A remote sensing-guided forest inventory concept using multispectral 3D and height information from ZiYuan-3 satellite data

Increased frequencies of storms and droughts due to climate change are changing central European forestsmore rapidly than in previous decades. To monitor these changes, multispectral 3D remote sensing (RS) data canprovide relevant information for forest management and inventory. In this case study, data of the multispectral3D-capable satellite system ZiYuan-3 (ZY-3) were used in a RS-guided forest inventory concept to reduce the fieldsample size compared to the standard grid inventory. We first pre-stratified the forest area via the ZY-3 datasetinto coniferous, broadleaved and mixed forest types using object-based image analysis. Each forest type wasthen split into three height strata using the ZY-3 stereo module-derived digital canopy height model (CHM).Due to limited sample sizes, we reduced the nine to six strata. Then, for each of the six strata, we randomlyselected representative segments for inventory plot placement. We then conducted field inventories in theseplots. The collected field data were used to calculate forest attributes, such as tree species composition, timbervolume and canopy height at plot level (terrestrially measured tree height and height information from ZY-3CHM).Subsequently,wecomparedtheresultingforestattributesfromtheRS-guidedinventorywiththereferencedata from a grid inventory based only on field plots. The difference in mean timber volumes to the reference was+30.21 m3ha−1(8.99 per cent) for the RS-guided inventory with terrestrial height and−11.32 m3ha−1(−3.37per cent) with height information from ZY-3 data. The relative efficiency (RE) indicator was used to comparethe different sampling schemes. The RE as compared to a random reduction of the sample size was 1.22 forthe RS-guided inventory with terrestrial height measurements and 1.85 with height information from ZY-3 data.The results show that the presented workflow based on 3D ZY-3 data is suitable to support forest inventories byreducing the sample size and hence potentially increase the inventory frequency

Calibration of DART Radiative Transfer Model with Satellite Images for Simulating Albedo and Thermal Irradiance Images and 3D Radiative Budget of Urban Environment

Remote sensing is increasingly used for managing urban environment. In this context, the H2020 project URBANFLUXES aims to improve our knowledge on urban anthropogenic heat fluxes, with the specific study of three cities: London, Basel and Heraklion. Usually, one expects to derive directly 2 major urban parameters from remote sensing: the albedo and thermal irradiance. However, the determination of these two parameters is seriously hampered by complexity of urban architecture. For example, urban reflectance and brightness temperature are far from isotropic and are spatially heterogeneous. Hence, radiative transfer models that consider the complexity of urban architecture when simulating remote sensing signals are essential tools. Even for these sophisticated models, there is a major constraint for an operational use of remote sensing: the complex 3D distribution of optical properties and temperatures in urban environments. Here, the work is conducted with the DART (Discrete Anisotropic Radiative Transfer) model. It is a comprehensive physically based 3D radiative transfer model that simulates optical signals at the entrance of imaging spectro-radiometers and LiDAR scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental (atmosphere, topography,…) and instrumental (sensor altitude, spatial resolution, UV to thermal infrared,…) configuration. Paul Sabatier University distributes free licenses for research activities. This paper presents the calibration of DART model with high spatial resolution satellite images (Landsat 8, Sentinel 2, etc.) that are acquired in the visible (VIS) / near infrared (NIR) domain and in the thermal infrared (TIR) domain. Here, the work is conducted with an atmospherically corrected Landsat 8 image and Bale city, with its urban database. The calibration approach in the VIS/IR domain encompasses 5 steps for computing the 2D distribution (image) of urban albedo at satellite spatial resolution. (1) DART simulation of satellite image at very high spatial resolution (e.g., 50cm) per satellite spectral band. Atmosphere conditions are specific to the satellite image acquisition. (2) Spatial resampling of DART image at the coarser spatial resolution of the available satellite image, per spectral band. (3) Iterative derivation of the urban surfaces (roofs, walls, streets, vegetation,…) optical properties as derived from pixel-wise comparison of DART and satellite images, independently per spectral band. (4) Computation of the band albedo image of the city, per spectral band. (5) Computation of the image of the city albedo and VIS/NIR exitance, as an integral over all satellite spectral bands. In order to get a time series of albedo and VIS/NIR exitance, even in the absence of satellite images, ECMWF information about local irradiance and atmosphere conditions are used. A similar approach is used for calculating the city thermal exitance using satellite images acquired in the thermal infrared domain. Finally, DART simulations that are conducted with the optical properties derived from remote sensing images give also the 3D radiative budget of the city at any date including the date of the satellite image acquisition

Advances for Urban Planning in Highly Dynamic Environments through very High-Resolution Remote Sensing Approaches

Die fortschreitende Urbanisierung und der Klimawandel stellen Städte und Stadtplanung vor große Herausforderungen. Der Lebensraum für die Bewohner und die Infrastruktur müssen entsprechend den Klimaschutzanforderungen angepasst werden, zudem muss die Resilienz urbaner Räume gegenüber Klimawandelwandelfolgen erhöht werden. Ziel der urbanen Planung und urbanen Infrastrukturplanung ist vor diesem Hintergrund im Auftrag der Gesellschaft Lösungen zu finden, um diesen Anforderungen der Zukunft gerecht zu werden und um lebenswerte Städte mit allen städtischen Funktionen zu gewährleisten. Zudem müssen durch Planer ökonomische und ökologisch geeignete Vorschläge für die Bereitstellung urbaner Infrastruktur gefunden werden, um Grundbedürfnisse zu erfüllen und Slums zurückzudrängen. Gute Planungspraxis erfordert dafür die Entwicklung von Planungsszenarien für angemessene, erfolgreiche und integrierte Lösungen, wobei eine Datenbasis als Entscheidungsgrundlage dienen muss, die durch Datenkonsistenz, -Qualität und -Aktualität als Evidenz für Szenarienentwicklung herangezogen werden kann. In dieser Dissertationsschrift wird durch drei Studien gezeigt, dass die Disziplin der Fernerkundung durch die Verwendung sehr hochaufgelöster Erdbeobachtungsdaten einen Beitrag für erfolgreiche urbane Planung und urbane Infrastrukturplanung leisten kann, indem der Informationsgehalt bisheriger Fernerkundungsansätze unter Verwendung anwendungsfreundlicher Ansätze erhöht werden kann und direkt planungsrelevante Informationen als Evidenz für die Entscheidungsfindung bereitgestellt werden kann. In den hochdynamischen Städten Da Nang (VN) und Belmopan (BZ) konnte an dieser Thematik gearbeitet werden. Durch die Differenzierung photogrammetrisch abgeleiteter Höhenmodelle in sehr hoher Auflösung wurden in Da Nang anstatt flächenhafter Änderungen urbaner Gebiete Dynamiken innerhalb des Gebäudebestands bestimmt und evaluiert. Der Gebäudetyp kann, wie in Belmopan gezeigt, als geeignetes Mittel für Abschätzung sozio-ökonomischer Indikatoren dienen, die in Zusammenhang mit spezifischen Verbräuchen stehen. Mit der Verwendung von Drohnendaten wurde die Bestimmung der Gebäudetypen verbessert und zudem der Zusammenhang zwischen Gebäudetyp und Stromverbrauch gezeigt, wodurch eine Photovoltaikenergie-Bilanzierung auf Einzelgebäudeebene durchgeführt werden konnte

Semantic location extraction from crowdsourced data

Crowdsourced Data (CSD) has recently received increased attention in many application areas including disaster management. Convenience of production and use, data currency and abundancy are some of the key reasons for attracting this high interest. Conversely, quality issues like incompleteness, credibility and relevancy prevent the direct use of such data in important applications like disaster management. Moreover, location information availability of CSD is problematic as it remains very low in many crowd sourced platforms such as Twitter. Also, this recorded location is mostly related to the mobile device or user location and often does not represent the event location. In CSD, event location is discussed descriptively in the comments in addition to the recorded location (which is generated by means of mobile device's GPS or mobile communication network). This study attempts to semantically extract the CSD location information with the help of an ontological Gazetteer and other available resources. 2011 Queensland flood tweets and Ushahidi Crowd Map data were semantically analysed to extract the location information with the support of Queensland Gazetteer which is converted to an ontological gazetteer and a global gazetteer. Some preliminary results show that the use of ontologies and semantics can improve the accuracy of place name identification of CSD and the process of location information extraction

Mapping drought stress in commercial eucalyptus forest plantations using remotely sensed techniques in Southern Africa.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Drought is one of the least understood and hazardous natural disasters that leave many parts of the world devastated. To improve understanding and detection of drought onset, remote sensing technology is required to map drought affected areas as it covers large geographical areas. The study aimed to evaluate the utility of a cost-effective Landsat 8 imagery in mapping the spatial extent of drought prone Eucalyptus dunnii plantations. The first objective was to compare the utility of Landsat spectra with a combination of vegetation indices to detect drought affected plantations using the Stochastic gradient boosting algorithm. The test datasets showed that using Landsat 8 spectra only produced an overall accuracy of 74.70% and a kappa value of 0.59. The integration of Landsat 8 spectra with vegetation indices produced an overall accuracy of 83.13% and a kappa of 0.76. The second objective of this study was to do a trend analysis of vegetation health during drought. The normalized difference vegetation index (NDVI) values fluctuated over the years where 2013 had the highest value of 0.68 and 2015 the lowest NDVI of 0.55 and the normalized difference water index (NDWI) had the lowest value in 2015. Most indices showed a similar trend where 2013 had the highest index value and 2015 the lowest. The third objective was to do a trend analysis of rainfall and temperature during drought. The rainfall trend analysis from 2013 to 2017 indicated that the month of February 2017 received the highest rainfall of 154 mm. In addition, July of 2016 received the highest rainfall compared to 2013, 2014, 2015 and 2017 with rainfalls of 6.4 mm, 0.6 mm, 28 mm, and 1 mm, respectively. The temperature trend analysis from 2013 to 2017 indicated that December 2015 had the highest temperature of 28 ° C compared to December of 2013 2014, 2016 and 2017 with temperatures of 24°C, 25°C, 27°C, 24°C, respectively. Furthermore, it was also noted that June 2017 had the highest temperature of 23°C while June 2015 had the lowest at 20°C. The fourth objective of this study was to compare the utility of topographical variables with a combination of Landsat vegetation indices to detect drought affected plantations using the One class support vector machine algorithm. The multiclass support vector machine using Landsat vegetation indices and topographical variables produced an overall accuracy of 73.86% and a kappa value of 0.71 with user’s and producer’s accuracies ranging between 61% to 69% for drought damaged trees, while for healthy trees ranged from 84% to 90%. The one class support vector machine using Landsat vegetation indices and topographical variables produced an overall accuracy of 82.35% and a kappa value of 0.73. The one class support vector machine produced the highest overall accuracy compared to the multiclass SVM and stochastic gradient boosting algorithm. The use of topographical variables further improved the accuracies compared to the combination of Landsat spectra with vegetation indices

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Depth Estimation Using 2D RGB Images

Single image depth estimation is an ill-posed problem. That is, it is not mathematically possible to uniquely estimate the 3rd dimension (or depth) from a single 2D image. Hence, additional constraints need to be incorporated in order to regulate the solution space. As a result, in the first part of this dissertation, the idea of constraining the model for more accurate depth estimation by taking advantage of the similarity between the RGB image and the corresponding depth map at the geometric edges of the 3D scene is explored. Although deep learning based methods are very successful in computer vision and handle noise very well, they suffer from poor generalization when the test and train distributions are not close. While, the geometric methods do not have the generalization problem since they benefit from temporal information in an unsupervised manner. They are sensitive to noise, though. At the same time, explicitly modeling of a dynamic scenes as well as flexible objects in traditional computer vision methods is a big challenge. Considering the advantages and disadvantages of each approach, a hybrid method, which benefits from both, is proposed here by extending traditional geometric models’ abilities to handle flexible and dynamic objects in the scene. This is made possible by relaxing geometric computer vision rules from one motion model for some areas of the scene into one for every pixel in the scene. This enables the model to detect even small, flexible, floating debris in a dynamic scene. However, it makes the optimization under-constrained. To change the optimization from under-constrained to over-constrained while maintaining the model’s flexibility, ”moving object detection loss” and ”synchrony loss” are designed. The algorithm is trained in an unsupervised fashion. The primary results are in no way comparable to the current state of the art. Because the training process is so slow, it is difficult to compare it to the current state of the art. Also, the algorithm lacks stability. In addition, the optical flow model is extremely noisy and naive. At the end, some solutions are suggested to address these issues

Dipterocarps protected by Jering local wisdom in Jering Menduyung Nature Recreational Park, Bangka Island, Indonesia

Apart of the oil palm plantation expansion, the Jering Menduyung Nature Recreational Park has relatively diverse plants. The 3,538 ha park is located at the north west of Bangka Island, Indonesia. The minimum species-area curve was 0.82 ha which is just below Dalil conservation forest that is 1.2 ha, but it is much higher than measurements of several secondary forests in the Island that are 0.2 ha. The plot is inhabited by more than 50 plant species. Of 22 tree species, there are 40 individual poles with the average diameter of 15.3 cm, and 64 individual trees with the average diameter of 48.9 cm. The density of Dipterocarpus grandiflorus (Blanco) Blanco or kruing, is 20.7 individual/ha with the diameter ranges of 12.1 – 212.7 cm or with the average diameter of 69.0 cm. The relatively intact park is supported by the local wisdom of Jering tribe, one of indigenous tribes in the island. People has regulated in cutting trees especially in the cape. The conservation agency designates the park as one of the kruing propagules sources in the province. The growing oil palm plantation and the less adoption of local wisdom among the youth is a challenge to forest conservation in the province where tin mining activities have been the economic driver for decades. More socialization from the conservation agency and the involvement of university students in raising environmental awareness is important to be done

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

12th International Conference on Geographic Information Science: GIScience 2023, September 12–15, 2023, Leeds, UK

No abstract available