Kappas, Martin W.

[["dc.bibliographiccitation.firstpage","1367"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","International Journal of Advanced Remote Sensing and GIS"],["dc.bibliographiccitation.lastpage","1384"],["dc.bibliographiccitation.volume","4"],["dc.contributor.author","Nguyen, Hong Quang"],["dc.contributor.author","Kappas, Martin W."],["dc.date.accessioned","2019-07-10T08:11:55Z"],["dc.date.accessioned","2020-05-11T14:52:15Z"],["dc.date.available","2019-07-10T08:11:55Z"],["dc.date.available","2020-05-11T14:52:15Z"],["dc.date.issued","2015"],["dc.description.abstract","Accurate estimation of surface runoff (Q) and evapotranspiration (ET) is a challenging task but an important research topic because both Q and ET play vital roles in the study of the hydrological cycle, of climate change, water resources, flood management and so on. In this paper we will present the modeling method to estimate the daily Q and ET for a medium-sized watershed in the tropical region of the North of Vietnam using the Soil and Water Assessment Tool (SWAT) and Bridging Event and Continuous Hydrological (BEACH) models. The models were calibrated and validated for the river discharge for SWAT and evaporation (E) for BEACH in a 12 year period from 2001 to 2012. The simulated ETs by the models were compared with the satellite-based ET of MODIS products. Our simulation results show that the SWAT and BEACH models are capable of satisfactorily reproducing (with the NSE > 0.62 and R 2 > 0.78) the stream-gauged river discharge and the observed E, respectively. Daily ET varied from 0.3 to 14 mm day-1 and was highest from May to August and lowest from December to March. Although the monthly and yearly MODIS ETs were slightly higher than those of SWAT and BEACH, a strong relationship between them was found with a standard deviation ranging from three to 40 mm. A light decrease of ET values in the 12 years can be seen in the result analyses; however a longer simulation time might be needed to ensure this trend."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2015"],["dc.identifier.doi","10.23953/cloud.ijarsg.124"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12569"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/60821"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/65073"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.relation.issn","2320-0243"],["dc.rights","CC BY 3.0"],["dc.rights.access","openAccess"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.subject","Modeling Surface Runoff and Evapotranspiration; Nam Kim Watershed; Remote Sensing; GIS 1."],["dc.subject.ddc","550"],["dc.title","Modeling Surface Runoff and Evapotranspiration using SWAT and BEACH for a Tropical Watershed in North Vietnam, Compared to MODIS Products"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","4216160"],["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.journal","International Journal of Forestry Research"],["dc.bibliographiccitation.lastpage","13"],["dc.bibliographiccitation.volume","2020"],["dc.contributor.author","Nguyen, The Dung"],["dc.contributor.author","Kappas, Martin"],["dc.date.accessioned","2020-08-31T11:23:00Z"],["dc.date.accessioned","2021-10-27T13:13:49Z"],["dc.date.available","2020-08-31T11:23:00Z"],["dc.date.available","2021-10-27T13:13:49Z"],["dc.date.issued","2020"],["dc.description.abstract","Forest biomass is an important ecological indicator for the sustainable management of forests. The aim of this study was to estimate forest aboveground biomass (AGB) by integrating SPOT-6 data with field-based measurements using the random forest (RF) algorithm. In total, 52 remote sensing variables, including spectral bands, vegetation indices, topography data, and textures, were extracted from SPOT-6 images to predict the forest AGB of Xuan Lien Nature Reserve, Vietnam. To determine the optimal predictor variables for AGB estimation, 10 different RF models were built. To evaluate these models, 10-fold cross-validation was applied. We found that a combination of spectral and vegetation indices and topography variables offer the highest prediction results ( R$^2_{\\textit{adj}}$ = 0.74 and RMSE = 61.24 Mg ha$^{−1}$). Adding texture features into the predictor variables did not improve the model performance. In addition, the SPOT-6 sensor has the potential to predict forest AGB using the RF algorithm."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2020"],["dc.identifier.doi","10.1155/2020/4216160"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17531"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/91810"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.relation.eissn","1687-9376"],["dc.relation.issn","1687-9368"],["dc.relation.orgunit","Fakultät für Geowissenschaften und Geographie"],["dc.rights","CC BY 4.0"],["dc.rights.access","openAccess"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","550"],["dc.title","Estimating the Aboveground Biomass of an Evergreen Broadleaf Forest in Xuan Lien Nature Reserve, Thanh Hoa, Vietnam, Using SPOT-6 Data and the Random Forest Algorithm"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","28"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Climate"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Phan, Thanh"],["dc.contributor.author","Kappas, Martin"],["dc.contributor.author","Tran, Trong"],["dc.date.accessioned","2019-07-09T11:45:24Z"],["dc.date.available","2019-07-09T11:45:24Z"],["dc.date.issued","2018"],["dc.description.abstract","Land surface temperature (LST) is one of the most important variables for applications relating to the physics of land surface processes. LST rapidly changes in both space and time, and knowledge of LST and its spatiotemporal variation is essential to understand the interactions between human activity and the environment. This study investigates the spatiotemporal variation of LST according to changes in elevation. The newest version (version 6) of MODIS LST data for 2015 was used. An area of 40,000 km2 (200 × 200 km2) in northwest Vietnam with elevations ranging from 8 m to 3165 m was chosen as a case study. Our results showed that the drop in LST with increased elevation varied throughout the year during both the daytime and nighttime. The monthly averages in 2015 and an altitude increase of 1000 m resulted in a decrease in LST ranging from 3.8 °C to 6.1 °C and 1.5 °C to 5.8 °C for the daytime and nighttime, respectively. This suggests that in any study relating to the spatial distribution of LST, the effect of elevation on LST should be considered. In addition, the effects of land use/cover and elevation distribution on the relationship between LST and elevation are discussed."],["dc.identifier.doi","10.3390/cli6020028"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15192"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59221"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","MDPI"],["dc.relation.eissn","2225-1154"],["dc.relation.issn","2225-1154"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","550"],["dc.title","Land Surface Temperature Variation Due to Changes in Elevation in Northwest Vietnam"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","8896310"],["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.journal","International Journal of Forestry Research"],["dc.bibliographiccitation.lastpage","18"],["dc.bibliographiccitation.volume","2020"],["dc.contributor.author","Nguyen Trong, Hung"],["dc.contributor.author","Nguyen, The Dung"],["dc.contributor.author","Kappas, Martin"],["dc.date.accessioned","2020-09-29T09:23:10Z"],["dc.date.accessioned","2021-10-27T13:14:03Z"],["dc.date.available","2020-09-29T09:23:10Z"],["dc.date.available","2021-10-27T13:14:03Z"],["dc.date.issued","2020"],["dc.description.abstract","This paper aims to (i) optimize the application of multiple bands of satellite images for land cover classification by using random forest algorithms and (ii) assess correlations and regression of vegetation indices of a better-performed land cover classification image with vertical and horizontal structures of tropical lowland forests in Central Vietnam. In this study, we used Sentinel-2 and Landsat-8 to classify seven land cover classes of which three forest types were substratified as undisturbed, low disturbed, and disturbed forests where forest inventory of 90 plots, as ground-truth, was randomly sampled to measure forest tree parameters. A total of 3226 training points were sampled on seven land cover types. The performance of Landsat-8 showed out-of-bag error of 31.6%, overall accuracy of 68%, kappa of 67.5%, while Sentinel-2 showed out-of-bag error of 14.3% and overall accuracy of 85.7% and kappa of 83%. Ten vegetation indices of the better-performed image were extracted to find out (i) the correlation and regression of horizontal and vertical structures of trees and (ii) assess the variation values between ground-truthing plots and training sample plots in three forest types. The result of the t test on vegetation indices showed that six out of ten vegetation indices were significant at p<0.05. Seven vegetation indices had a correlation with the horizontal structure, but four vegetation indices, namely, Enhanced Vegetation Index, Perpendicular Vegetation Index, Difference Vegetation Index, and Transformed Normalized Difference Vegetation Index, had better correlations r = 0.66, 0.65, 0.65, 0.63 and regression results were of R$^2$ = 0.44, 0.43, 0.43, and 0.40, respectively. The correlations of tree height were r = 0.46, 0.43, 0.43, and 0.49 and its regressions were of R$^2$ = 0.21, 0.19, 0.18, and 0.24, respectively. The results show the possibility of using random forest algorithm with Sentinel-2 in forest type classification in line with vegetation indices application."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2020"],["dc.identifier.doi","10.1155/2020/8896310"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17575"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/91828"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.relation.eissn","1687-9376"],["dc.relation.issn","1687-9368"],["dc.relation.orgunit","Fakultät für Geowissenschaften und Geographie"],["dc.rights","CC BY 4.0"],["dc.rights.access","openAccess"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","550"],["dc.title","Land Cover and Forest Type Classification by Values of Vegetation Indices and Forest Structure of Tropical Lowland Forests in Central Vietnam"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","18"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Sensors"],["dc.bibliographiccitation.lastpage","20"],["dc.bibliographiccitation.volume","18"],["dc.contributor.author","Phan, Thanh Noi"],["dc.contributor.author","Kappas, Martin"],["dc.date.accessioned","2019-07-09T11:45:04Z"],["dc.date.accessioned","2020-05-08T08:39:43Z"],["dc.date.available","2019-07-09T11:45:04Z"],["dc.date.available","2020-05-08T08:39:43Z"],["dc.date.issued","2017"],["dc.description.abstract","In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost classifiers at producing high accuracies. However, only a few studies have compared the performances of these classifiers with different training sample sizes for the same remote sensing images, particularly the Sentinel-2 Multispectral Imager (MSI). In this study, we examined and compared the performances of the RF, kNN, and SVM classifiers for land use/cover classification using Sentinel-2 image data. An area of 30 × 30 km² within the Red River Delta of Vietnam with six land use/cover types was classified using 14 different training sample sizes, including balanced and imbalanced, from 50 to over 1250 pixels/class. All classification results showed a high overall accuracy (OA) ranging from 90% to 95%. Among the three classifiers and 14 sub-datasets, SVM produced the highest OA with the least sensitivity to the training sample sizes, followed consecutively by RF and kNN. In relation to the sample size, all three classifiers showed a similar and high OA (over 93.85%) when the training sample size was large enough, i.e., greater than 750 pixels/class or representing an area of approximately 0.25% of the total study area. The high accuracy was achieved with both imbalanced and balanced datasets."],["dc.identifier.doi","10.3390/s18010018"],["dc.identifier.pmid","29271909"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15026"],["dc.identifier.scopus","2-s2.0-85039732569"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/64952"],["dc.identifier.url","http://www.scopus.com/inward/record.url?eid=2-s2.0-85039732569&partnerID=MN8TOARS"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","MDPI"],["dc.relation.eissn","1424-8220"],["dc.relation.issn","1424-8220"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.subject.ddc","550"],["dc.title","Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","147"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","ISPRS International Journal of Geo-Information"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Gia Pham, Tung"],["dc.contributor.author","Kappas, Martin"],["dc.contributor.author","Van Huynh, Chuong"],["dc.contributor.author","Hoang Khanh Nguyen, Linh"],["dc.date.accessioned","2019-07-09T11:50:33Z"],["dc.date.available","2019-07-09T11:50:33Z"],["dc.date.issued","2019"],["dc.description.abstract","Soil property maps are essential resources for agricultural land use. However, soil properties mapping is costly and time-consuming, especially in the regions with complicated topographic conditions. This study was conducted in a hilly region of Central Vietnam with the following objectives: (i) to evaluate the best environmental variables to estimate soil organic carbon (SOC), total nitrogen (TN), and soil reaction (pH) with a regression kriging (RK) model, and (ii) to compare the accuracy of the ordinary kriging (OK) and RK methods. SOC, TN, and soil pH data were measured at 155 locations within the research area with a sampling grid of 2 km 2 km for a soil layer from 0 to 30 cm depth. From these samples, 117 were used for interpolation, and the 38 randomly remaining samples were used for evaluating accuracy. The chosen environmental variables are land use type (LUT), topographic wetness index (TWI), and transformed soil adjusted vegetation index (TSAVI). The results indicate that the LUT variable is more effective than TWI and TSAVI for determining TN and pH when using the RK method, with a variance of 7.00% and 18.40%, respectively. In contrast, a combination of the LUT and TWI variables is the best for SOC mapping with the RK method, with a variance of 14.98%. The OK method seemed more accurate than the RK method for SOC mapping by 3.33% and for TN mapping by 10% but the RK method was found more precise than the OK method for soil pH mapping by 1.81%. Further selection of auxiliary variables and higher sampling density should be considered to improve the accuracy of the RK method."],["dc.identifier.doi","10.3390/ijgi8030147"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15963"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59796"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","550"],["dc.title","Application of Ordinary Kriging and Regression Kriging Method for Soil Properties Mapping in Hilly Region of Central Vietnam"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","75"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","EARSeL eProceedings"],["dc.bibliographiccitation.lastpage","92"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Propastin, Pavel"],["dc.contributor.author","Kappas, Martin"],["dc.date.accessioned","2019-07-10T08:13:20Z"],["dc.date.available","2019-07-10T08:13:20Z"],["dc.date.issued","2009"],["dc.description.abstract","Maps of peak seasonal leaf area index (LAI) were produced using the Normalised Difference Vegetation Index (NDVI) from SPOT VEGETATION (VEG) satellite at 1 km resolution over a large region in the semi-arid zone of Kazakhstan. Ground measurements of LAI were acquired using indirect and direct techniques across a 150·150 km2 large region. A Landsat Enhanced Thematic Mapper (ETM+) scene at 30 m spatial resolution was used to locate ground sites and to facilitate spatial scaling to 1 km pixels. A high-resolution LAI map retrieved from the Landsat ETM+ data was aggregated to 1 km resolution and afterwards used as reference data. The methods tested for transfer function between ETM+ LAI and SPOT-VEG were ordinary least squares (OLS) regres-sion, non-linear regression, and reduced major axis (RMA) regression. In this paper, final maps of peak season LAI at a 1 km resolution are presented after an assessment of their accuracy using the aggregated ETM+ LAI scene. The most appropriate results were attained by RMA. Advantages and shortages of the used regression approaches were analysed and discussed. Errors were mostly caused by uncertainties in co-registration of Landsat ETM+ and SPOT-VEG images as it was demonstrated by a pixel degradation experiment. The methodology presented in this paper can serve as a basis for generation of medium- and coarse-resolution LAI satellite products for wide areas of Central Asia and Kazakhstan. The study exposed a general transferability of the de-veloped model for LAI estimations at coarser scales. The 1000 m SPOT-VEG model has proved to be fully suitable for utilising with the SPOT-VEG data with resolution of 2 km."],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/5860"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/61206"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.rights","Goescholar"],["dc.rights.access","openAccess"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.subject","SEMI-DESERT"],["dc.subject.ddc","550"],["dc.title","Mapping leaf area index over semi-desert and steppe biomes in kazakhstan using satellite imagery and ground measurements"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]