Hyper Spectral Remote Sensing of Tropical and Sub-Tropical Forest (Editors: Margaret Kalacsca & G. Arturo Sances–Publisher: Azofeita CRC Press, Year 2008, 320 pages)

It is estimated that most of the problems in forestry associated with the spatial attributes.    From the perspective of forest function that includes production, ecological, and social functions, the spatial aspects has always been a very important part. In Indonesia, the forestry areas is always dealing with very large areas which is mostly inaccessible due to limitation of roads, mountainous with steep slopes, cliffs, hills or wetland such as peat, swamp or marsh.  This condition makes it difficult to collect the data in quick manner comprehensively with low cost. Veronique et al. (2012) recognized that remote technology may provide objective, practical and cost-effective solution.   Currently, one of the most reliable data source that can be repetitively acquired with a unique and consistent traits are those derived from satellite imageries.  It had been known that since the 1990s, earth resources remote sensing sensor is progressively developed either with finer spatial resolution, higher spectral resolution, more frequent revisit or wider dynamic range.   The advent of high spectral resolution (e.g. hyperspectral) is quite challenging and prospectively gives a significant contribution, especially in forest management with higher level of detailed information.  Without having adequate spatial information supported by strong scientific arguments, the forestry sector will be persistently pressured by many other sectors

Urban forestry planning using remote sensing/GIS technique

Urban forestry has become an important value, not only for the aesthetic but also their effectiveness in the environmental control and health. There is a potential to plan and develop urban forest landscape in Malaysian cities due to her richness in plant biodiversity. The advances in remote sensing technology and geographic information system (GIS) technique have provided an effective tool not just for monitoring the change of environment but also very useful for planning, managing and developing of urban forest landscaping. This study was undertaken to assess the capability of integrating remote sensing and GIS to provide information for urban forest potential sites surrounding Kuala Lumpur International Airport (KLIA) and its vicinity. Landsat TM imagery scene 126.58 (path/row) in the form of computer compatible tape (CCT) taken in May 1996 was digitally processed and analysed using a PC-based PCI EASIPACE software system version 6.2. Ancillary data such as topographical map, land use map and soil series map were used to support the satellite data.Integrating satellite data and GIS produced a map 'showing the potential site for urban forest landscaping at KLIA. Future studies should attempt to utilize airborne hyperspectral high-resolution data for more accurate mapping and landscape planning process

GIS and Remote Sensing for Mangroves Mapping and Monitoring

Malaysia is one of the few South East Asian counties with large tracts of mangroves. They provide ecosystem goods and services to the environment and the surroundings regarding shoreline stabilization, storm protection, water quality maintenance, micro-climate stabilization, recreation, tourism, fishing and supply of various forest products. Despite extensive distribution of the mangroves, threats posed by different land use activities are inevitable. Therefore, knowledge on mangroves distribution and change is importance for effective management and making protection policies. Although remote sensing (RS) and geographic information system (GIS) has been widely used to characterize and monitor mangroves change over a range of spatial and temporal scales, studies on mangroves change in Malaysia is lacking. Effective mangrove management is vital via acquiring knowledge on forest distribution and changes to establish protection policies. This chapter will elaborate technically how GIS and RS were utilized to identify, map, and monitor changes of mangroves ecosystem in Malaysia. It also highlights how GIS can enhance the current governance and regulations related to forestry in Malaysia

High spatial resolution and hyperspectral remote sensing for mapping vegetation species in tropical rainforest

The focus of this study is on vegetation species mapping using high spatial resolution IKONOS-2 and digital Color Infrared (CIR) Aerial Photos (spatial resolution 4 m for IKONOS-2 and 20 cm for CIR) and Hyperion Hyperspectral data (spectral resolution 10 nm) in Pasoh Forest Reserve, Negeri Sembilan. Spatial and spectral separability in distinguishing vegetation species were investigated prior to vegetation species mapping to provide optimal vegetation species discrimination. A total of 88 selected vegetation species and common timber groups of the dominant family Dipterocarpaceae with diameter at breast height more than 30 cm were used in this study, where trees spectra were collected by both in situ and laboratory measurements of foliar samples. The trees spectra were analysed using first and second order derivative analysis together with scatter matrix plot based on multiobjective optimization algorithm to identify the best separability and sensitive wavelength portions for vegetation species mapping. In high spatial resolution data mapping, both IKONOS-2 and CIR data were classified by supervised classification approach using maximum likelihood and neural network classifiers, while the Hyperion data was classified by spectral angle mapper and linear mixture modeling. Results of this study indicate that only a total of ten common timber group of dominant Dipterocarpaceae genus were able to be recognized at significant divergence. Both high spatial resolution data (IKONOS-2 and CIR) gave very good classification accuracy of more than 83%. The classified hyperspectral data at 30 m spatial resolution gave a classification accuracy of 65%, hence confirming that spatial resolution is more sensitive in identification of tree genus. However, for species mapping, both high spatial and spectral remotely sensed data used are marginally less sensitive than at genus level

A synergetic approach to burned area mapping using maximum entropy modeling trained with hyperspectral data and VIIRS hotspots

Producción CientíficaSouthern European countries, particularly Spain, are greatly affected by forest fires each year. Quantification of burned area is essential to assess wildfire consequences (both ecological and socioeconomic) and to support decision making in land management. Our study proposed a new synergetic approach based on hotspots and reflectance data to map burned areas from remote sensing data in Mediterranean countries. It was based on a widely used species distribution modeling algorithm, in particular the Maximum Entropy (MaxEnt) one-class classifier. Additionally, MaxEnt identifies variables with the highest contribution to the final model. MaxEnt was trained with hyperspectral indexes (from Earth-Observing One (EO-1) Hyperion data) and hotspot information (from Visible Infrared Imaging Radiometer Suite Near Real-Time 375 m active fire product). Official fire perimeter measurements by Global Positioning System acted as a ground reference. A highly accurate burned area estimation (overall accuracy = 0.99%) was obtained, and the indexes which most contributed to identifying burned areas included Simple Ratio (SR), Red Edge Normalized Difference Vegetation Index (NDVI750), Normalized Difference Water Index (NDWI), Plant Senescence Reflectance Index (PSRI), and Normalized Burn Ratio (NBR). We concluded that the presented methodology enables accurate burned area mapping in Mediterranean ecosystems and may easily be automated and generalized to other ecosystems and satellite sensors.Ministerio de Economía, Industria y Competitividad (grant AGL2017-86075-C2-1-R)Junta de Castilla y León (project LE001P17

Exploring the Potential of Feature Selection Methods in the Classification of Urban Trees Using Field Spectroscopy Data

Mapping of vegetation at the species level using hyperspectral satellite data can be effective and accurate because of its high spectral and spatial resolutions that can detect detailed information of a target object. Its wide application, however, not only is restricted by its high cost and large data storage requirements, but its processing is also complicated by challenges of what is known as the Hughes effect. The Hughes effect is where classification accuracy decreases once the number of features or wavelengths passes a certain limit. This study aimed to explore the potential of feature selection methods in the classification of urban trees using field hyperspectral data. We identified the best feature selection method of key wavelengths that respond to the target urban tree species for effective and accurate classification. The study compared the effectiveness of Principal Component Analysis Discriminant Analysis (PCA-DA), Partial Least Squares Discriminant Analysis (PLS-DA) and Guided Regularized Random Forest (GRRF) in feature selection of the key wavelengths for classification of urban trees. The classification performance of Random Forest (RF) and Support Vector Machines (SVM) algorithms were also compared to determine the importance of the key wavelengths selected for the detection of the target urban trees. The feature selection methods managed to reduce the high dimensionality of the hyperspectral data. Both the PCA-DA and PLS-DA selected 10 wavelengths and the GRRF algorithm selected 13 wavelengths from the entire dataset (n = 1523). Most of the key wavelengths were from the short-wave infrared region (1300-2500 nm). SVM outperformed RF in classifying the key wavelengths selected by the feature selection methods. The SVM classifier produced overall accuracy values of 95.3%, 93.3% and 86% using the GRRF, PLS-DA and PCA-DA techniques, respectively, whereas those for the RF classifier were 88.7%, 72% and 56.8%, respectively

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

Using remote sensing to explore the spectral and spatial characteristics of wetland vegetation.

Wetlands play an important role as ecotones between terrestrial and aquatic habitats and, as a result, represent an environment of high biodiversity and important hydrological function. Ecological understanding in these environments is hampered by difficult terrain and the dynamic and heterogeneous nature of the vegetation. Remote sensing can provide large amounts of contemporaneous data quickly, objectively and over large areas. This study utilises remote sensing data in conjunction with field data and habitat maps derived from traditional ecological surveys to investigate the use of remote sensing as a tool to aid the ecological understanding and monitoring of wetland environments. This study investigated three main objectives; the first two involved the use of field spectrometry from six habitat types in a freshwater wetland in the north of Scotland. Multivariate analyses demonstrated the possibility of distinguishing between these habitat types using field spectra alone. Detailed vegetation datasets were also collected and the relationship between these and variation in the associated spectra was investigated. Significant relationships were established between ordination axes and spectral bands in the green and NIR regions of the spectrum. Results also demonstrated the potential for remote sensing data to characterise the nature of habitat boundaries. The third objective involved the use of airborne imagery to classify remote sensing data into ecologically meaningful classes. Classification accuracies of over 70% were obtained. Work over the last decade has seen a bridging of the relationship between remote sensing and ecology although it is widely acknowledged that our ecological understanding of the remote sensing-vegetation relationship is still limited at many scales and in many ecosystems, not least the wetland environment. This study provides a much needed basis to research in this cross-disciplinary field and identifies further areas that would benefit from future work