Relevant bibliographies by topics / Exploratory landscape analysis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Exploratory landscape analysis'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Exploratory landscape analysis"

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Rosetia, Amanda, and Nor Zalina Harun. "An Exploratory Analysis of the Definition and Conceptualization of Cultural Landscape." Jurnal Kejuruteraan si6, no. 1 (October 31, 2023): 17–27. http://dx.doi.org/10.17576/jkukm-2023-si6(1)-02.

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Many have analyzed “cultural landscape” and its meaning. Some recall the basics of diverse fields, such as “a cultural landscape is a landscape that has been cultivated” or “culture that dwells in a landscape.” The need to decipher “cultural landscape” has drive n research on people’s cultural views of their immediate surroundings. This study suggests cultural landscape research in South East Asia against the backdrop of emerging approaches in cultural landscape research worldwide. Thus, this essay will examine cultural dynamics arguments and hypotheses. This exploratory review and inductive qualitative analysis examined “cultural landscape” studies from the early 19th century to the present. Google Scholar, Science Direct, and other professional education search portals were used to find and examine over fifty papers on local cultural landscapes. To understand cultural landscape studies’ interconnectedness, research results are synthesized and thematized. Therefore, the study provides a concise overview of eight (8) significant fields of study: geography, governance, environmental studies, etymology, tourism, agricultural studies, health studies and technology. Only tourism, ecology, geography, and politics study Indonesia’s cultural landscape. However, despite the abundance of creative and literary creation in Indonesia, little progress has been made in this area. Instead, new findings in domains as diverse as literature, semantics, and etymology are advancing our knowledge of the cultural environment and its effects.
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Lang, Ryan Dieter, and Andries Petrus Engelbrecht. "An Exploratory Landscape Analysis-Based Benchmark Suite." Algorithms 14, no. 3 (February 27, 2021): 78. http://dx.doi.org/10.3390/a14030078.

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The choice of which objective functions, or benchmark problems, should be used to test an optimization algorithm is a crucial part of the algorithm selection framework. Benchmark suites that are often used in the literature have been shown to exhibit poor coverage of the problem space. Exploratory landscape analysis can be used to quantify characteristics of objective functions. However, exploratory landscape analysis measures are based on samples of the objective function, and there is a lack of work on the appropriate choice of sample size needed to produce reliable measures. This study presents an approach to determine the minimum sample size needed to obtain robust exploratory landscape analysis measures. Based on reliable exploratory landscape analysis measures, a self-organizing feature map is used to cluster a comprehensive set of benchmark functions. From this, a benchmark suite that has better coverage of the single-objective, boundary-constrained problem space is proposed.
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Chang Chien, Yi-Min, Steve Carver, and Alexis Comber. "An Exploratory Analysis of Expert and Nonexpert-Based Land-Scape Aesthetics Evaluations: A Case Study from Wales." Land 10, no. 2 (February 13, 2021): 192. http://dx.doi.org/10.3390/land10020192.

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The debate over the conceptual constructs of landscape aesthetics, specifically whether landscape quality is inherently related to landscape physical characteristics or is subjectively “in the eye of the beholder,” has continued for years. Solutions accommodating both the biophysical and perceptual aspects of landscapes are thus desirable for landscape planners and policymakers. In response to policy shifts that emphasise both expert and public landscape perspectives, this study investigates the relationships between formal and informal landscape evaluations. It analyses crowdsourced data describing landscape aesthetic quality (Scenic-Or-Not) and authoritative landscape quality assessments (the Landscape Assessment Decision Making Process (LANDMAP) of Wales). Some agreement was found regarding landforms most likely to be perceived as scenic or unattractive by experts and non-experts, which aligns with previous landscape perception studies. However, contested landscape typologies are identified formal and informal landscape aesthetic evaluations are compared. Several limitations and implications for current formal landscape assessment paradigms (GIS based and vertical) are discussed and several approaches for capturing on-the-ground perceptions are suggested including recent extensions to GIS derived viewsheds (e.g., vertical voxel viewsheds).
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Staniak, Mateusz, and Przemysław Biecek. "The Landscape of R Packages for Automated Exploratory Data Analysis." R Journal 11, no. 2 (2019): 347. http://dx.doi.org/10.32614/rj-2019-033.

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Sarefo, Seth, Maurice Dawson, and Mphago Banyatsang. "An exploratory analysis of the cybersecurity threat landscape for Botswana." Procedia Computer Science 219 (2023): 1012–22. http://dx.doi.org/10.1016/j.procs.2023.01.379.

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Song, Shangzhou, Shaohua Wang, Huichun Ye, and Yong Guan. "Exploratory Analysis on the Spatial Distribution and Influencing Factors of Beitang Landscape in the Shangzhuang Basin." Land 11, no. 3 (March 12, 2022): 418. http://dx.doi.org/10.3390/land11030418.

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Beitang landscape is a production system and land use pattern that ancient people created to adapt to droughts and floods during a long traditional farming culture. It has a critical reference meaning for water resource use and water systems protection in modern cities. Taking the Shangzhuang Basin (China) as an example, this study used multi-source data, such as remote sensing images, Beitang vector dataset, land-use dataset, elevation, slope, river, road, and field survey, to investigate the spatial distribution and influencing factors Beitang landscape. Results showed that in a typical small watershed basin, an area of ponds accounted for 1.0%, about 12 ponds per square kilometer—the average area of ponds is 814 m2, of which the vast majority is less than 1000 m2. The study found that the spatial distribution of Beitang in the Shangzhuang Basin has cluster characteristics, influenced by elevation, slope, aspect, river, roads, villages, farmland, woodland, and other factors, all of which have closely related to the natural environment development and human activities. The upstream, middle, and downstream of three Beitang landscapes were coordinated to support the Beitang landscape system in the small watershed of the basin. Findings provided a model for protecting and utilizing natural water systems in rural areas during the construction of sponge cities.
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Munoz, Mario A., Michael Kirley, and Saman K. Halgamuge. "Exploratory Landscape Analysis of Continuous Space Optimization Problems Using Information Content." IEEE Transactions on Evolutionary Computation 19, no. 1 (February 2015): 74–87. http://dx.doi.org/10.1109/tevc.2014.2302006.

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Alyahya, Khulood, and Jonathan E. Rowe. "Landscape Analysis of a Class of NP-Hard Binary Packing Problems." Evolutionary Computation 27, no. 1 (March 2019): 47–73. http://dx.doi.org/10.1162/evco_a_00237.

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This article presents an exploratory landscape analysis of three NP-hard combinatorial optimisation problems: the number partitioning problem, the binary knapsack problem, and the quadratic binary knapsack problem. In the article, we examine empirically a number of fitness landscape properties of randomly generated instances of these problems. We believe that the studied properties give insight into the structure of the problem landscape and can be representative of the problem difficulty, in particular with respect to local search algorithms. Our work focuses on studying how these properties vary with different values of problem parameters. We also compare these properties across various landscapes that were induced by different penalty functions and different neighbourhood operators. Unlike existing studies of these problems, we study instances generated at random from various distributions. We found a general trend where some of the landscape features in all of the three problems were found to vary between the different distributions. We captured this variation by a single, easy to calculate parameter and we showed that it has a potentially useful application in guiding the choice of the neighbourhood operator of some local search heuristics.
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Judijanto, Loso, Eva Yuniarti Utami, Rianti Setyawasih, and Teddy Oswari. "Exploratory Analysis of Literature on the Impact of Globalization on Finance." West Science Interdisciplinary Studies 1, no. 12 (December 30, 2023): 1451–60. http://dx.doi.org/10.58812/wsis.v1i12.525.

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This exploratory analysis delves into the extensive body of literature examining the intricate relationship between globalization and finance in the contemporary era. As global economic forces continue to reshape financial landscapes worldwide, this study synthesizes insights from diverse disciplines, including economics, finance, sociology, and political science. The analysis uncovers the transformative power of globalization on financial structures, institutions, and market behaviors, revealing a nuanced interplay that extends beyond economic transactions. The literature review explores key themes such as the new dynamics of financial globalization, its effects on developing countries, and the role of technology, market structure, and policy responses. Additionally, it addresses the social and political consequences, emphasizing the need for a comprehensive understanding to inform regulatory frameworks and international cooperation. The method section details a systematic bibliometric analysis of Human Resource Management research in the context of globalization, providing key metrics, trends, and research opportunities. The results and discussion section identifies research trends, key articles, and potential terms for future exploration. Drawing implications from the analysis, the study advocates for adaptive strategies and a forward-thinking perspective to navigate the complexities of the interconnected global financial landscape.
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Caldeira, Sofia P. "The Pluralization of Feminist Hashtag Landscapes: An Exploratory Mapping of Feminist Hashtags on Portuguese Instagram." Social Media + Society 9, no. 2 (April 2023): 205630512311716. http://dx.doi.org/10.1177/20563051231171638.

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Feminist hashtags are often studied in the context of concise and highly visible hashtag movements. However, most social media and hashtag use exist outside the narrow confines of such movements. This article broadens the scope of exploration of hashtag feminisms, encompassing not only well-defined hashtag movements but also generic hashtags where feminist action might not be overtly or strategically mobilized. It grounds these explorations on the mapping of the Portuguese landscape of feminist hashtags on Instagram. Starting from four hashtags—#feminismoportugal, #igualdadedegénero, #naopartilhes, and #portugalmaisigual—this study explores hashtagging practices in 294 posts created by 101 users. Combining the analysis of a hashtag co-occurrences network with qualitative close readings, this article explores how these online practices incorporate varied typologies of fourth-wave feminist action, how a focus on intersectionality can be conducive to a heterogeneous and differentiated hashtag landscape, and how questions of effectiveness of hashtag activism can be problematized by low metrics of engagement. As such, this exploration of the Portuguese feminist hashtag landscape serves as an entry point for a critical reflection on the everyday uses of feminist hashtags, illustrating the tensions, complexities, and contradictions that such pluralized hashtag landscapes can encompass.
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Dissertations / Theses on the topic "Exploratory landscape analysis"

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Jankovic, Anja. "Towards Online Landscape-Aware Algorithm Selection in Numerical Black-Box Optimization." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS302.

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Les algorithmes d'optimisation de boîte noire (BBOA) sont conçus pour des scénarios où les formulations exactes de problèmes sont inexistantes, inaccessibles, ou trop complexes pour la résolution analytique. Les BBOA sont le seul moyen de trouver une bonne solution à un tel problème. En raison de leur applicabilité générale, les BBOA présentent des comportements différents lors de l'optimisation de différents types de problèmes. Cela donne un problème de méta-optimisation consistant à choisir l'algorithme le mieux adapté à un problème particulier, appelé problème de sélection d'algorithmes (AS). La vision d'automatiser cette sélection a vite gagné du terrain dans la communauté. Un moyen important de le faire est l'AS tenant compte du paysage, où le choix de l'algorithme est basé sur la prédiction de ses performances via des représentations numériques d'instances de problèmes appelées caractéristiques. Un défi clé auquel l'AS tenant compte du paysage est confrontée est le coût de calcul de l'extraction des caractéristiques, une étape qui précède l'optimisation. Dans cette thèse, nous proposons une approche d'AS tenant compte du paysage basée sur la trajectoire de recherche qui intègre cette étape d'extraction dans celle d'optimisation. Nous montrons que les caractéristiques calculées à l'aide de la trajectoire conduisent à des prédictions robustes et fiables des performances des algorithmes, et à de puissants modèles d'AS construits dessus. Nous présentons aussi plusieurs analyses préparatoires, y compris une perspective de combinaison de 2 stratégies de régression complémentaires qui surpasse des modèles classiques de régression simple et amplifie la qualité du sélecteur
Black-box optimization algorithms (BBOAs) are conceived for settings in which exact problem formulations are non-existent, inaccessible, or too complex for an analytical solution. BBOAs are essentially the only means of finding a good solution to such problems. Due to their general applicability, BBOAs can exhibit different behaviors when optimizing different types of problems. This yields a meta-optimization problem of choosing the best suited algorithm for a particular problem, called the algorithm selection (AS) problem. By reason of inherent human bias and limited expert knowledge, the vision of automating the selection process has quickly gained traction in the community. One prominent way of doing so is via so-called landscape-aware AS, where the choice of the algorithm is based on predicting its performance by means of numerical problem instance representations called features. A key challenge that landscape-aware AS faces is the computational overhead of extracting the features, a step typically designed to precede the actual optimization. In this thesis, we propose a novel trajectory-based landscape-aware AS approach which incorporates the feature extraction step within the optimization process. We show that the features computed using the search trajectory samples lead to robust and reliable predictions of algorithm performance, and to powerful algorithm selection models built atop. We also present several preparatory analyses, including a novel perspective of combining two complementary regression strategies that outperforms any of the classical, single regression models, to amplify the quality of the final selector
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McBride, Gemma. "An exploratory analysis of landscape-level effects on wild dog home ranges and core areas : a case study at Kosciuszko National Park, and Bago and Maragle State Forest." Master's thesis, 2007. http://hdl.handle.net/1885/132120.

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Wild dogs in Australia comprise both purebred dingoes and dingo/domestic dog hybrids. Wil d dogs are widespread across mainland Australia including the south eastern region and still maintain the role of top order predator and trophic regulator. Although they are protected in many National Parks as native species and important ecosystem regulators they are also considered to be pests in rural areas where they spread di sease and prey on small cattle and sheep. This dichotomy of perspectives can make management of the species difficult, however, with more knowledge of the behaviour and habitat requirements of the dingo perhaps more effective management strategies can be developed. The purpose of this thesis is to examine the hypothesis that selected landscapelevel variables play a significant role in determining the core and home ranges of wild dogs. The steps in researching this hypothesis are two-fold, (1) to estimate the home ranges and core areas of the wild dogs in the case s tudy, and (2) examine the l andscape features of the home ranges and core areas of movement to determine whether or not certain features in the landscape may effect or shape home ranges. The wild dog data were drawn from three previous studies to give a total of eighteen wild dogs. All of these wild dogs were located in either the Bago or Maragle State Forests near Tumbarumba, NSW or in the adjoining northwest comer of Kosciuszko National Park. The study found the wild dogs ' home ranges, usmg 100 % minimum convex polygons, to be significantly larger than expected compared with the findings of a number of previous studies of wild dog home ranges in a variety of locations around Australia. Kernel estimators, which were used to calculate the core movement areas were found to produce better estimates than minimum convex polygo ns, but were sensitive to small clusters of data points that could heavily influence both the size and location of core areas. This was highli ghted by th e compariso n between two data sets (AR 181 and DJ 220) of the one wild dog that produced very different results for what appeared to be essentially the same home range and core areas. The landscape features of vegetation, topography and water surfaces were also studied within the home ranges and core areas of each of the wild dogs and it was found that they tended to be in areas made up predominantly of Montane Tableland Forests and although, in general, terrain tended to be rugged with large relief, territory within the core areas were mainly flat or undulating plateaus. No s imple linear correlation was found between wild dog sites and Gross Primary Productivity. Similarly, no link was found with water surfaces and home and core ranges.
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Books on the topic "Exploratory landscape analysis"

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Jenset, Gard B., and Barbara McGillivray. A new methodology for quantitative historical linguistics. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198718178.003.0007.

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Chapter 7 summarizes the book and discusses how the proposed framework provides researchers with the methodological guidance to answer new questions, as well as answering existing questions in new ways. A section summarizes the core steps of the quantitative research process based on the principles and best practices of Chapter 2. The importance of open data and open research processes for transparent research is highlighted. An extended case study on morphological change in early modern English is used to exemplify a research process that includes exploratory data analysis and different types of multivariate statistical techniques. The case study highlights that quantitative studies still require interpretation, and that judgements must be made about the adequacy of the statistical models, an important point that is not always sufficiently emphasized in existing methodological introductions. The chapter ends with a summary locating the framework of the book in the historical linguistics landscape.
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Book chapters on the topic "Exploratory landscape analysis"

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Beham, Andreas, Erik Pitzer, Stefan Wagner, and Michael Affenzeller. "Integrating Exploratory Landscape Analysis into Metaheuristic Algorithms." In Computer Aided Systems Theory – EUROCAST 2017, 473–80. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74718-7_57.

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Mersmann, Olaf, Mike Preuss, and Heike Trautmann. "Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis." In Parallel Problem Solving from Nature, PPSN XI, 73–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15844-5_8.

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Kerschke, Pascal, Mike Preuss, Carlos Hernández, Oliver Schütze, Jian-Qiao Sun, Christian Grimme, Günter Rudolph, Bernd Bischl, and Heike Trautmann. "Cell Mapping Techniques for Exploratory Landscape Analysis." In Advances in Intelligent Systems and Computing, 115–31. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07494-8_9.

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Pienaar, Johannes J., Anna S. Boman, and Katherine M. Malan. "Hilbert Curves for Efficient Exploratory Landscape Analysis Neighbourhood Sampling." In Applications of Evolutionary Computation, 293–309. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56855-8_18.

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Renau, Quentin, Carola Doerr, Johann Dreo, and Benjamin Doerr. "Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy." In Parallel Problem Solving from Nature – PPSN XVI, 139–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58115-2_10.

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Schneider, Lennart, Lennart Schäpermeier, Raphael Patrick Prager, Bernd Bischl, Heike Trautmann, and Pascal Kerschke. "HPO $$\times $$ ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis." In Lecture Notes in Computer Science, 575–89. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14714-2_40.

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AbstractHyperparameter optimization (HPO) is a key component of machine learning models for achieving peak predictive performance. While numerous methods and algorithms for HPO have been proposed over the last years, little progress has been made in illuminating and examining the actual structure of these black-box optimization problems. Exploratory landscape analysis (ELA) subsumes a set of techniques that can be used to gain knowledge about properties of unknown optimization problems. In this paper, we evaluate the performance of five different black-box optimizers on 30 HPO problems, which consist of two-, three- and five-dimensional continuous search spaces of the XGBoost learner trained on 10 different data sets. This is contrasted with the performance of the same optimizers evaluated on 360 problem instances from the black-box optimization benchmark (BBOB). We then compute ELA features on the HPO and BBOB problems and examine similarities and differences. A cluster analysis of the HPO and BBOB problems in ELA feature space allows us to identify how the HPO problems compare to the BBOB problems on a structural meta-level. We identify a subset of BBOB problems that are close to the HPO problems in ELA feature space and show that optimizer performance is comparably similar on these two sets of benchmark problems. We highlight open challenges of ELA for HPO and discuss potential directions of future research and applications.
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Prager, Raphael Patrick, and Heike Trautmann. "Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features." In Applications of Evolutionary Computation, 411–25. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30229-9_27.

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Renau, Quentin, Johann Dreo, Carola Doerr, and Benjamin Doerr. "Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions." In Applications of Evolutionary Computation, 17–33. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72699-7_2.

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Färber, Michael, David Lamprecht, Johan Krause, Linn Aung, and Peter Haase. "SemOpenAlex: The Scientific Landscape in 26 Billion RDF Triples." In The Semantic Web – ISWC 2023, 94–112. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47243-5_6.

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AbstractWe present SemOpenAlex, an extensive RDF knowledge graph that contains over 26 billion triples about scientific publications and their associated entities, such as authors, institutions, journals, and concepts. SemOpenAlex is licensed under CC0, providing free and open access to the data. We offer the data through multiple channels, including RDF dump files, a SPARQL endpoint, and as a data source in the Linked Open Data cloud, complete with resolvable URIs and links to other data sources. Moreover, we provide embeddings for knowledge graph entities using high-performance computing. SemOpenAlex enables a broad range of use-case scenarios, such as exploratory semantic search via our website, large-scale scientific impact quantification, and other forms of scholarly big data analytics within and across scientific disciplines. Additionally, it enables academic recommender systems, such as recommending collaborators, publications, and venues, including explainability capabilities. Finally, SemOpenAlex can serve for RDF query optimization benchmarks, creating scholarly knowledge-guided language models, and as a hub for semantic scientific publishing. Data and Services: https://semopenalex.org https://w3id.org/SemOpenAlex Code: https://github.com/metaphacts/semopenalex/ Data License: Creative Commons Zero (CC0) Code License: MIT License
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Winter, Bodo. "Mapping the landscape of exploratory and confirmatory data analysis in linguistics." In Data Analytics in Cognitive Linguistics, 13–48. De Gruyter, 2022. http://dx.doi.org/10.1515/9783110687279-002.

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Conference papers on the topic "Exploratory landscape analysis"

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Mersmann, Olaf, Bernd Bischl, Heike Trautmann, Mike Preuss, Claus Weihs, and Günter Rudolph. "Exploratory landscape analysis." In the 13th annual conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2001576.2001690.

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Kerschke, Pascal, and Mike Preuss. "Exploratory landscape analysis." In GECCO '19: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3319619.3323389.

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Kerschke, Pascal, and Mike Preuss. "Exploratory landscape analysis." In GECCO '17: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3067695.3067696.

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Kerschke, Pascal, and Mike Preuss. "Exploratory Landscape Analysis." In GECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3583133.3595058.

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Pikalov, Maxim, and Aleksei Pismerov. "Exploratory Landscape Analysis Based Parameter Control." In GECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3583133.3596364.

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He, Yaodong, Shiu Yin Yuen, and Yang Lou. "Exploratory landscape analysis using algorithm based sampling." In GECCO '18: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3205651.3205660.

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Tanabe, Ryoji. "Towards exploratory landscape analysis for large-scale optimization." In GECCO '21: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3449639.3459300.

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Kerschke, Pascal, Mike Preuss, Simon Wessing, and Heike Trautmann. "Detecting Funnel Structures by Means of Exploratory Landscape Analysis." In GECCO '15: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2739480.2754642.

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Škvorc, Urban, Tome Eftimov, and Peter Korošec. "Using exploratory landscape analysis to visualize single-objective problems." In GECCO '20: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377929.3397488.

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Kerschke, Pascal, Mike Preuss, Simon Wessing, and Heike Trautmann. "Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models." In GECCO '16: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2908812.2908845.

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Reports on the topic "Exploratory landscape analysis"

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Wainaina, Priscilla, Eunice Gituku, and Peter Minang. An Exploratory Study of Cost-Benefit Analysis of Landscape Restoration. World Agroforestry Centre (ICRAF), 2020. http://dx.doi.org/10.5716/wp20014.pdf.

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