Integrating technologies for scalable ecology and conservation

Marvin, David C.; Koh, Lian Pin; Lynam, Antony J.; Wich, Serge A.; Davies, Andrew B.; Krishnamurthy, R.; Stokes, Emma J.; Starkey, Ruth; Asner, Gregory P.

doi:10.1016/j.gecco.2016.07.002

Cited by 130 publications

(104 citation statements)

References 63 publications

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“…RIL has scientific and engineering principles that involve worker education and training (Medjibe et al 2011). The GPS technology and remote sensing are used to control and evaluate field work or mitigate forest damages (Marvin et al 2016). Due to the high operational complexity and costs of selective logging, mathematical programming models are effetive aids in planning and executing logging routines (Braz et al 2004;Gomide et al 2010, Fernandes et al 2013, but have yet to be implemented as widespread optimization tools in selective logging enterprises in the Brazilian Amazon.…”

Section: Introductionmentioning

confidence: 99%

Optimal selective logging regime and log landing location models: a case study in the Amazon forest

et al. 2018

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Reduced-impact logging is a well known practice applied in most sustainable forest management plans in the Amazon. Nevertheless, there are still ways to improve the operational planning process. Therefore, the aim of this study was to create an integer linear programming (ILP) to fill in the knowledge gaps in the decision support system of reduced impact logging explorations. The minimization of harvest tree distance to wood log landing was assessed. Forest structure aspects, income and wood production were set in the model, as well as the adjacency constraints. Data are from a dense ombrophylous forest in the western Brazilian Amazon. We applied the phytosociological analysis and BDq method to define the selective logging criteria. Then, ILP models were formulated to allow the application of the constraints. Finally, 32 scenarios (unbalanced forest, UF, and balanced forest, BF) were generated and compared with real executed plans (RE). Robust results were achieved and the expected finding of each scenario was met. The feasibility to integrate ILP models in uneven-aged forest management projects was endorsed. Consequently, the UF and BF scenarios tested were efficient and concise, introducing new advances for forest management plans in the Amazon. The proposed models have a high potential to improve the selective logging activities in the Amazon forest.KEYWORDS: forest planning, reduced-impact logging, integer linear programming, sustainable forest management, Liocourt quocient Regime de corte seletivo ótimo em modelos de locação de pátios de estocagem de madeira: um estudo de caso na floresta amazônica RESUMO A exploração de impacto reduzido é uma prática bem conhecida e aplicada na maioria dos planos de manejo florestal sustentável na Amazônia. Todavia, ainda há a possibilidade de melhoria do processo de planejamento da operação. Diante da falta de métodos auxiliares à tomada de decisão, o objetivo do trabalho foi desenvolver modelos de programação linear inteira (PLI) para suprir esta demanda. Considerou-se a minimização da distância entre as árvores exploradas e os pátios de estocagem de madeira. Aspectos estruturais, econômicos e produtivos foram incorporados ao modelo, bem como restrições de adjacência. Os dados derivam de uma floresta ombrófila densa na Amazônia Ocidental. Previamente, utilizou-se uma análise fitossociológica e o método BDq como critério de remoção. Posteriormente, modelos de PLI foram formulados para exemplificar as aplicações. Finalmente, 32 cenários (para floresta desbalaceada, UF, e floresta balanceada, BF) foram gerados e comparados com o plano executado no campo (RE). Resultados robustos foram obtidos e atenderam às expectativas de cada cenário. A viabilidade da integração dos modelos de PLI em projetos de manejo de florestas ineqüiâneas foi testado. Os cenários UF e BF testados foram eficientes e concisos, confirmando seu potencial para aumentar a eficiência de planos de exploração madeireira e manejo florestal na Amazônia.

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Section: Introductionmentioning

confidence: 99%

Optimal selective logging regime and log landing location models: a case study in the Amazon forest

et al. 2018

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“…Here, we used a spectral index based on the red edge spectral bands of RapidEye imagery, known to relate to vegetation structure and therefore biomass in the Cerrado (Gomes and Maillard 2015). Likewise, data collected by unmanned aerial vehicles (or drones)-a technology with an exponential popularity-could be used for the bridging the gap between ground-based and satellite data (He et al 2015, Marvin et al 2016). This, however, falls outside the scope of this study and would raise issues related to data availability constraints.…”

Section: Discussionmentioning

confidence: 99%

“…Also, when the sample units are smaller than the image pixels, the proportions of pixels not fully covered by the field data need to be extrapolated. The integration of field and (meso-scale) remote sensing data can be done with the aid of high-resolution remote sensing data, either coming from high-resolution satellite sensors or coming from (manned and unmanned) airborne sensors (Marvin et al 2016). Such analyses include, for example, modeling the aboveground carbon of a particular region by fitting the pixel-allocated sample data to the respective remote sensing data in a regression approach (Zandler et al 2015).…”

Section: Introductionmentioning

confidence: 99%

From sample to pixel: multi‐scale remote sensing data for upscaling aboveground carbon data in heterogeneous landscapes

et al. 2018

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In times of rapid global change, ecosystem monitoring is of utmost importance. Combined field and remote sensing data enable large-scale ecosystem assessments, while maintaining local relevance and accuracy. In heterogeneous landscapes, however, the integration of field-collected data with remote sensing image pixels is not a trivial matter. Indeed, much of the uncertainty in models that use remote sensing to map larger areas lies on the field data integration. In this study, we propose to use fine spatial resolution (5 9 5 m 2 ) remote sensing data as auxiliary data for upscaling field-sampled aboveground carbon data to target (meso-scale, i.e., 30 9 30 m 2 ) image pixels. In this process, we assess the effects of field data disaggregation and extrapolation, with and without the auxiliary data. We test this on three study sites in heterogeneous landscapes of the Brazilian savanna. We thus compare two methods that use auxiliary data-surface method, which uses a weighting layer, and regression method, which applies a regression model-with one method without auxiliary data-cartographic method. To evaluate our results, we compared observed vs. estimated aboveground carbon values (for known samples) at the pixel level. Additionally, we fitted a random forest regression model with the assigned carbon estimates and the target satellite imagery and assessed the influence of the fraction of extrapolated vs. sampled carbon values on model performance. We observed that, in heterogeneous landscapes, the use of fine spatial resolution remote sensing data improves the upscaling of field-based aboveground carbon data to coarser image pixels. We also show that a surface method is more suitable for spatial disaggregation, while a regression approach is preferable for extrapolating non-sampled pixel fractions. In our study, larger datasets, which included a higher proportion of estimated values, generally delivered better models of aboveground carbon than smaller datasets that are assumed to more reliably reflect reality. Our approach enables to link field and remote sensing data, which in turn enables the detailed mapping of aboveground carbon in heterogeneous landscapes over large areas through the optimized integration of field data and multi-scale remote sensing data.

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“…; Marvin et al . ). However, many technological advances fail to deliver their potential conservation outcomes (McGowan et al .…”

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confidence: 97%

Identifying technology solutions to bring conservation into the innovation era

Iacona

Ramachandra

McGowan

et al. 2019

Frontiers in Ecol & Environ

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Innovation has the potential to enable conservation science and practice to keep pace with the escalating threats to global biodiversity, but this potential will only be realized if such innovations are designed and developed to fulfill specific needs and solve well‐defined conservation problems. We propose that business‐world strategies for assessing the practicality of innovation can be applied to assess the viability of innovations, such as new technology, for addressing biodiversity conservation challenges. Here, we outline a five‐step, “lean start‐up” based approach for considering conservation innovation from a business‐planning perspective. Then, using three prominent conservation initiatives – Marxan (software), Conservation Drones (technology support), and Mataki (wildlife‐tracking devices) – as case studies, we show how considering proposed initiatives from the perspective of a conceptual business model can support innovative technologies in achieving desired conservation outcomes.

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Integrating technologies for scalable ecology and conservation

Cited by 130 publications

References 63 publications

Optimal selective logging regime and log landing location models: a case study in the Amazon forest

Optimal selective logging regime and log landing location models: a case study in the Amazon forest

From sample to pixel: multi‐scale remote sensing data for upscaling aboveground carbon data in heterogeneous landscapes

Identifying technology solutions to bring conservation into the innovation era

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