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

Making (remote) sense of lianas

Lianas (woody vines) are abundant and diverse, particularly in tropical ecosystems. Lianas use trees for structural support to reach the forest canopy, often putting leaves above their host tree. Thus they are major parts of many forest canopies. Yet, relatively little is known about distributions of lianas in tropical forest canopies, because studying those canopies is challenging. This knowledge gap is urgent to address because lianas compete strongly with trees, reduce forest carbon uptake and are thought to be increasing, at least in the Neotropics. Lianas can be difficult to study using traditional field methods. Their pliable stems often twist and loop through the understorey, making it difficult to assess their structure and biomass, and the sizes and locations of their crowns. Furthermore, liana stems are commonly omitted from standard field surveys. Remote sensing of lianas can help overcome some of these obstacles and can provide critical insights into liana ecology, but to date there has been no systematic assessment of that contribution. We review progress in studying liana ecology using ground-based, airborne and space-borne remote sensing in four key areas: (i) spatial and temporal distributions, (ii) structure and biomass, (iii) responses to environmental conditions and (iv) diversity. This demonstrates the great potential of remote sensing for rapid advances in our knowledge and understanding of liana ecology. We then look ahead, to the possibilities offered by new and future advances. We specifically consider the data requirements, the role of technological advances and the types of methods and experimental designs that should be prioritised. Synthesis. The particular characteristics of the liana growth form make lianas difficult to study by ground-based field methods. However, remote sensing is well suited to collecting data on lianas. Our review shows that remote sensing is an emerging tool for the study of lianas, and will continue to improve with recent developments in sensor and platform technology. It is surprising, therefore, how little liana ecology research has utilised remote sensing to date—this should rapidly change if urgent knowledge gaps are to be addressed. In short, liana ecology needs remote sensing

Liana effects on tree functional groups and carbon balance in a lowland tropical forest, Panama and an analysis of progress in remote sensing of lianas

1. Lianas (or vines) are woody climbers that root in the ground and utilise the structure of neighbouring trees to ascend into the forest canopy. These plant forms are found in forests globally, although they are particularly prevalent seasonal and lowland forests of the neotropics where they make up a significant proportion of woody species and stems. Lianas infest host trees and can dominate forest canopies, often described as structural parasites, lianas compete intensely with trees for sunlight and belowground resources without investing in their own supporting structure. 2. Research interest in lianas has increased substantially in recent decades, though they remain understudied compared to other plant forms. Chapter 1 reviews the increasingly comprehensive and geographically broad evidence base documenting the competitive effect of lianas upon host trees. Liana infestation has a significant and detrimental impact on tree growth, mortality, reproduction, and regeneration. Ultimately this reduces the capacity of trees to sequester and store carbon, a globally important ecosystem service provided by tropical forests. Furthermore, some evidence suggests that lianas are disproportionately affecting shade-tolerant carbon dense tree genera, contributing to a decline in these species. Reports of increasing liana abundance in the neotropics adds to the urgency for further research into the extent, magnitude, and mechanisms by which lianas effect the carbon balance in tropical forests. 3. Studying tropical forest canopies is challenging, as such relatively little is known about the distribution of lianas in forest canopies. Remote sensing is an emerging research method that has overcome some of the shortfalls associated with standard field surveys and can provide new and critical insights into liana ecology. Chapter 2 provides the first systematic assessment of the progress in the use of remote sensing to further understand the (i) spatial and temporal distributions, (ii) structure and biomass, (iii) responses to environmental conditions, and (iv) diversity, of lianas. The possibilities offered by new and future advances in remote sensing technology to study lianas, and the further data requirements needed, are then considered. For unanswered research questions to be resolved, liana ecology needs remote sensing. 4. Chapter 3 is the first study to report findings from a large-scale liana removal experiment for which there is more than 3-4 years of data. I consider eight census years of data from an ongoing liana-removal experiment in Gigante, Panama to address the paucity of research into how lianas impact carbon accumulation in trees with differing life histories and functional traits. Tree biomass growth was 49.21% lower in liana-infested plots over the eight-year period. This is attributed to a growth release in low wood density pioneer species in the four years after removal, which shifts towards high wood density shade tolerant species dominating growth contributions to overall forest carbon in the latter four census years. My findings also support the notion that severity of crown infestation dictates the magnitude of liana effect on tree biomass growth. This study highlights the need to quantify the contribution of species functional groups to forest carbon balance in order to better understand potential future liana effects. With reports of increasing liana abundance, my findings present a worrying picture of the resilience of tropical forests to persist as a functioning global carbon sink. 5. Synthesis. Our understanding of liana ecology and the relationship between these plant forms and carbon balance in tropical forests has increased rapidly. Observational studies, removal experiments, repeated plot censuses and the rapidly evolving applications of remote sensing have provided new insights into the nature of lianas and their interactions with the tropical forests they infest. This study adds to these foundations for future liana research by presenting a thorough review of existing literature, a critical analysis of the integration of remote sensing and an assessment of disproportionate lianas effects on tree functional groups. With the current state of tropical forest decline, urgent knowledge gaps must be addressed now more than ever

UAV-Based forest health monitoring : a systematic review

CITATION: Ecke, S. et al. 2022. UAV-Based forest health monitoring : a systematic review. Remote Sensing, 14(13):3205, doi:10.3390/rs14133205.The original publication is available at https://www.mdpi.comIn recent years, technological advances have led to the increasing use of unmanned aerial vehicles (UAVs) for forestry applications. One emerging field for drone application is forest health monitoring (FHM). Common approaches for FHM involve small-scale resource-extensive fieldwork combined with traditional remote sensing platforms. However, the highly dynamic nature of forests requires timely and repetitive data acquisition, often at very high spatial resolution, where conventional remote sensing techniques reach the limits of feasibility. UAVs have shown that they can meet the demands of flexible operation and high spatial resolution. This is also reflected in a rapidly growing number of publications using drones to study forest health. Only a few reviews exist which do not cover the whole research history of UAV-based FHM. Since a comprehensive review is becoming critical to identify research gaps, trends, and drawbacks, we offer a systematic analysis of 99 papers covering the last ten years of research related to UAV-based monitoring of forests threatened by biotic and abiotic stressors. Advances in drone technology are being rapidly adopted and put into practice, further improving the economical use of UAVs. Despite the many advantages of UAVs, such as their flexibility, relatively low costs, and the possibility to fly below cloud cover, we also identified some shortcomings: (1) multitemporal and long-term monitoring of forests is clearly underrepresented; (2) the rare use of hyperspectral and LiDAR sensors must drastically increase; (3) complementary data from other RS sources are not sufficiently being exploited; (4) a lack of standardized workflows poses a problem to ensure data uniformity; (5) complex machine learning algorithms and workflows obscure interpretability and hinders widespread adoption; (6) the data pipeline from acquisition to final analysis often relies on commercial software at the expense of open-source tools.https://www.mdpi.com/2072-4292/14/13/3205Publisher's versio

Bayesian Network Modeling and Inference in Plant Gene Networks And Analysis of Sequencing and Imaging Data

Scientific and technological advancements over the years have made curing, preventing or managing all diseases, a goal that seems to be within reach. The approach to manipulating biological systems is multifaceted. This dissertation focuses on two problems that pose fundamental challenges in developing methods to control biological systems: the first is to model complex interactions in biological systems; the second is faithful representation and analysis of biological data obtained from scientific equipments. The first part of this dissertation is a discussion on modeling and inference in gene networks, and Bayesian inference. Then we describe the application of Bayesian network modeling to represent interactions among genes, and integrating gene expression data in order to identify potential points of intervention in the gene network. We conclude with a summary of evolving directions for modeling gene interactions. The second topic this dissertation focuses on is taming biological data to obtain actionable insights. We introduce the challenges in representation and analysis of high throughput sequencing data and proceeds to describe the analysis of imaging data in the dynamic environment of cancer cells. Then we discuss tackling the problem of analyzing high throughput RNA sequencing data in order to pinpoint genes that exhibit different behaviors under monitored experimental conditions. Then we address the interesting problem of deciphering and quantifying gene-level activity from epifluorescent imaging data

Insect pests in tropical forestry

The layout of this second edition follows that of the first, though the content has been substantially rewritten to reflect 10 years of research and development, as well as the emergence of new pest species. Chapter 1 presents an overview, from a somewhat entomological perspective, of tropical forestry in its many guises. Chapters 2, 3 and 4 then discuss the 'pure' biology and ecology of tropical insects and their co-evolved relationships with the trees and forests in which they live. Chapter 5 is necessarily the largest chapter in the book, looking in detail at a selection of major pest species from all over the tropical world. Chapters 6, 7, 8 and 9 then discuss the theory and practice of insect pest management, starting at the fundamental planning stage, before any seeds hit the soil. Nursery management and stand management were considered in Chapters 7 and 8. Chapter 9 covers the topics of forest health surveillance, quarantine and forest invasive species, topics which again have significance at all stages of forestry but for convenience are presented after nursery and forest management. This, in fact, we attempt to do in the final chapter, Chapter 10, which combines most of the previous nine chapters in examples illustrating the concept of integrated pest management. ©CABI Publishing CABI Publishin