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C. Atzberger
Institute of Geomatics, University of Natural Resources and Life Sciences (BOKU), Peter Jordan Straße 82, Vienna 1190, Austria

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Journal article
Published: 20 February 2021 in ISPRS Journal of Photogrammetry and Remote Sensing
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Zanthoxylum bungeanum Maxim (ZBM) is an important woody species in large parts of Asia, which provides oils and medicinal materials. Timely and accurate mapping of its spatial distribution and planting area is of great significance to local economy and ecology. As a special tree species planted in the Grain for Green Program of China, Linxia Hui Autonomous Prefecture (Linxia) in Gansu Province of China has vigorously developed ZBM cultivation since the launch of the program. However, lacking the accurate ZBM planting information hinders the assessment of the benefits and losses of the program to local people. Therefore, this study investigated the potential of multi-temporal Sentinel-2 Multi-Spectral Instrument (MSI) to accurately map ZBM in the study area in 2019. We first investigated the classification accuracies of four alternative Random Forest (RF) classifications using either alone or in combination, spectral bands, vegetation indices (VIs), and topographical variables. The importance of the three categories of features was examined based on the mean decrease accuracy (MDA) metric. The classification results with the most important features were further assessed for their consistency by considering the voting rates of 800 trees based on testing samples. Results show that the sole use of the spectral bands (40 input features) already achieves a satisfactory classification accuracy of 95.43%. Adding extra VIs and topographical variables further improves the results, but only to a small extent. However, certain VIs and topographic variables are far more effective in classification compared with the original spectral bands, especially the Red Edge Normalized Difference Vegetation Index (NDVI705) and Normalized Difference Yellow Index (NDYI). The classification accuracy achieves nearly 95% when using only the top 15 most important features. The desirable periods for differencing of ZBM and other land cover types are fruit coloring and ripening periods. The final map shows that the ZBM planting in Linxia is mainly distributed along the Yellow River and around the Liujiaxia reservoir. The total mapped acreages of ZBM is 51,601 ha, covering 9.51% of the study area. 99% of ZBM tends to grow between 1500 and 2400 m altitude, and 67% of ZBM are planted in areas with slopes between 5 and 25°. Voting rates show that the percentages of classification results with strong and good consistency are generally over 70% for all land cover types, proving the derived land cover map's high credibility, including ZBM. Altogether, our results demonstrate the high potential of multi-temporal Sentinel-2 images in accurate mapping of ZBM, which can serve as a reference for other specialty crops or tree species.

ACS Style

Mingxing Liu; Jianhong Liu; Clement Atzberger; Ya Jiang; Minfei Ma; Xunmei Wang. Zanthoxylum bungeanum Maxim mapping with multi-temporal Sentinel-2 images: The importance of different features and consistency of results. ISPRS Journal of Photogrammetry and Remote Sensing 2021, 174, 68 -86.

AMA Style

Mingxing Liu, Jianhong Liu, Clement Atzberger, Ya Jiang, Minfei Ma, Xunmei Wang. Zanthoxylum bungeanum Maxim mapping with multi-temporal Sentinel-2 images: The importance of different features and consistency of results. ISPRS Journal of Photogrammetry and Remote Sensing. 2021; 174 ():68-86.

Chicago/Turabian Style

Mingxing Liu; Jianhong Liu; Clement Atzberger; Ya Jiang; Minfei Ma; Xunmei Wang. 2021. "Zanthoxylum bungeanum Maxim mapping with multi-temporal Sentinel-2 images: The importance of different features and consistency of results." ISPRS Journal of Photogrammetry and Remote Sensing 174, no. : 68-86.

Journal article
Published: 30 October 2020 in Land
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Economic theory notes tenure security is a critical factor in agricultural investment and productivity. Therefore, several African countries’ development initiatives enabled land titling to enhance tenure security. This paper examines the effect of land certification on tenure security, land investment, crop productivity and land dispute in Gozamin District, Ethiopia. In addition, the impact of land certification on farm households’ perceptions and confidence in land tenure and land use rights is investigated. Face-to-face interviews with 343 randomly selected farm households, group discussions and expert panels are the sources of primary data. Quantitative data are analyzed using various statistical tools and complemented by qualitative data. According to the results, most farm households (56%) feel that their land use rights are secure after the certification process. Only 17% fear that the government at any time could take their land use rights. The majority of farm households (71.7%) identified a reduction of disputes after certification and land management practices improved from 70.3% before certification to 90.1% after certification. As key factors for the increase of terracing and the application of manure, the study determined total farm size, the average distance from farm to homestead, perception of degradation, access to credit, training to land resource management, fear about land take-over by the government and total livestock holdings. Crop productivity improved significantly after land certification. The results should encourage policy makers to minimize the sources of insecurity, such as frustrations of future land redistribution and land taking without proper land compensation. Land certification is the right tool for creating tenure security, enhancing farmers’ confidence in their land rights and—supported by a proper land use planning system—improving land-related investments and crop productivity.

ACS Style

Abebaw Andarge Gedefaw; Clement Atzberger; Walter Seher; Sayeh Kassaw Agegnehu; Reinfried Mansberger. Effects of Land Certification for Rural Farm Households in Ethiopia: Evidence from Gozamin District, Ethiopia. Land 2020, 9, 421 .

AMA Style

Abebaw Andarge Gedefaw, Clement Atzberger, Walter Seher, Sayeh Kassaw Agegnehu, Reinfried Mansberger. Effects of Land Certification for Rural Farm Households in Ethiopia: Evidence from Gozamin District, Ethiopia. Land. 2020; 9 (11):421.

Chicago/Turabian Style

Abebaw Andarge Gedefaw; Clement Atzberger; Walter Seher; Sayeh Kassaw Agegnehu; Reinfried Mansberger. 2020. "Effects of Land Certification for Rural Farm Households in Ethiopia: Evidence from Gozamin District, Ethiopia." Land 9, no. 11: 421.

Journal article
Published: 19 August 2020 in Computers and Electronics in Agriculture
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Sparse vegetation such as riparian forests and trees outside forests (TOF) cover only small areas but present various ecological advantages. The detection of these vegetation types in semi-arid mountainous areas is challenging as trees are heavily mixed with other land cover types. Their mapping requires therefore high-resolution imagery. We propose to leverage the advantages and synergies of freely available Sentinel-2 data and a light-weight consumer-grade unmanned aerial vehicle (UAV) with a simple red–greenblue (RGB) camera to detect these vegetation types. In our approach, an object-based random forest land cover classification is first developed over smaller sites using very high-resolution UAV data. The resulting maps are afterwards used as training data for multi-temporal Sentinel-2 based classifications at regional scale. We tested the approach in five different riparian landscapes of a semi-arid mountainous area in Iran. For comparison, mono- and multi-temporal Sentinel-2 data were also used alone – without support from UAV data – to build pixel-based random forest classification models at regional scale. Our results show that compared to the best mono-temporal results, the multi-temporal classification approach improved the overall accuracy and Kappa values of Sentinel-2 classifications from 77.0% to 83.9% and 0.72 to 0.81, respectively. The producer’s and user’s accuracy of the riparian forest class were also improved from 64.0% to 70.0% and 57.1% to 73.7%, respectively. Combining UAV and Sentinel-2 data improved the overall accuracy only slightly, but enabled a much better detection of Persian oak stands – for this class, the producer’s accuracy increased by 13.0 percentage points. Overall, we recommend the combined use of UAV and multi-temporal Sentinel-2 data to detect Persian oak forest stands.

ACS Style

Ardalan Daryaei; Hormoz Sohrabi; Clement Atzberger; Markus Immitzer. Fine-scale detection of vegetation in semi-arid mountainous areas with focus on riparian landscapes using Sentinel-2 and UAV data. Computers and Electronics in Agriculture 2020, 177, 105686 .

AMA Style

Ardalan Daryaei, Hormoz Sohrabi, Clement Atzberger, Markus Immitzer. Fine-scale detection of vegetation in semi-arid mountainous areas with focus on riparian landscapes using Sentinel-2 and UAV data. Computers and Electronics in Agriculture. 2020; 177 ():105686.

Chicago/Turabian Style

Ardalan Daryaei; Hormoz Sohrabi; Clement Atzberger; Markus Immitzer. 2020. "Fine-scale detection of vegetation in semi-arid mountainous areas with focus on riparian landscapes using Sentinel-2 and UAV data." Computers and Electronics in Agriculture 177, no. : 105686.

Journal article
Published: 02 June 2020 in Sustainability
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Land cover patterns in sub-Saharan Africa are rapidly changing. This study aims to quantify the land cover change and to identify its major determinants by using the Drivers, Pressures, State, Impact, Responses (DPSIR) framework in the Ethiopian Gozamin District over a period of 32 years (1986 to 2018). Satellite images of Landsat 5 (1986), Landsat 7 (2003), and Sentinel-2 (2018) and a supervised image classification methodology were used to assess the dynamics of land cover change. Land cover maps of the three dates, focus group discussions (FGDs), interviews, and farmers’ lived experiences through a household survey were applied to identify the factors for changes based on the DPSIR framework. Results of the investigations revealed that during the last three decades the study area has undergone an extensive land cover change, primarily a shift from cropland and grassland into forests and built-up areas. Thus, quantitative land cover change detection between 1986 and 2018 revealed that cropland, grassland, and bare areas declined by 10.53%, 5.7%, and 2.49%. Forest, built-up, shrub/scattered vegetation, and water bodies expanded by 13.47%, 4.02%, 0.98%, and 0.25%. Household surveys and focus group discussions (FGDs) identified the population growth, the rural land tenure system, the overuse of land, the climate change, and the scarcity of grazing land as drivers of these land cover changes. Major impacts were rural to urban migration, population size change, scarcity of land, and decline in land productivity. The outputs from this study could be used to assure sustainability in resource utilization, proper land use planning, and proper decision-making by the concerned government authorities.

ACS Style

Abebaw Gedefaw; Clement Atzberger; Thomas Bauer; Sayeh Agegnehu; Reinfried Mansberger. Analysis of Land Cover Change Detection in Gozamin District, Ethiopia: From Remote Sensing and DPSIR Perspectives. Sustainability 2020, 12, 4534 .

AMA Style

Abebaw Gedefaw, Clement Atzberger, Thomas Bauer, Sayeh Agegnehu, Reinfried Mansberger. Analysis of Land Cover Change Detection in Gozamin District, Ethiopia: From Remote Sensing and DPSIR Perspectives. Sustainability. 2020; 12 (11):4534.

Chicago/Turabian Style

Abebaw Gedefaw; Clement Atzberger; Thomas Bauer; Sayeh Agegnehu; Reinfried Mansberger. 2020. "Analysis of Land Cover Change Detection in Gozamin District, Ethiopia: From Remote Sensing and DPSIR Perspectives." Sustainability 12, no. 11: 4534.

Editorial
Published: 13 March 2020 in Remote Sensing
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High-throughput crop phenotyping is harnessing the potential of genomic resources for the genetic improvement of crop production under changing climate conditions. As global food security is not yet assured, crop phenotyping has received increased attention during the past decade. This spectral issue (SI) collects 30 papers reporting research on estimation of crop phenotyping traits using unmanned ground vehicle (UGV) and unmanned aerial vehicle (UAV) imagery. Such platforms were previously not widely available. The special issue includes papers presenting recent advances in the field, with 22 UAV-based papers and 12 UGV-based articles. The special issue covers 16 RGB sensor papers, 11 papers on multi-spectral imagery, and further 4 papers on hyperspectral and 3D data acquisition systems. A total of 13 plants’ phenotyping traits, including morphological, structural, and biochemical traits are covered. Twenty different data processing and machine learning methods are presented. In this way, the special issue provides a good overview regarding potential applications of the platforms and sensors, to timely provide crop phenotyping traits in a cost-efficient and objective manner. With the fast development of sensors technology and image processing algorithms, we expect that the estimation of crop phenotyping traits supporting crop breeding scientists will gain even more attention in the future.

ACS Style

Xiuliang Jin; Zhenhai Li; Clement Atzberger. Editorial for the Special Issue “Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery”. Remote Sensing 2020, 12, 940 .

AMA Style

Xiuliang Jin, Zhenhai Li, Clement Atzberger. Editorial for the Special Issue “Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery”. Remote Sensing. 2020; 12 (6):940.

Chicago/Turabian Style

Xiuliang Jin; Zhenhai Li; Clement Atzberger. 2020. "Editorial for the Special Issue “Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery”." Remote Sensing 12, no. 6: 940.

Journal article
Published: 05 February 2020 in Remote Sensing
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This work aims at addressing two issues simultaneously: data compression at input space and semantic segmentation. Semantic segmentation of remotely sensed multi- or hyperspectral images through deep learning (DL) artificial neural networks (ANN) delivers as output the corresponding matrix of pixels classified elementwise, achieving competitive performance metrics. With technological progress, current remote sensing (RS) sensors have more spectral bands and higher spatial resolution than before, which means a greater number of pixels in the same area. Nevertheless, the more spectral bands and the greater number of pixels, the higher the computational complexity and the longer the processing times. Therefore, without dimensionality reduction, the classification task is challenging, particularly if large areas have to be processed. To solve this problem, our approach maps an RS-image or third-order tensor into a core tensor, representative of our input image, with the same spatial domain but with a lower number of new tensor bands using a Tucker decomposition (TKD). Then, a new input space with reduced dimensionality is built. To find the core tensor, the higher-order orthogonal iteration (HOOI) algorithm is used. A fully convolutional network (FCN) is employed afterwards to classify at the pixel domain, each core tensor. The whole framework, called here HOOI-FCN, achieves high performance metrics competitive with some RS-multispectral images (MSI) semantic segmentation state-of-the-art methods, while significantly reducing computational complexity, and thereby, processing time. We used a Sentinel-2 image data set from Central Europe as a case study, for which our framework outperformed other methods (included the FCN itself) with average pixel accuracy (PA) of 90% (computational time ∼90s) and nine spectral bands, achieving a higher average PA of 91.97% (computational time ∼36.5s), and average PA of 91.56% (computational time ∼9.5s) for seven and five new tensor bands, respectively.

ACS Style

Josué López; Deni Torres; Stewart Santos; Clement Atzberger. Spectral Imagery Tensor Decomposition for Semantic Segmentation of Remote Sensing Data through Fully Convolutional Networks. Remote Sensing 2020, 12, 517 .

AMA Style

Josué López, Deni Torres, Stewart Santos, Clement Atzberger. Spectral Imagery Tensor Decomposition for Semantic Segmentation of Remote Sensing Data through Fully Convolutional Networks. Remote Sensing. 2020; 12 (3):517.

Chicago/Turabian Style

Josué López; Deni Torres; Stewart Santos; Clement Atzberger. 2020. "Spectral Imagery Tensor Decomposition for Semantic Segmentation of Remote Sensing Data through Fully Convolutional Networks." Remote Sensing 12, no. 3: 517.

Journal article
Published: 08 December 2019 in ISPRS International Journal of Geo-Information
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For improved drought planning and response, there is an increasing need for highly predictive and stable drought prediction models. This paper presents the performance of both homogeneous and heterogeneous model ensembles in the satellite-based prediction of drought severity using artificial neural networks (ANN) and support vector regression (SVR). For each of the homogeneous and heterogeneous model ensembles, the study investigates the performance of three model ensembling approaches: (1) non-weighted linear averaging, (2) ranked weighted averaging, and (3) model stacking using artificial neural networks. Using the approach of “over-produce then select”, the study used 17 years of satellite data on 16 selected variables for predictive drought monitoring to build 244 individual ANN and SVR models from which 111 models were automatically selected for the building of the model ensembles. Model stacking is shown to realize models that are superior in performance in the prediction of future drought conditions as compared to the linear averaging and weighted averaging approaches. The best performance from the heterogeneous stacked model ensembles recorded an R2 of 0.94 in the prediction of future (1 month ahead) vegetation conditions on unseen test data (2016–2017) as compared to an R2 of 0.83 and R2 of 0.78 for ANN and SVR, respectively, in the traditional approach of selection of the best (champion) model. We conclude that despite the computational resource intensiveness of the model ensembling approach, the returns in terms of model performance for drought prediction are worth the investment, especially in the context of the continued exponential increase in computational power and the potential benefits of improved forecasting for vulnerable populations.

ACS Style

Chrisgone Adede; Robert Oboko; Peter W. Wagacha; Clement Atzberger. Model Ensembles of Artificial Neural Networks and Support Vector Regression for Improved Accuracy in the Prediction of Vegetation Conditions and Droughts in Four Northern Kenya Counties. ISPRS International Journal of Geo-Information 2019, 8, 562 .

AMA Style

Chrisgone Adede, Robert Oboko, Peter W. Wagacha, Clement Atzberger. Model Ensembles of Artificial Neural Networks and Support Vector Regression for Improved Accuracy in the Prediction of Vegetation Conditions and Droughts in Four Northern Kenya Counties. ISPRS International Journal of Geo-Information. 2019; 8 (12):562.

Chicago/Turabian Style

Chrisgone Adede; Robert Oboko; Peter W. Wagacha; Clement Atzberger. 2019. "Model Ensembles of Artificial Neural Networks and Support Vector Regression for Improved Accuracy in the Prediction of Vegetation Conditions and Droughts in Four Northern Kenya Counties." ISPRS International Journal of Geo-Information 8, no. 12: 562.

Journal article
Published: 20 November 2019 in Remote Sensing
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Crop phenology is an important parameter for crop growth monitoring, yield prediction, and growth simulation. The dynamic threshold method is widely used to retrieve vegetation phenology from remotely sensed vegetation index time series. However, crop growth is not only driven by natural conditions, but also modified through field management activities. Complicated planting patterns, such as multiple cropping, makes the vegetation index dynamics less symmetrical. These impacts are not considered in current approaches for crop phenology retrieval based on the dynamic threshold method. Thus, this paper aimed to (1) investigate the optimal thresholds for retrieving the start of the season (SOS) and the end of the season (EOS) of different crops, and (2) compare the performances of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) in retrieving crop phenology with a modified version of the dynamic threshold method. The reference data included SOS and EOS ground observations of three major crop types in 2015 and 2016, which includes rice, wheat, and maize. Results show that (1) the modification of the original method ensures a 100% retrieval rate, which was not guaranteed using the original method. The modified dynamic threshold method is more suitable to retrieve crop SOS/EOS because it considers the asymmetry of crop vegetation index time series. (2) It is inappropriate to retrieve SOS and EOS with the same threshold for all crops, and the commonly used 20% or 50% thresholds are not the optimal thresholds for all crops. (3) For single and late rice, the accuracies of the SOS estimations based on EVI are generally higher compared to those based on NDVI. However, for spring maize and summer maize, results based on NDVI give higher accuracies. In terms of EOS, for early rice and summer maize, estimates based on EVI result in higher accuracies, but, for late rice and winter wheat, results based on NDVI are closer to the ground records.

ACS Style

Xin Huang; Jianhong Liu; Wenquan Zhu; Clement Atzberger. The Optimal Threshold and Vegetation Index Time Series for Retrieving Crop Phenology Based on a Modified Dynamic Threshold Method. Remote Sensing 2019, 11, 2725 .

AMA Style

Xin Huang, Jianhong Liu, Wenquan Zhu, Clement Atzberger. The Optimal Threshold and Vegetation Index Time Series for Retrieving Crop Phenology Based on a Modified Dynamic Threshold Method. Remote Sensing. 2019; 11 (23):2725.

Chicago/Turabian Style

Xin Huang; Jianhong Liu; Wenquan Zhu; Clement Atzberger. 2019. "The Optimal Threshold and Vegetation Index Time Series for Retrieving Crop Phenology Based on a Modified Dynamic Threshold Method." Remote Sensing 11, no. 23: 2725.

Journal article
Published: 06 November 2019 in Remote Sensing
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Detailed knowledge about tree species composition is of great importance for forest management. The two identical European Space Agency (ESA) Sentinel-2 (S2) satellites provide data with unprecedented spectral, spatial and temporal resolution. Here, we investigated the potential benefits of using high temporal resolution data for classification of five coniferous and seven broadleaved tree species in a diverse Central European Forest. To run the classification, 18 cloud-free S2 acquisitions were analyzed in a two-step approach. The available scenes were first used to stratify the study area into six broad land-cover classes. Subsequently, additional classification models were created separately for the coniferous and the broadleaved forest strata. To permit a deeper analytical insight in the benefits of multi-temporal datasets for species identification, classification models were developed taking into account all 262,143 possible permutations of the 18 S2 scenes. Each model was fine-tuned using a stepwise recursive feature reduction. The additional use of vegetation indices improved the model performances by around 5 percentage points. Individual mono-temporal tree species accuracies range from 48.1% (January 2017) to 78.6% (June 2017). Compared to the best mono-temporal results, the multi-temporal analysis approach improves the out-of-bag overall accuracy from 72.9% to 85.7% for the broadleaved and from 83.8% to 95.3% for the coniferous tree species, respectively. Remarkably, a combination of six–seven scenes achieves a model quality equally high as the model based on all data; images from April until August proved most important. The classes European Beech and European Larch attain the highest user’s accuracies of 96.3% and 95.9%, respectively. The most important spectral variables to distinguish between tree species are located in the Red (coniferous) and short wave infrared (SWIR) bands (broadleaved), respectively. Overall, the study highlights the high potential of multi-temporal S2 data for species-level classifications in Central European forests.

ACS Style

Markus Immitzer; Martin Neuwirth; Sebastian Böck; Harald Brenner; Francesco Vuolo; Clement Atzberger. Optimal Input Features for Tree Species Classification in Central Europe Based on Multi-Temporal Sentinel-2 Data. Remote Sensing 2019, 11, 2599 .

AMA Style

Markus Immitzer, Martin Neuwirth, Sebastian Böck, Harald Brenner, Francesco Vuolo, Clement Atzberger. Optimal Input Features for Tree Species Classification in Central Europe Based on Multi-Temporal Sentinel-2 Data. Remote Sensing. 2019; 11 (22):2599.

Chicago/Turabian Style

Markus Immitzer; Martin Neuwirth; Sebastian Böck; Harald Brenner; Francesco Vuolo; Clement Atzberger. 2019. "Optimal Input Features for Tree Species Classification in Central Europe Based on Multi-Temporal Sentinel-2 Data." Remote Sensing 11, no. 22: 2599.

Journal article
Published: 04 November 2019 in Remote Sensing
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The objective of this research was to develop a robust statistical model to estimate climatologies (2002–2017) of monthly average near-surface air temperature (Ta) over Mongolia using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) time series products and terrain parameters. Two regression models were analyzed in this study linking automatic weather station data (Ta) with Earth observation (EO) images: partial least squares (PLS) and random forest (RF). Both models were trained to predict Ta climatologies for each of the twelve months, using up to 17 variables as predictors. The models were applied to the entire land surface of Mongolia, the eighteenth largest but most sparsely populated country in the world. Twelve of the predictor variables were derived from the LST time series products of the Terra MODIS satellite. The LST MOD11A2 (collection 6) products provided thermal information at a spatial resolution of 1 km and with 8-day temporal resolution from 2002 to 2017. Three terrain variables, namely, elevation, slope, and aspect, were extracted using a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), and two variables describing the geographical location of weather stations were extracted from vector data. For training, a total of 8544 meteorological data points from 63 automatic weather stations were used covering the same period as MODIS LST products. The PLS regression resulted in a coefficient of determination (R2) between 0.74 and 0.87 and a root-mean-square error (RMSE) from 1.20 °C to 2.19 °C between measured and estimated monthly Ta. The non-linear RF regression yielded even more accurate results with R2 in the range from 0.82 to 0.95 and RMSE from 0.84 °C to 1.93 °C. Using RF, the two best modeled months were July and August and the two worst months were January and February. The four most predictive variables were day/nighttime LST, elevation, and latitude. Using the developed RF models, spatial maps of the monthly average Ta at a spatial resolution of 1 km were generated for Mongolia (~1566 × 106 km2). This spatial dataset might be useful for various environmental applications. The method is transparent and relatively easy to implement.

ACS Style

Munkhdulam Otgonbayar; Clement Atzberger; Matteo Mattiuzzi; Avirmed Erdenedalai. Estimation of Climatologies of Average Monthly Air Temperature over Mongolia Using MODIS Land Surface Temperature (LST) Time Series and Machine Learning Techniques. Remote Sensing 2019, 11, 2588 .

AMA Style

Munkhdulam Otgonbayar, Clement Atzberger, Matteo Mattiuzzi, Avirmed Erdenedalai. Estimation of Climatologies of Average Monthly Air Temperature over Mongolia Using MODIS Land Surface Temperature (LST) Time Series and Machine Learning Techniques. Remote Sensing. 2019; 11 (21):2588.

Chicago/Turabian Style

Munkhdulam Otgonbayar; Clement Atzberger; Matteo Mattiuzzi; Avirmed Erdenedalai. 2019. "Estimation of Climatologies of Average Monthly Air Temperature over Mongolia Using MODIS Land Surface Temperature (LST) Time Series and Machine Learning Techniques." Remote Sensing 11, no. 21: 2588.

Chapter
Published: 12 October 2019 in Umwelt- und Bioressourcenmanagement für eine nachhaltige Zukunftsgestaltung
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In diesem Beitrag werden die Bereiche Umweltdatenmanagement und Umweltstatistik vorgestellt und ihre Anwendung in der Umweltökonomie beispielhaft gezeigt. Dafür sind Kenntnisse und Fertigkeiten zu Management, Modellierung und Bewertung von Umweltdaten mit Raum- und Zeitbezug wichtig. Im Bachelorstudium UBRM werden dazu Grundlagen vermittelt, die im Masterstudium in einem eigenen Fachbereich vertieft werden können. Die vermittelten Fertigkeiten beinhalten Datenhaltung und -management, Visualisierung und Analyse mittels Geoinformationssystemen (GIS), Grundlagen des Modellierens und Simulierens, statistische Modellierung für Umweltdaten und deren Extremwerte sowie Methoden zur Bewertung von Umweltdaten und zur umweltökonomischen Entscheidungsunterstützung.

ACS Style

Gregor Laaha; Johannes Schmidt; Sebastian Wehrle; Anja Klisch; Thomas Bauer; Reinfried Mansberger; Clement Atzberger. Umweltinformationssysteme und -management. Umwelt- und Bioressourcenmanagement für eine nachhaltige Zukunftsgestaltung 2019, 233 -256.

AMA Style

Gregor Laaha, Johannes Schmidt, Sebastian Wehrle, Anja Klisch, Thomas Bauer, Reinfried Mansberger, Clement Atzberger. Umweltinformationssysteme und -management. Umwelt- und Bioressourcenmanagement für eine nachhaltige Zukunftsgestaltung. 2019; ():233-256.

Chicago/Turabian Style

Gregor Laaha; Johannes Schmidt; Sebastian Wehrle; Anja Klisch; Thomas Bauer; Reinfried Mansberger; Clement Atzberger. 2019. "Umweltinformationssysteme und -management." Umwelt- und Bioressourcenmanagement für eine nachhaltige Zukunftsgestaltung , no. : 233-256.

Journal article
Published: 12 October 2019 in Land
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In many African countries and especially in the highlands of Ethiopia—the investigation site of this paper—agricultural land is highly fragmented. Small and scattered parcels impede a necessary increase in agricultural efficiency. Land consolidation is a proper tool to solve inefficiencies in agricultural production, as it enables consolidating plots based on the consent of landholders. Its major benefits are that individual farms get larger, more compact, contiguous parcels, resulting in lower cultivation efforts. This paper investigates the determinants influencing the willingness of landholder farmers to participate in voluntary land consolidation processes. The study was conducted in Gozamin District, Amhara Region, Ethiopia. The study was mainly based on survey data collected from 343 randomly selected landholder farmers. In addition, structured interviews and focus group discussions with farmers were held. The collected data were analyzed quantitatively mainly by using a logistic regression model and qualitatively by using focus group discussions and expert panels. According to the results, landholder farmers are predominantly willing to participate in voluntary land consolidation (66.8%), while a substantive fraction of farmers express unease with voluntary land consolidation. The study highlighted the following four determinants to be significant in influencing the willingness of farmers for voluntary land consolidation: (1) the exchange should preferably happen with parcels of neighbors, (2) land consolidation should lead to better arranged parcels, (3) nearness of plots to the farmstead, and (4) an expected improvement in productivity. Interestingly, the majority of farmers believes that land consolidation could reduce land use conflicts. The study provides evidence that policymakers should consider these socio-economic, legal, cultural, infrastructural, and land-related factors when designing and implementing voluntary land consolidation policies and programs.

ACS Style

Abebaw Andarge Gedefaw; Clement Atzberger; Walter Seher; Reinfried Mansberger. Farmers Willingness to Participate In Voluntary Land Consolidation in Gozamin District, Ethiopia. Land 2019, 8, 148 .

AMA Style

Abebaw Andarge Gedefaw, Clement Atzberger, Walter Seher, Reinfried Mansberger. Farmers Willingness to Participate In Voluntary Land Consolidation in Gozamin District, Ethiopia. Land. 2019; 8 (10):148.

Chicago/Turabian Style

Abebaw Andarge Gedefaw; Clement Atzberger; Walter Seher; Reinfried Mansberger. 2019. "Farmers Willingness to Participate In Voluntary Land Consolidation in Gozamin District, Ethiopia." Land 8, no. 10: 148.

Journal article
Published: 08 May 2019 in Remote Sensing
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Droughts, with their increasing frequency of occurrence, especially in the Greater Horn of Africa (GHA), continue to negatively affect lives and livelihoods. For example, the 2011 drought in East Africa caused massive losses, documented to have cost the Kenyan economy over 12 billion US dollars. Consequently, the demand is ever-increasing for ex-ante drought early warning systems with the ability to offer drought forecasts with sufficient lead times The study uses 10 precipitation and vegetation condition indices that are lagged over 1, 2 and 3-month time-steps to predict future values of vegetation condition index aggregated over a 3-month time period (VCI3M) that is a proxy variable for drought monitoring. The study used data covering the period 2001–2015 at a monthly frequency for four arid northern Kenya counties for model training, with data for 2016–2017 used as out-of-sample data for model testing. The study adopted a model space search approach to obtain the most predictive artificial neural network (ANN) model as opposed to the traditional greedy search approach that is based on optimal variable selection at each model building step. The initial large model-space was reduced using the general additive model (GAM) technique together with a set of assumptions. Even though we built a total of 102 GAM models, only 20 had R2 ≥ 0.7, and together with the model with lag of the predicted variable, were subjected to the ANN modelling process. The ANN process itself uses the brute-force approach that automatically partitions the training data into 10 sub-samples, builds the ANN models in these samples and evaluates their performance using multiple metrics. The results show the superiority of 1-month lag of the variables as compared to longer time lags of 2 and 3 months. The best ANN model recorded an R2 of 0.78 between actual and predicted vegetation conditions 1-month ahead using the out-of-sample data. Investigated as a classifier distinguishing five vegetation deficit classes, the best ANN model had a modest accuracy of 67% and a multi-class area under the receiver operating characteristic curve (AUROC) of 89.99%.

ACS Style

Chrisgone Adede; Robert Oboko; Peter Waiganjo Wagacha; Clement Atzberger. A Mixed Model Approach to Vegetation Condition Prediction Using Artificial Neural Networks (ANN): Case of Kenya’s Operational Drought Monitoring. Remote Sensing 2019, 11, 1099 .

AMA Style

Chrisgone Adede, Robert Oboko, Peter Waiganjo Wagacha, Clement Atzberger. A Mixed Model Approach to Vegetation Condition Prediction Using Artificial Neural Networks (ANN): Case of Kenya’s Operational Drought Monitoring. Remote Sensing. 2019; 11 (9):1099.

Chicago/Turabian Style

Chrisgone Adede; Robert Oboko; Peter Waiganjo Wagacha; Clement Atzberger. 2019. "A Mixed Model Approach to Vegetation Condition Prediction Using Artificial Neural Networks (ANN): Case of Kenya’s Operational Drought Monitoring." Remote Sensing 11, no. 9: 1099.

Journal article
Published: 03 May 2019 in Ecological Economics
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For those developing satellite-based insurance products, there is no consensus in the scientific community on which of the many available indices most accurately track agro-ecological shocks as experienced by farmers and pastoralists. Furthermore, metrics commonly used by the remote sensing community for assessing the accuracy of indices in retrieving biophysical variables are insufficient in the case of insurance, because they do not consider the value of insurance coverage in terms of household welfare. This study begins to fill this knowledge gap by bridging two index insurance literatures: the remote sensing science literature that focuses on the predictive power of indices, and the economic literature that focuses on welfare outcomes. The article uses a longitudinal panel of household survey data from Kenya to compare the quality of existing and potential insurance products. These products are developed from different processing chains applied to time series of the satellite-based Normalized Difference Vegetation Index (NDVI). Although the indices are highly correlated to each other (ρ > 0.95), a utility analysis provides insight into how small differences can lead to larger differences in product value. Our results highlight that index accuracy, cost, and timeliness of payments must be considered jointly when assessing insurance quality for clients.

ACS Style

Nathaniel Jensen; Quentin Stoeffler; Francesco Pietro Fava; Anton Vrieling; Clement Atzberger; Michele Meroni; Andrew Mude; Michael Carter. Does the design matter? Comparing satellite-based indices for insuring pastoralists against drought. Ecological Economics 2019, 162, 59 -73.

AMA Style

Nathaniel Jensen, Quentin Stoeffler, Francesco Pietro Fava, Anton Vrieling, Clement Atzberger, Michele Meroni, Andrew Mude, Michael Carter. Does the design matter? Comparing satellite-based indices for insuring pastoralists against drought. Ecological Economics. 2019; 162 ():59-73.

Chicago/Turabian Style

Nathaniel Jensen; Quentin Stoeffler; Francesco Pietro Fava; Anton Vrieling; Clement Atzberger; Michele Meroni; Andrew Mude; Michael Carter. 2019. "Does the design matter? Comparing satellite-based indices for insuring pastoralists against drought." Ecological Economics 162, no. : 59-73.

Letter
Published: 19 December 2018 in Remote Sensing
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Satellite hyperspectral Earth observation missions have strong potential to support sustainable agriculture by providing accurate spatial and temporal information of important vegetation biophysical and biochemical variables. To meet this goal, possible error sources in the modelling approaches should be minimized. Thus, first of all, the capability of a model to reproduce the measured spectral signals has to be tested before applying any retrieval algorithm. For an exemplary demonstration, the coupled PROSPECT-D and SAIL radiative transfer models (PROSAIL) were employed to emulate the setup of future hyperspectral sensors in the visible and near-infrared (VNIR) spectral regions with a 6.5 nm spectral sampling distance. Model uncertainties were determined to subsequently exclude those wavelengths with the highest mean absolute error (MAE) between model simulation and spectral measurement. The largest mismatch could be found in the green visible and red edge regions, which can be explained by complex interactions of several biochemical and structural variables in these spectral domains. For leaf area index (LAI, m2·m−2) retrieval, results indicated only a small improvement when using optimized spectral samplings. However, a significant increase in accuracy for leaf chlorophyll content (LCC, µg·cm−2) estimations could be obtained, with the relative root mean square error (RMSE) decreasing from 26% (full VNIR range) to 15% (optimized VNIR) for maize and from 77% to 29% for soybean, respectively. We therefore recommend applying a specific model-error threshold (MAE of ~0.01) to stabilize the retrieval of crop biochemical variables.

ACS Style

Katja Berger; Clement Atzberger; Martin Danner; Matthias Wocher; Wolfram Mauser; Tobias Hank. Model-Based Optimization of Spectral Sampling for the Retrieval of Crop Variables with the PROSAIL Model. Remote Sensing 2018, 10, 2063 .

AMA Style

Katja Berger, Clement Atzberger, Martin Danner, Matthias Wocher, Wolfram Mauser, Tobias Hank. Model-Based Optimization of Spectral Sampling for the Retrieval of Crop Variables with the PROSAIL Model. Remote Sensing. 2018; 10 (12):2063.

Chicago/Turabian Style

Katja Berger; Clement Atzberger; Martin Danner; Matthias Wocher; Wolfram Mauser; Tobias Hank. 2018. "Model-Based Optimization of Spectral Sampling for the Retrieval of Crop Variables with the PROSAIL Model." Remote Sensing 10, no. 12: 2063.

Journal article
Published: 06 December 2018 in Remote Sensing of Environment
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For food crises early warning purposes, coarse spatial resolution NDVI data are widely used to monitor vegetation conditions in near real-time (NRT). Different types of NDVI anomalies are typically employed to assess the current state of crops and rangelands as compared to previous years. Timeliness and accuracy of such anomalies are critical factors to an effective monitoring. Temporal smoothing can efficiently reduce noise and cloud contamination in the time series of historical observations, where data points are available before and after each observation to be smoothed. With NRT data, smoothing methods are adapted to cope with the unbalanced availability of data before and after the most recent data points. These NRT approaches provide successive updates of the estimation of the same data point as more observations become available. Anomalies compare the current NDVI value with some statistics (e.g. indicators of central tendency and dispersion) extracted from the historical archive of observations. With multiple updates of the same datasets being available, two options can be selected to compute anomalies, i.e. using the same update level for the NRT data and the statistics or using the most reliable update for the latter. In this study we assess the accuracy of three commonly employed 1 km MODIS NDVI anomalies (standard scores, non-exceedance probability and vegetation condition index) with respect to (1) delay with which they become available and (2) option selected for their computation. We show that a large estimation error affects the earlier estimates and that this error is efficiently reduced in subsequent updates. In addition, with regards to the preferable option to compute anomalies, we empirically observe that it depends on the type of application (e.g. averaging anomalies value over an area of interest vs. detecting “drought” conditions by setting a threshold on the anomaly value) and the employed anomaly type. Finally, we map the spatial pattern in the magnitude of NRT anomaly estimation errors over the globe and relate it to average cloudiness.

ACS Style

Michele Meroni; Dominique Fasbender; Felix Rembold; Clement Atzberger; Anja Klisch. Near real-time vegetation anomaly detection with MODIS NDVI: Timeliness vs. accuracy and effect of anomaly computation options. Remote Sensing of Environment 2018, 221, 508 -521.

AMA Style

Michele Meroni, Dominique Fasbender, Felix Rembold, Clement Atzberger, Anja Klisch. Near real-time vegetation anomaly detection with MODIS NDVI: Timeliness vs. accuracy and effect of anomaly computation options. Remote Sensing of Environment. 2018; 221 ():508-521.

Chicago/Turabian Style

Michele Meroni; Dominique Fasbender; Felix Rembold; Clement Atzberger; Anja Klisch. 2018. "Near real-time vegetation anomaly detection with MODIS NDVI: Timeliness vs. accuracy and effect of anomaly computation options." Remote Sensing of Environment 221, no. : 508-521.

Original article
Published: 01 December 2018 in PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science
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Model-based Selection of hyperspectral EnMAP Channels for optimal Inversion of Radiation Transfer Models in Agriculture. Satellite-based hyperspectral Earth observation data combined with physically based radiative transfer models have the strong potential to support sustainable agriculture by providing accurate spatial and temporal information of important vegetation biophysical and biochemical variables such as leaf chlorophyll content. To meet this goal, possible error sources in the modelling should be minimized. Thus, the capability of a model to reproduce the measured spectral signals has to be tested before applying any retrieval algorithm. For an exemplary demonstration, the PROSAIL model was employed to emulate the setup of the future EnMAP hyperspectral sensor in the visible and near-infrared (VNIR) spectral region with a 6.5 nm spectral sampling distance. Model uncertainties were determined to subsequently exclude those wavelengths with highest mean absolute error (MAE) between model simulation and spectral measurement. For this purpose data from two campaigns were exploited (1) from Nebraska–Lincoln (maize and soybean) and (2) from Munich–North-Isar (maize and winter wheat). A significant increase of accuracy for leaf chlorophyll content (LCC, µg cm−2) estimations could be obtained, with relative RMSE decreasing from 26% (full VNIR range) to 15% (optimized VNIR) for maize and from 77% to 29% for soybean, respectively. We therefore recommend applying a specific model-error threshold (MAE ~ 0.01) to stabilize the retrieval of crop biochemical variables. Zukünftige hyperspektrale Erdbeobachtungsmissionen, kombiniert mit physikalisch basierten Strahlungstransfermodellen, haben ein großes Potential eine nachhaltige Landwirtschaft zu unterstützen. Anhand dieser neuen Aufnahmen können noch genauere räumliche und zeitliche Informationen über wichtige biophysikalische und biochemische Variablen der Vegetation, wie beispielsweise den Blattchlorophyllgehalt, zur Verfügung gestellt werden. Allerdings müssen dafür mögliche Fehlerquellen in der Modellierung minimiert werden. Bevor ein Schätz- bzw. Inversionsalgorithmus angewendet wird, sollte zunächst die Fähigkeit des Modells zur Simulation der gemessenen spektralen Signatur getestet werden. In der aktuellen Studie wurde das weit verbreitete PROSAIL-Modell verwendet, um den zukünftigen EnMAP Hyperspektralsensor im sichtbaren und nahen Infraroten Spektralbereich (VNIR) mit einer spektralen Auflösung von 6,5 nm zu simulieren. Die Abweichungen zwischen Modell und gemessenen Daten wurden beispielhaft an zwei Kampagnen und drei verschiedenen Feldfrüchten (Mais, Winterweizen und Sojabohnen) demonstriert. Diejenigen Wellenlängen mit dem höchsten mittleren absoluten Fehler (MAE) zwischen der Modellsimulation und der Spektralmessung wurden vollautomatisiert mit einem sequentiellen Look-up Table (LUT) basierten Algorithmus (Feature-Selektions-Algorithmus) ausgeschlossen. Für die Bestimmung des Blattchlorophyllgehalts (LCC, µg cm−2) der Nebraska-Kampagne konnten signifikante Verbesserungen der Schätzung erreicht werden: im Fall von Mais sank der relative RMSE (rRMSE) von 26% (gesamter VNIR-Bereich) auf 15% (optimierter VNIR-Bereich), und für Sojabohnen von rRMSE = 77% auf rRMSE = 29%. Auf Grund dieser positiven Ergebnisse empfehlen wir die Anwendung eines spezifischen Modellfehler-Schwellenwerts (MAE ~ 0.01) für den spektralen VNIR Bereich. In diesem Rahmen sollten die spektralen Messungen und Modellsimulationen mindestens übereinstimmen, um eine vertrauenswürdige Schätzung biochemischer Vegetationsvariablen aus zukünftigen Hyperspektralmissionen zu garantieren.

ACS Style

Katja Berger; Clement Atzberger; Martin Danner; Matthias Wocher; Wolfram Mauser; Tobias Hank. Modellbasierte Selektion hyperspektraler EnMAP Kanäle zur optimalen Invertierung von Strahlungstransfermodellen für landwirtschaftliche Kulturen. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science 2018, 86, 263 -272.

AMA Style

Katja Berger, Clement Atzberger, Martin Danner, Matthias Wocher, Wolfram Mauser, Tobias Hank. Modellbasierte Selektion hyperspektraler EnMAP Kanäle zur optimalen Invertierung von Strahlungstransfermodellen für landwirtschaftliche Kulturen. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science. 2018; 86 (5-6):263-272.

Chicago/Turabian Style

Katja Berger; Clement Atzberger; Martin Danner; Matthias Wocher; Wolfram Mauser; Tobias Hank. 2018. "Modellbasierte Selektion hyperspektraler EnMAP Kanäle zur optimalen Invertierung von Strahlungstransfermodellen für landwirtschaftliche Kulturen." PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science 86, no. 5-6: 263-272.

Original research
Published: 21 November 2018 in Ecology and Evolution
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Aim Prosopis spp. are an invasive alien plant species native to the Americas and well adapted to thrive in arid environments. In Kenya, several remote‐sensing studies conclude that the genus is well established throughout the country and is rapidly invading new areas. This research aims to model the potential habitat of Prosopis spp. by using an ensemble model consisting of four species distribution models. Furthermore, environmental and expert knowledge‐based variables are assessed. Location Turkana County, Kenya. Methods We collected and assessed a large number of environmental and expert knowledge‐based variables through variable correlation, collinearity, and bias tests. The variables were used for an ensemble model consisting of four species distribution models: (a) logistic regression, (b) maximum entropy, (c) random forest, and (d) Bayesian networks. The models were evaluated through a block cross‐validation providing statistical measures. Results The best predictors for Prosopis spp. habitat are distance from water and built‐up areas, soil type, elevation, lithology, and temperature seasonality. All species distribution models achieved high accuracies while the ensemble model achieved the highest scores. Highly and moderately suitable Prosopis spp. habitat covers 6% and 9% of the study area, respectively. Main conclusions Both ensemble and individual models predict a high risk of continued invasion, confirming local observations and conceptions. Findings are valuable to stakeholders for managing invaded area, protecting areas at risk, and to raise awareness.

ACS Style

Wai-Tim Ng; Alexsandro Cândido De Oliveira Silva; Purity Rima; Clement Atzberger; Markus Immitzer. Ensemble approach for potential habitat mapping of invasive Prosopis spp. in Turkana, Kenya. Ecology and Evolution 2018, 8, 11921 -11931.

AMA Style

Wai-Tim Ng, Alexsandro Cândido De Oliveira Silva, Purity Rima, Clement Atzberger, Markus Immitzer. Ensemble approach for potential habitat mapping of invasive Prosopis spp. in Turkana, Kenya. Ecology and Evolution. 2018; 8 (23):11921-11931.

Chicago/Turabian Style

Wai-Tim Ng; Alexsandro Cândido De Oliveira Silva; Purity Rima; Clement Atzberger; Markus Immitzer. 2018. "Ensemble approach for potential habitat mapping of invasive Prosopis spp. in Turkana, Kenya." Ecology and Evolution 8, no. 23: 11921-11931.

Articles
Published: 13 November 2018 in International Journal of Remote Sensing
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The aim of this study was to develop a robust methodology to estimate pasture biomass across the huge land surface of Mongolia (1.56 × 106 km2) using high-resolution Landsat 8 satellite data calibrated against field-measured biomass samples. Two widely used regression models were compared and adopted for this study: Partial Least Squares (PLS) and Random Forest (RF). Both methods were trained to predict pasture biomass using a total of 17 spectral indices derived from Landsat 8 multi-temporal satellite imagery as predictor variables. For training, reference biomass data from a field survey of 553 sites were available. PLS results showed a satisfactory correlation between field measured and estimated biomass with coefficient of determination (R2) = 0.750 and Root Mean Square Error (RMSE) = 101.10 kg ha−1. The RF regression gave similar results with R2 = 0.764, RMSE = 98.00 kg ha−1. An examination of feature importance found the following vegetation indices to be the most relevant: Green Chlorophyll Index (CLgreen), Simple Ratio (SR), Wide Dynamic Range Vegetation Index (WDRVI), Enhanced Vegetation Index EVI1 and Normalized Difference Vegetation Index (NDVI) indices. With respect to the spectral reflectances, Red and Short Wavelength Infra-Red2 (SWIR2) bands showed the strongest correlation with biomass. Using the developed PLS models, a spatial map of pasture biomass covering Mongolia at a spatial resolution of 30 m was generated. Our study confirms the high potential of RF and PLS regression (PLSR) models to predict pasture biomass. The computationally simpler PLSR model is preferred for applications involving large regions. This method can be implemented easily, provided that sufficient reference data and cloud-free observations are available.

ACS Style

Munkhdulam Otgonbayar; Clement Atzberger; Jonathan Chambers; Amarsaikhan Damdinsuren. Mapping pasture biomass in Mongolia using Partial Least Squares, Random Forest regression and Landsat 8 imagery. International Journal of Remote Sensing 2018, 40, 3204 -3226.

AMA Style

Munkhdulam Otgonbayar, Clement Atzberger, Jonathan Chambers, Amarsaikhan Damdinsuren. Mapping pasture biomass in Mongolia using Partial Least Squares, Random Forest regression and Landsat 8 imagery. International Journal of Remote Sensing. 2018; 40 (8):3204-3226.

Chicago/Turabian Style

Munkhdulam Otgonbayar; Clement Atzberger; Jonathan Chambers; Amarsaikhan Damdinsuren. 2018. "Mapping pasture biomass in Mongolia using Partial Least Squares, Random Forest regression and Landsat 8 imagery." International Journal of Remote Sensing 40, no. 8: 3204-3226.

Conference paper
Published: 01 November 2018 in 2018 IEEE 10th Latin-American Conference on Communications (LATINCOM)
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The recent impulse in development of artificial intelligence (AI) methodologies has simplified the application of this in multiple research areas. This simplification was not favorable before, due to the limitations in dimensionality, processing time, computational resources, among others. Working with multispectral remote sensing (RS) images, in an artificial neural network (NN) was quite complex. Due the methods used required millions of processes that took a long time to be executed and produce competitive results compared with the state of the art (SoA). Deep learning (DL) strategies have been applied to alleviate these limitations and have greatly improved the use of neural networks. Therefore, this paper presents the analysis of DL-NNs to perform semantic segmentation of multispectral RS images. Images are captured by the constellation of satellites Sentinel-2 from the European Space Agency. The objective of this research is to classify each pixel of a scene into five categories: 1-vegetation, 2-soil, 3-water, 4-clouds and 5-cloud shadows. The selection of spectral bands for the formation of input datasets for segmentation of these classes is very important. The spectral signatures of each material aid to discern among several classes. Results presented in this work, show that the AI strategy proposed offer better accuracy segmentation than other methods of the SoA in competitive processing time.

ACS Style

Josue Lopez; Stewart Santos; Clement Atzberger; Deni Torres. Convolutional Neural Networks for Semantic Segmentation of Multispectral Remote Sensing Images. 2018 IEEE 10th Latin-American Conference on Communications (LATINCOM) 2018, 1 -5.

AMA Style

Josue Lopez, Stewart Santos, Clement Atzberger, Deni Torres. Convolutional Neural Networks for Semantic Segmentation of Multispectral Remote Sensing Images. 2018 IEEE 10th Latin-American Conference on Communications (LATINCOM). 2018; ():1-5.

Chicago/Turabian Style

Josue Lopez; Stewart Santos; Clement Atzberger; Deni Torres. 2018. "Convolutional Neural Networks for Semantic Segmentation of Multispectral Remote Sensing Images." 2018 IEEE 10th Latin-American Conference on Communications (LATINCOM) , no. : 1-5.