Hyperspectral classification of Cyperus esculentus clones and morphologically similar weeds

Cyperus esculentus (yellow nutsedge) is one of the world's worst weeds as it can cause great damage to crops and crop production. To eradicate C. esculentus, early detection is key-a challenging task as it is often confused with other Cyperaceae and displays wide genetic variability. In this study, the objective was to classify C. esculentus clones and morphologically similar weeds. Hyperspectral reflectance between 500 and 800 nm was tested as a measure to discriminate between (I) C. esculentus and morphologically similar Cyperaceae weeds, and between (II) different clonal populations of C. esculentus using three classification models: random forest (RF), regularized logistic regression (RLR) and partial least squares-discriminant analysis (PLS-DA). RLR performed better than RF and PLS-DA, and was able to adequately classify the samples. The possibility of creating an affordable multispectral sensing tool, for precise in-field recognition of C. esculentus plants based on fewer spectral bands, was tested. Results of this study were compared against simulated results from a commercially available multispectral camera with four spectral bands. The model created with customized bands performed almost equally well as the original PLS-DA or RLR model, and much better than the model describing multispectral image data from a commercially available camera. These results open up the opportunity to develop a dedicated robust tool for C. esculentus recognition based on four spectral bands and an appropriate classification model

Remote sensing liana infestation in an aseasonal tropical forest:addressing mismatch in spatial units of analyses

The ability to accurately assess liana (woody vine) infestation at the landscape level is essential to quantify their impact on carbon dynamics and help inform targeted forest management and conservation action. Remote sensing techniques provide potential solutions for assessing liana infestation at broader spatial scales. However, their use so far has been limited to seasonal forests, where there is a high spectral contrast between lianas and trees. Additionally, the ability to align the spatial units of remotely sensed data with canopy observations of liana infestation requires further attention. We combined airborne hyperspectral and LiDAR data with a neural network machine learning classification to assess the distribution of liana infestation at the landscape‐level across an aseasonal primary forest in Sabah, Malaysia. We tested whether an object‐based classification was more effective at predicting liana infestation when compared to a pixel‐based classification. We found a stronger relationship between predicted and observed liana infestation when using a pixel‐based approach (RMSD = 27.0% ± 0.80) in comparison to an object‐based approach (RMSD = 32.6% ± 4.84). However, there was no significant difference in accuracy for object‐ versus pixel‐based classifications when liana infestation was grouped into three classes; Low [0–30%], Medium [31–69%] and High [70–100%] (McNemar’s χ2 = 0.211, P = 0.65). We demonstrate, for the first time, that remote sensing approaches are effective in accurately assessing liana infestation at a landscape scale in an aseasonal tropical forest. Our results indicate potential limitations in object‐based approaches which require refinement in order to accurately segment imagery across contiguous closed‐canopy forests. We conclude that the decision on whether to use a pixel‐ or object‐based approach may depend on the structure of the forest and the ultimate application of the resulting output. Both approaches will provide a valuable tool to inform effective conservation and forest management

Mapping urban tree species in a tropical environment using airborne multispectral and LiDAR data

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesAccurate and up-to-date urban tree inventory is an essential resource for the development of strategies towards sustainable urban planning, as well as for effective management and preservation of biodiversity. Trees contribute to thermal comfort within urban centers by lessening heat island effect and have a direct impact in the reduction of air pollution. However, mapping individual trees species normally involves time-consuming field work over large areas or image interpretation performed by specialists. The integration of airborne LiDAR data with high-spatial resolution and multispectral aerial image is an alternative and effective approach to differentiate tree species at the individual crown level. This thesis aims to investigate the potential of such remotely sensed data to discriminate 5 common urban tree species using traditional Machine Learning classifiers (Random Forest, Support Vector Machine, and k-Nearest Neighbors) in the tropical environment of Salvador, Brazil. Vegetation indices and texture information were extracted from multispectral imagery, and LiDAR-derived variables for tree crowns, were tested separately and combined to perform tree species classification applying three different classifiers. Random Forest outperformed the other two classifiers, reaching overall accuracy of 82.5% when using combined multispectral and LiDAR data. The results indicate that (1) given the similarity in spectral signature, multispectral data alone is not sufficient to distinguish tropical tree species (only k-NN classifier could detect all species); (2) height values and intensity of crown returns points were the most relevant LiDAR features, combination of both datasets improved accuracy up to 20%; (3) generation of canopy height model derived from LiDAR point cloud is an effective method to delineate individual tree crowns in a semi-automatic approach

Exploiting hyperspectral and multispectral images in the detection of tree species: A review

Classification of tree species provides important data in forest monitoring, sustainable forest management and planning. The recent developments in Multi Spectral (MS) and Hyper Spectral (HS) Imaging sensors in remote sensing have made the detection of tree species easier and accurate. With this systematic review study, it is aimed to understand the contribution of using the Multi Spectral and Hyper Spectral Imaging data in the detection of tree species while highlighting recent advances in the field and emphasizing important directions together with new possibilities for future inquiries. In this review, researchers and decision makers will be informed in two different subjects: First one is about the processing steps of exploiting Multi Spectral and HS images and the second one is about determining the advantages of exploiting Multi Spectral and Hyper Spectral images in the application area of detecting tree species. In this way exploiting satellite data will be facilitated. This will also provide an economical gain for using commercial Multi Spectral and Hyper Spectral Imaging data. Moreover, it should be also kept in mind that, as the number of spectral tags that will be obtained from each tree type are different, both the processing method and the classification method will change accordingly. This review, studies were grouped according to the data exploited (only Hyper Spectral images, only Multi Spectral images and their combinations), type of tree monitored and the processing method used. Then, the contribution of the image data used in the study was evaluated according to the accuracy of classification, the suitable type of tree and the classification method

Detection of spatial and temporal patterns of liana infestation using satellite-derived imagery

Lianas (woody vines) play a key role in tropical forest dynamics because of their strong influence on tree growth, mortality and regeneration. Assessing liana infestation over large areas is critical to understand the factors that drive their spatial distribution and to monitor change over time. However, it currently remains unclear whether satellite-based imagery can be used to detect liana infestation across closed-canopy forests and therefore if satellite-observed changes in liana infestation can be detected over time and in response to climatic conditions. Here, we aim to determine the efficacy of satellite-based remote sensing for the detection of spatial and temporal patterns of liana infestation across a primary and selectively logged aseasonal forest in Sabah, Borneo. We used predicted liana infestation derived from airborne hyperspectral data to train a neural network classification for prediction across four Sentinel-2 satellite-based images from 2016 to 2019. Our results showed that liana infestation was positively related to an increase in Greenness Index (GI), a simple metric relating to the amount of photosynthetically active green leaves. Furthermore, this relationship was observed in different forest types and during (2016), as well as after (2017–2019), an El Niño-induced drought. Using a neural network classification, we assessed liana infestation over time and showed an increase in the percentage of severely (>75%) liana infested pixels from 12.9% ± 0.63 (95% CI) in 2016 to 17.3% ± 2 in 2019. This implies that reports of increasing liana abundance may be more wide-spread than currently assumed. This is the first study to show that liana infestation can be accurately detected across closed-canopy tropical forests using satellite-based imagery. Furthermore, the detection of liana infestation during both dry and wet years and across forest types suggests this method should be broadly applicable across tropical forests. This work therefore advances our ability to explore the drivers responsible for patterns of liana infestation at multiple spatial and temporal scales and to quantify liana-induced impacts on carbon dynamics in tropical forests globally

Integrating Remote Sensing Techniques into Forest Monitoring: Selected Topics with a Focus on Thermal Remote Sensing

A sustainable management of natural resources, in particular of forests, is of great importance to preserve the ecological, environmental and economic benefits of forests for future generations. An enhanced understanding of the current situation and ongoing trends of forests, e.g. through policy interventions, is crucial to managing the forest wisely. In this context, forest monitoring is essential for collecting the base data required and for observing trends. Despite the wide range of approved methods and techniques for both close-range and satellite-based remote sensing monitoring, ongoing forest monitoring research is still grappling with specific and unresolved questions: The data acquired must be more reliable, in particular over a long-term period; costs need to be reduced through advancements in both methods and technology that offer easier and more feasible ways of interpreting data. This thesis comprises a number of focused studies, each with their individual and specific research questions, and aims to explore the benefits of innovative methods and technologies. The main emphasis of the studies presented is the integration of close-range and satellite-based remote sensing for enhancing the efficiency of forest monitoring. Manuscript I discusses thermal canopy photography, a new field of application. This approach takes advantage of the large differences in temperature between sky and non-sky pixels and overcomes the inconsistencies of finding an optimal threshold. For an unambiguously separation of “sky” and “non-sky” pixels, a global threshold of 0 °C was defined. Currently, optical or hemispherical canopy photography is the most widely used method to extract crown-related variables. However, a number of aspects, such as exposure, illumination conditions, and threshold definition present a challenge in optical canopy photography and dramatically influence the result; consequently, a comparison of the results from optical canopy photography at a different point in time derived is not advisable. For forest monitoring, where repeated measurements of the canopy cover on the same plots were undertaken, it is therefore of utmost importance to devise a standard protocol to estimate changes in and compare the canopy covers. This paper offers such a protocol by introducing thermal canopy photography. A feasible and accurate method that examines the strong correlation (R2 = 0.96) of canopy closure values derived from thermal and optical image pairs. Thermal photography, as a close-range remote sensing technique, also aids data collection and analysis in other contexts, for instance to expand our knowledge about bamboo tree species: Information about the maturity of bamboo culms is of utmost importance for managing bamboo stands because only then the process of lignification is finished and the culm is technically stronger and more resistant to insect and fungi attacks. The findings of a study (Manuscript III) conducted in Pereira, Colombia, show small differences in culm surface temperature between culms of different ages for the bamboo species Guadua angustifolia K., which may be a sign of maturity. The surface temperature of 12 culms was measured after sunrise using the thermal camera system FLIR 60Ebx. This study shows an innovative close-range remote sensing technique which may support researchers’ determination of the maturity of bamboo culms. This research is in its inception phase and our results are the first of this kind. In the context of analyzing, in particular of thermal imagery time-series data, Manuscript (IV) offers a new methodology using advanced statistical methods. Otsu Thresholding, an automatic segmentation technique is used in a first processing step. O’Sullivan penalized splines estimated the temperature profile extracted from the canopy leaf temperature. A final comparison of the different profiles is done by constructing simultaneous confidence bands. The result shows an approximately significant difference in canopy leaf temperature. For this study, we successfully cooperated with the Center for Statistics at Göttingen University (Prof. Kneib). The second close-range remote sensing technology employed in this thesis is terrestrial laser scanning which is used here to enhance our understanding about buttressed trees. Big trees with an irregular non-convex shape are important contributors to aboveground biomass in tropical forests, but an accurate estimation of their biomass is still a challenge and often remains biased. Allometric equations including tree diameter and height as predictors are currently used in tropical forests, but they are often not calibrated for such large and irregular trees where measuring the diameter is quite difficult. Against this background, Manuscript II shows the result of the 3D-analysis of 12 buttressed trees. This study was conducted in the Botanical Garden of Bogor, Indonesia, using a state-of-the-art terrestrial laser scanner. The findings allow for new insights into the irregular geometry of buttressed trees and the methodological approach employed in this paper will help to improve volume and biomass models for this kind of tree. The results suggest a strong relationship (R² = 0.87) between cross-sectional areas at diameter above buttress (DAB) height and the actual tree basal area measured at 1.3 m height. The accuracy of field biomass estimates is crucial if the data are used to calibrate models to predict the forest biomass on landscape level using remote sensing imagery. The linkage between technology and methodology in the context of forest monitoring remote sensing enhance our knowledge in extracting more reliable information on tree cover estimation. The pre-processing of satellite images plays a crucial role in the processing workflow and particularly the illumination correction has a direct effect on the estimated tree cover. Manuscript IV evaluates four DEMs (Pleiades DSM, SRTM30, SRTM V4.1 and SRTM-X) that are available for the area of Shitai County (Anhui Province, Southeast China) for the purpose of an optimized illumination correction and tree cover estimation from optical RapidEye satellite images. The findings presented in this study suggest that the change in tree cover is contingent on the respective digital elevation models used for pre-processing the data. Imagery corrected with the freely available SRTM30 DEM with 30 m resolution leads to a higher accuracy in the estimation of tree cover based on the high-resolution and cost intensive Pleaides DEM. These manuscripts eventually seek to resolve some of the issues and provide answers to some of the detailed questions that still persist at different steps of the forest monitoring process. In future, these new and innovate methods and technologies will maybe integrate into forest monitoring programs

Forage supply of West African rangelands : Towards a better understanding of ecosystem services by application of hyperspectral remote sensing

Grazing is the predominant type of land use in savanna regions all over the world. Although large savanna areas in Africa are still grazed by wild herbivores, the West African Sudanian savanna region mainly comprises rangeland ecosystems, providing the important ecosystem service of forage supply for domestic livestock. However, these dryland rangelands are threatened by global change, including a predicted in-crease in climatic aridity and variability as well as land degradation caused by overgrazing. In this context, the international research project WASCAL (West African Science Service Centre on Climate Change and Adapted Land Use) was initiated to investigate the effects of climatic change in this region and to develop effective adaptation and mitigation measures. This cumulative dissertation aims at providing a methodology for a regular knowledge-driven monitoring of forage resources in West Africa. Due to the vast and remote nature of Sudanian savannas, remote sensing technologies are required to achieve this goal. Hence, as a first step, it was necessary to test whether hyperspectral near-surface remote sensing offers the means to model and estimate the two most important aspects of forage supply, i.e. forage quantity (green biomass) and quality (metabolisable energy) (Chapter 2.1). Evidence was provided that partial least squares regression was able to generate robust and transferable forage models. In a second step, direct and indirect drivers of forage supply on the plot and site level were identified by using path modelling within the well-defined concept of social-ecological systems (Chapter 2.2). Results indicate that the provisioning ecosystem service of forage supply is mainly driven by land use, while climatic aridity exerts foremost indirect control by determining the way people use their environment. Building on these findings, upscaling of models was tested to generate maps of forage quality and quantity from satellite images (Chapter 2.3). Here, two different available data sources, i.e. multi- and hyperspectral satellites, were compared to serve the overall objective to install a regular forage monitoring system. In conclusion, preliminary forage maps could be created from both systems. An independent validation would be a research desiderate for future studies. Moreover, both systems feature certain shortcomings that might only be overcome by future satellite missions

Remote Sensing of Plant Biodiversity

This Open Access volume aims to methodologically improve our understanding of biodiversity by linking disciplines that incorporate remote sensing, and uniting data and perspectives in the fields of biology, landscape ecology, and geography. The book provides a framework for how biodiversity can be detected and evaluated—focusing particularly on plants—using proximal and remotely sensed hyperspectral data and other tools such as LiDAR. The volume, whose chapters bring together a large cross-section of the biodiversity community engaged in these methods, attempts to establish a common language across disciplines for understanding and implementing remote sensing of biodiversity across scales. The first part of the book offers a potential basis for remote detection of biodiversity. An overview of the nature of biodiversity is described, along with ways for determining traits of plant biodiversity through spectral analyses across spatial scales and linking spectral data to the tree of life. The second part details what can be detected spectrally and remotely. Specific instrumentation and technologies are described, as well as the technical challenges of detection and data synthesis, collection and processing. The third part discusses spatial resolution and integration across scales and ends with a vision for developing a global biodiversity monitoring system. Topics include spectral and functional variation across habitats and biomes, biodiversity variables for global scale assessment, and the prospects and pitfalls in remote sensing of biodiversity at the global scale

Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors

The ability to accurately and rapidly acquire leaf area index (LAI) is an indispensable component of process-based ecological research facilitating the understanding of gas-vegetation exchange phenomenon at an array of spatial scales from the leaf to the landscape. However, LAI is difficult to directly acquire for large spatial extents due to its time consuming and work intensive nature. Such efforts have been significantly improved by the emergence of optical and active remote sensing techniques. This paper reviews the definitions and theories of LAI measurement with respect to direct and indirect methods. Then, the methodologies for LAI retrieval with regard to the characteristics of a range of remotely sensed datasets are discussed. Remote sensing indirect methods are subdivided into two categories of passive and active remote sensing, which are further categorized as terrestrial, aerial and satellite-born platforms. Due to a wide variety in spatial resolution of remotely sensed data and the requirements of ecological modeling, the scaling issue of LAI is discussed and special consideration is given to extrapolation of measurement to landscape and regional levels

Remote Sensing of Plant Biodiversity

At last, here it is. For some time now, the world has needed a text providing both a new theoretical foundation and practical guidance on how to approach the challenge of biodiversity decline in the Anthropocene. This is a global challenge demanding global approaches to understand its scope and implications. Until recently, we have simply lacked the tools to do so. We are now entering an era in which we can realistically begin to understand and monitor the multidimensional phenomenon of biodiversity at a planetary scale. This era builds upon three centuries of scientific research on biodiversity at site to landscape levels, augmented over the past two decades by airborne research platforms carrying spectrometers, lidars, and radars for larger-scale observations. Emerging international networks of fine-grain in-situ biodiversity observations complemented by space-based sensors offering coarser-grain imagery—but global coverage—of ecosystem composition, function, and structure together provide the information necessary to monitor and track change in biodiversity globally. This book is a road map on how to observe and interpret terrestrial biodiversity across scales through plants—primary producers and the foundation of the trophic pyramid. It honors the fact that biodiversity exists across different dimensions, including both phylogenetic and functional. Then, it relates these aspects of biodiversity to another dimension, the spectral diversity captured by remote sensing instruments operating at scales from leaf to canopy to biome. The biodiversity community has needed a Rosetta Stone to translate between the language of satellite remote sensing and its resulting spectral diversity and the languages of those exploring the phylogenetic diversity and functional trait diversity of life on Earth. By assembling the vital translation, this volume has globalized our ability to track biodiversity state and change. Thus, a global problem meets a key component of the global solution. The editors have cleverly built the book in three parts. Part 1 addresses the theory behind the remote sensing of terrestrial plant biodiversity: why spectral diversity relates to plant functional traits and phylogenetic diversity. Starting with first principles, it connects plant biochemistry, physiology, and macroecology to remotely sensed spectra and explores the processes behind the patterns we observe. Examples from the field demonstrate the rising synthesis of multiple disciplines to create a new cross-spatial and spectral science of biodiversity. Part 2 discusses how to implement this evolving science. It focuses on the plethora of novel in-situ, airborne, and spaceborne Earth observation tools currently and soon to be available while also incorporating the ways of actually making biodiversity measurements with these tools. It includes instructions for organizing and conducting a field campaign. Throughout, there is a focus on the burgeoning field of imaging spectroscopy, which is revolutionizing our ability to characterize life remotely. Part 3 takes on an overarching issue for any effort to globalize biodiversity observations, the issue of scale. It addresses scale from two perspectives. The first is that of combining observations across varying spatial, temporal, and spectral resolutions for better understanding—that is, what scales and how. This is an area of ongoing research driven by a confluence of innovations in observation systems and rising computational capacity. The second is the organizational side of the scaling challenge. It explores existing frameworks for integrating multi-scale observations within global networks. The focus here is on what practical steps can be taken to organize multi-scale data and what is already happening in this regard. These frameworks include essential biodiversity variables and the Group on Earth Observations Biodiversity Observation Network (GEO BON). This book constitutes an end-to-end guide uniting the latest in research and techniques to cover the theory and practice of the remote sensing of plant biodiversity. In putting it together, the editors and their coauthors, all preeminent in their fields, have done a great service for those seeking to understand and conserve life on Earth—just when we need it most. For if the world is ever to construct a coordinated response to the planetwide crisis of biodiversity loss, it must first assemble adequate—and global—measures of what we are losing