Complex land cover classifications and physical properties retrieval of tropical forests using multi-source remote sensing

The work presented in this thesis mainly focuses on two subjects related to the application of remote sensing data: (1) for land cover classification combining optical sensor, texture features generated from spectral information and synthetic aperture radar (SAR) features, and (2) to develop a non-destructive approach for above ground biomass (AGB) and forest attributes estimation employing multi-source remote sensing data (i.e. optical data, SAR backscatter) combined with in-situ data. Information provided by reliable land cover map is useful for management of forest resources to support sustainable forest management, whereas the generation of the non-destructive approach to model forest biophysical properties (e.g. AGB and stem volume) is required to assess the forest resources more efficiently and cost-effective, and coupled with remote sensing data the model can be applied over large forest areas. This work considers study sites over tropical rain forest landscape in Indonesia characterized by different successional stages and complex vegetation structure including tropical peatland forests. The thesis begins with a brief introduction and the state of the art explaining recent trends on monitoring and modeling of forest resources using remote sensing data and approach. The research works on the integration of spectral information and texture features for forest cover mapping is presented subsequently, followed by development of a non-destructive approach for AGB and forest parameters predictions and modeling. Ultimately, this work evaluates the potential of mosaic SAR data for AGB modeling and the fusion of optical and SAR data for peatlands discrimination. The results show that the inclusion of geostatistics texture features improved the classification accuracy of optical Landsat ETM data. Moreover, the fusion of SAR and optical data enhanced the peatlands discrimination over tropical peat swamp forest. For forest stand parameters modeling, neural networks method resulted in lower error estimate than standard multi-linear regression technique, and the combination of non-destructive measurement (i.e. stem number) and remote sensing data improved the model accuracy. The up scaling of stem volume and biomass estimates using Kriging method and bi-temporal ETM image also provide favorable estimate results upon comparison with the land cover map.Die in dieser Dissertation präsentierten Ergebnisse konzentrieren sich hauptsächlich auf zwei Themen mit Bezug zur angewandten Fernerkundung: 1) Der Klassifizierung von Oberflächenbedeckung basierend auf der Verknüpfung von optischen Sensoren, Textureigenschaften erzeugt durch Spektraldaten und Synthetic-Aperture-Radar (SAR) features und 2) die Entwicklung eines nichtdestruktiven Verfahrens zur Bestimmung oberirdischer Biomasse (AGB) und weiterer Waldeigenschaften mittels multi-source Fernerkundungsdaten (optische Daten, SAR Rückstreuung) sowie in-situ Daten. Eine zuverlässige Karte der Landbedeckung dient der Unterstützung von nachhaltigem Waldmanagement, während eine nichtdestruktive Herangehensweise zur Modellierung von biophysikalischen Waldeigenschaften (z.B. AGB und Stammvolumen) für eine effiziente und kostengünstige Beurteilung der Waldressourcen notwendig ist. Durch die Kopplung mit Fernerkundungsdaten kann das Modell auf große Waldflächen übertragen werden. Die vorliegende Arbeit berücksichtigt Untersuchungsgebiete im tropischen Regenwald Indonesiens, welche durch verschiedene Regenerations- und Sukzessionsstadien sowie komplexe Vegetationsstrukturen, inklusive tropischer Torfwälder, gekennzeichnet sind. Am Anfang der Arbeit werden in einer kurzen Einleitung der Stand der Forschung und die neuesten Forschungstrends in der Überwachung und Modellierung von Waldressourcen mithilfe von Fernerkundungsdaten dargestellt. Anschließend werden die Forschungsergebnisse der Kombination von Spektraleigenschaften und Textureigenschaften zur Waldbedeckungskartierung erläutert. Desweiteren folgen Ergebnisse zur Entwicklung eines nichtdestruktiven Ansatzes zur Vorhersage und Modellierung von AGB und Waldeigenschaften, zur Auswertung von Mosaik- SAR Daten für die Modellierung von AGB, sowie zur Fusion optischer mit SAR Daten für die Identifizierung von Torfwäldern. Die Ergebnisse zeigen, dass die Einbeziehung von geostatistischen Textureigenschaften die Genauigkeit der Klassifikation von optischen Landsat ETM Daten gesteigert hat. Desweiteren führte die Fusion von SAR und optischen Daten zu einer Verbesserung der Unterscheidung zwischen Torfwäldern und tropischen Sumpfwäldern. Bei der Modellierung der Waldparameter führte die Neural-Network-Methode zu niedrigeren Fehlerschätzungen als die multiple Regressions. Die Kombination von nichtdestruktiven Messungen (z.B. Stammzahl) und Fernerkundungsdaten führte zu einer Steigerung der Modellgenauigkeit. Die Hochskalierung des Stammvolumens und Schätzungen der Biomasse mithilfe von Kriging und bi-temporalen ETM Daten lieferten positive Schätzergebnisse im Vergleich zur Landbedeckungskarte

Remote Sensing in Agriculture: State-of-the-Art

The Special Issue on “Remote Sensing in Agriculture: State-of-the-Art” gives an exhaustive overview of the ongoing remote sensing technology transfer into the agricultural sector. It consists of 10 high-quality papers focusing on a wide range of remote sensing models and techniques to forecast crop production and yield, to map agricultural landscape and to evaluate plant and soil biophysical features. Satellite, RPAS, and SAR data were involved. This preface describes shortly each contribution published in such Special Issue

A collaborative change detection approach on multi-sensor spatial imagery for desertwetland monitoring after a flash flood in Southern Morocco

© 2019 by the authors. This study aims to present a technique that combines multi-sensor spatial data to monitor wetland areas after a flash-flood event in a Saharan arid region. To extract the most efficient information, seven satellite images (radar and optical) taken before and after the event were used. To achieve the objectives, this study used Sentinel-1 data to discriminate water body and soil roughness, and optical data to monitor the soil moisture after the event. The proposed method combines two approaches: one based on spectral processing, and the other based on categorical processing. The first step was to extract four spectral indices and utilize change vector analysis on multispectral diachronic images from three MSI Sentinel-2 images and two Landsat-8 OLI images acquired before and after the event. The second step was performed using pattern classification techniques, namely, linear classifiers based on support vector machines (SVM) with Gaussian kernels. The results of these two approaches were fused to generate a collaborative wetland change map. The application of co-registration and supervised classification based on textural and intensity information from Radar Sentinel-1 images taken before and after the event completes this work. The results obtained demonstrate the importance of the complementarity of multi-sensor images and a multi-approach methodology to better monitor changes to a wetland area after a flash-flood disaster

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 140

This bibliography lists 306 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1975

Predicting Microhabitat Suitability for an Endangered Small Mammal Using Sentinel-2 Data

Accurate mapping is a main challenge for endangered small-sized terrestrial species. Freely available spatio-temporal data at high resolution from multispectral satellite offer excellent opportunities for improving predictive distribution models of such species based on fine-scale habitat features, thus making it easier to achieve comprehensive biodiversity conservation goals. However, there are still few examples showing the utility of remote-sensing-based products in mapping microhabitat suitability for small species of conservation concern. Here, we address this issue using Sentinel-2 sensor-derived habitat variables, used in combination with more commonly used explanatory variables (e.g., topography), to predict the distribution of the endangered Cabrera vole (Microtus cabrerae) in agrosilvopastorial systems. Based on vole surveys conducted in two different seasons over a ~176,000 ha landscape in Southern Portugal, we assessed the significance of each predictor in explaining Cabrera vole occurrence using the Boruta algorithm, a novel Random forest variant for dealing with high dimensionality of explanatory variables. Overall, results showed a strong contribution of Sentinel-2-derived variables for predicting microhabitat suitability of Cabrera voles. In particular, we found that photosynthetic activity (NDI45), specific spectral signal (SWIR1), and landscape heterogeneity (Rao’s Q) were good proxies of Cabrera voles’ microhabitat, mostly during temporally greener and wetter conditions. In addition to remote-sensing-based variables, the presence of road verges was also an important driver of voles’ distribution, highlighting their potential role as refuges and/or corridors. Overall, our study supports the use of remote-sensing data to predict microhabitat suitability for endangered small-sized species in marginal areas that potentially hold most of the biodiversity found in human-dominated landscapes. We believe our approach can be widely applied to other species, for which detailed habitat mapping over large spatial extents is difficult to obtain using traditional descriptors. This would certainly contribute to improving conservation planning, thereby contributing to global conservation efforts in landscapes that are managed for multiple purposes