conditions, whilst the AP is related to variation in these conditions. Nevertheless, PlanetScope is still the best high spatial resolution image that provides daily acquisition and is highly beneficial for various applications in tropical areas with persistent cloud coverage.
, Evelyn Joan Momo, Sarvia Filippo, Borgogno-Mondino Enrico
Published: 5 July 2022
Geocarto International pp 1-22; https://doi.org/10.1080/10106049.2022.2098388

Abstract:
Approaches based on multitemporal analysis of optical-retrieved vegetation index time series were successfully applied to describe forest disturbances like forest fires; conversely, only few works make use of multitemporal Synthetic Aperture Radar (SAR) data. In this work, a multi-temporal approach based on Sentinel-1 data (S1) is proposed based on CR polarimetric index to monitor forest canopy along the considered period (2016-2019) preceding and following an important fire event occurred in the Piemonte Region (NW Italy) in November 2017. The Pettitt test, applied to the polarimetric index time series, was used for testing fire occurrence date and map burned areas (795 ha) resulting in user’s accuracy of burned area equal to 88%. A trend analysis was also conducted, on “burned” pixels only, to describe tree canopy damage and strength of the consequent recovery process at pixel level using linear trend slope values of cross ratio polarimetric index time series. Finally, a k-means cluster analysis was applied to define classes having the same ecological behavior with respect to two different criteria: one aimed at mapping type and intensity of damage and a second one aimed at describing the ecological behaviour in terms of resistance and resilience of burned patches. In the study area, the cluster layer called forest damage map classifies about the 22% of burned area as characterized by an early high severity whiled the residual by moderate-low severity levels. The second cluster layer called ecological response map defined the 61% of the burned area as resistant forests, the 20% as resilient forests and the 19% as increasing forest zones. All maps were generated with the aim of supporting post-fire assessment and management with free satellite SAR data.
Chun Liu, , Hangbin Wu, Youyuan Li, Zhanyong Fan
Published: 1 July 2022
Geocarto International pp 1-18; https://doi.org/10.1080/10106049.2022.2097321

Abstract:
Street trees provide a multitude of benefits but may cause hidden dangers to traffic safety under inaccurate maintenance. Mobile laser scanning (MLS) system can efficiently provide high-precision urban environment information. This paper aims to develop a clearance pipeline-based method to obtain street tree maintenance information by analysing the spatial relationship between street trees and the clearance pipeline using MLS data. Our method consists of two steps: pipeline construction and maintenance information detection. The proposed approach can map street tree maintenance information and serve as an urban management tool. An urban block with a total length of 6.4km was selected as the case area. The results indicated our method can effectively detect obstructive objects and pruning the street trees identified by our method can effectively improve the visibility of traffic signs. Our study shows the potential of MLS data in urban facilities management and aims to provide a safer road environment.
, , Sara Zamzam, Djamel Boubaya
Published: 1 July 2022
Geocarto International pp 1-31; https://doi.org/10.1080/10106049.2022.2097481

Abstract:
Prospecting and exploring minerals present major challenges in tectonically complex regions for sustainable development as in Northeastern Algeria. This area is promising for its mineral potential, especially the metallogenic province "The Diapiric Zone". This study concerns mapping and predicting potential polymetallic mineralization locations by integration of remote sensing, gravity, and magnetic datasets. Several enhancement and processing methods have been applied on Landsat8_OLI and ASTER_1T remote sensed data to reduce uncertainty for achieving the best detection of hydrothermal alteration zones and lithological mapping. Furthermore, the Centre for Exploration Targeting grid analysis technique, the contact occurrence density and entropy orientation tools were employed on ground-gravity and aeromagnetic data to understand and visualize the pathways for hydrothermal fluids circulation of mineral deposits. The polymetallic mineralization prospective areas were produced using a logistic regression model on the resulting multifactor. High zones of lead-zinc cover most the area that has been confirmed by field investigation.
Shang Tian, Hongwei Guo, , Xiaotong Zhu, Zijie Zhang
Published: 1 July 2022
Geocarto International pp 1-19; https://doi.org/10.1080/10106049.2022.2097320

Abstract:
Atmospheric correction (AC) is a crucial step in the quantitative analysis of inland and coastal waters. In this study, the performances of six water-based AC methods (SeaDAS, ACOLITE-DSF/EXP, C2RCC, iCOR) were evaluated by using multiple global datasets (N = 139). Four evaluation strategies were applied including spectral similarity and Chlorophyll-a retrieval. The results showed that SeaDAS and ACOLITE-DSF performed the best in terms of analytical match, band ratios, Chlorophyll-a retrieval, and spectral similarity. SeaDAS had the lowest RMSE in the blue-green bands and showed good consistency across the spectral with the lowest median spectral angle of 7°. It should be noted that ACOLITE-DSF outperforms SeaDAS in high turbidity waters. SeaDAS coupled with Chlorophyll-a retrieval algorithms of OC3 and Clark had the lowest RMSE, which showed the advantage of SeaDAS in Chlorophyll-a retrieval. This study provides scientific basis for choosing AC methods of Landsat-8 data for aquatic environment monitoring.
, Brahim Elmoutchou, Abdelouahed El Ouazani Touhami, Mustapha Namous, Riyaz Ahmad Mir
Published: 1 July 2022
Geocarto International pp 1-23; https://doi.org/10.1080/10106049.2022.2097322

Abstract:
The coastline between Tetouan and Bou Ahmed in the northernmost Rif of Morocco and its hinterland has become immensely hazardous due to frequent triggering of diversified landslides from last two decades. This paper describes the potential application of a set of multisource data and the GIS platform for zoning and identifying anomalous areas prone to landsliding and its associated landslide hazards. For this purpose, Information value (IV), Statistical index SI (Wi), Weighting factors (WF) and Evidential belief function (EBF) models have been used in this study. Eleven conditioning factors such as elevation, slope, aspect, curvature, shaded/relief, proximity to streams, proximity to faults, proximity to roads, land use, lithology, annual rainfall and an inventory of 905 unstable spots were used to develop the spatial database for landslides susceptibility mapping (LSM). The factors have been used after a test of multi-collinearity. The Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) methods were used for validation of the LSM. The AUC results showed good prediction accuracy for all models with a prediction rate of 78% (IV), 77% (SI), 73% (WF) and 70% (EBF) respectively. However, the results indicated that comparatively, the IV model followed by WI model is more precise and accurate for landslides susceptibility mapping than other models. According to the presented models, about 64% of the study area is located in high to very high landslide susceptible zone. The findings presented in this study are imperatively valuable especially wherein large development projects and land use planning activities are going on.
Ru Xiang, , Zhaojin Yan, Abdallah. M. Mohamed Taha, Xiao Xu, Teng Wu
Published: 30 June 2022
Geocarto International pp 1-26; https://doi.org/10.1080/10106049.2022.2096699

Abstract:
Satellites provide global long-time series of spatio-temporal continuous CO2 observations. However, it is difficult to be applied to the study of small-scale carbon cycle because of its low spatial resolution. In this paper, the Greenhouse Gases Observing SATellite (GOSAT) XCO2 data are super-resolution reconstructed using bicubic interpolation, which improved the spatial resolution from 2.5° to 0.5°. CO2 measurements from ten selected TCCON sites are used to compare with the reconstructed GOSAT. Further, the high accuracy Orbiting Carbon Observatory-2 (OCO-2) data analysed by the combination of geographical grid statistics and kriging is used to evaluate the reconstructed data. The results show that compared with the original GOSAT data, the reconstructed GOSAT data not only improves the spatial resolution but also has little loss of the average accuracy. The mean error of original data has significant seasonal fluctuations with a peak from February to March and a trough from June to July.
, Rustam Pshegusov
Published: 30 June 2022
Geocarto International pp 1-18; https://doi.org/10.1080/10106049.2022.2096701

Abstract:
This paper presents an approach to studying rangeland degradation by using remote sensing data, geographic information systems, and ecological niche theory. The role of environmental factors and land use in the spatial distribution of degraded rangelands in the Central Caucasus was assessed. Degradation stages were modelled in R using ENVIREM predictors, VIF test to select noncorrelated variables, ENMeval package to select the optimal model parameters, and the Maxent method to develop distribution models in the dismo package. Selected models had AUCtest, AUCtrain and CBI values close to 1, deltaAICc and AUCdiff values close to 0, and quite low AICc values. Environmental predictors of climate type (Thornthwaite aridity index and Emberger’s pluviothermic quotient) were explanatory variables for least disturbed rangelands, while topography (Terrain roughness index) largely explained the distribution of most disturbed grasslands. Quantitative (Schoener’s D) and graphical (Kernel density estimation, analysis of predictive maps) assessment revealed a significant overlap of ‘ecological niches’ and potential ranges of grasslands at different degradation stages, which indirectly supports the hypothesis of the important role of overgrazing in their degradation. Livestock management is likely to help restore disturbed mountain meadow steppes to steppe grasslands. Restoration of the arid shrub ecosystems to steppe grasslands or meadow steppes probably requires additional agricultural practices.
Published: 30 June 2022
Geocarto International pp 1-24; https://doi.org/10.1080/10106049.2022.2096702

Abstract:
Flooding is one of the most challenging and important natural disasters to predict, it is becoming more frequent and more intense. The study area is badly damaged by devastating flood in 2015. We assessed the flood susceptibility to northern coastal area of Tamil Nadu using various machine learning algorithms such as Gradient Boosting Machine (GBM), XGBoost (XGB), Rotation Forest (RTF), Support Vector Machine (SVM), and Naive Bayes (NB). Google Earth Engine (GEE) is used to demarcate flooded areas using Sentinel-l and other multi-source geospatial data to generate influential factors. Recursive Feature Elimination (RFE) removes weak factors in this study. The flood susceptibility resultant map is classified into five classes very low, low, moderate, high, and very high. The GBM algorithm attained high classification accuracy with an area under the curve (AUC) value of 92%. The study area is urbanized and vulnerable identifying flood inundation useful for effective planning and implementation.
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