Abstract:Elevation mapping at ground level is challenging in forested areas like the Amazon region, which is mostly covered by dense rainforest. The most common techniques, i.e. photogrammetry and short wavelength radar, provide elevations at canopy level at best, while most applications require ground elevations. Even lidar and P-band radar, which can penetrate foliage and measure elevations at ground level, have some limitations which are analyzed in here. We address three research questions: To what extent can a ter… Show more
“…As in the case of other disturbances, abiotic factors such as slope, elevation, distance to roads, or weather patterns are important for incorporating complexity at small spatial and temporal scales [73]. However, getting good quality for such small-scale variables could be a challenge in areas with dense forest cover and sparse road networks, as is the case in most tropical or boreal forests [74].…”
Section: Modelling Forests Beyond Treesmentioning
confidence: 99%
“…This approach aims to bring nonacademic forest stakeholders into the process at the beginning, so they develop a sense of ownership of the research outcome and therefore are much more likely to implement the model outcomes. Three models for sciencepolicy interaction have identified [74]: the "linear phase" when science informed policy-making in a unidirectional manner, the "interactive phase" when both sides found themselves in a continuous interaction, and the "embedded phase." Our own experience is that the linear phase is still dominant in many regions, with scientists developing models and scenarios of their interest and then approaching nonacademic stakeholders with their results.…”
Purpose of Review
Forest models are becoming essential tools in forest research, management, and policymaking but currently are under deep transformation. In this review of the most recent literature (2018–2022), we aim to provide an updated general view of the main topics currently attracting the efforts of forest modelers, the trends already in place, and some of the current and future challenges that the field will face.
Recent Findings
Four major topics attracting most of on current modelling efforts: data acquisition, productivity estimation, ecological pattern predictions, and forest management related to ecosystem services. Although the topics may seem different, they all are converging towards integrated modelling approaches by the pressure of climate change as the major coalescent force, pushing current research efforts into integrated mechanistic, cross-scale simulations of forest functioning and structure.
Summary
We conclude that forest modelling is experiencing an exciting but challenging time, due to the combination of new methods to easily acquire massive amounts of data, new techniques to statistically process such data, and refinements in mechanistic modelling that are incorporating higher levels of ecological complexity and breaking traditional barriers in spatial and temporal scales. However, new available data and techniques are also creating new challenges. In any case, forest modelling is increasingly acknowledged as a community and interdisciplinary effort. As such, ways to deliver simplified versions or easy entry points to models should be encouraged to integrate non-modelers stakeholders into the modelling process since its inception. This should be considered particularly as academic forest modelers may be increasing the ecological and mathematical complexity of forest models.
“…As in the case of other disturbances, abiotic factors such as slope, elevation, distance to roads, or weather patterns are important for incorporating complexity at small spatial and temporal scales [73]. However, getting good quality for such small-scale variables could be a challenge in areas with dense forest cover and sparse road networks, as is the case in most tropical or boreal forests [74].…”
Section: Modelling Forests Beyond Treesmentioning
confidence: 99%
“…This approach aims to bring nonacademic forest stakeholders into the process at the beginning, so they develop a sense of ownership of the research outcome and therefore are much more likely to implement the model outcomes. Three models for sciencepolicy interaction have identified [74]: the "linear phase" when science informed policy-making in a unidirectional manner, the "interactive phase" when both sides found themselves in a continuous interaction, and the "embedded phase." Our own experience is that the linear phase is still dominant in many regions, with scientists developing models and scenarios of their interest and then approaching nonacademic stakeholders with their results.…”
Purpose of Review
Forest models are becoming essential tools in forest research, management, and policymaking but currently are under deep transformation. In this review of the most recent literature (2018–2022), we aim to provide an updated general view of the main topics currently attracting the efforts of forest modelers, the trends already in place, and some of the current and future challenges that the field will face.
Recent Findings
Four major topics attracting most of on current modelling efforts: data acquisition, productivity estimation, ecological pattern predictions, and forest management related to ecosystem services. Although the topics may seem different, they all are converging towards integrated modelling approaches by the pressure of climate change as the major coalescent force, pushing current research efforts into integrated mechanistic, cross-scale simulations of forest functioning and structure.
Summary
We conclude that forest modelling is experiencing an exciting but challenging time, due to the combination of new methods to easily acquire massive amounts of data, new techniques to statistically process such data, and refinements in mechanistic modelling that are incorporating higher levels of ecological complexity and breaking traditional barriers in spatial and temporal scales. However, new available data and techniques are also creating new challenges. In any case, forest modelling is increasingly acknowledged as a community and interdisciplinary effort. As such, ways to deliver simplified versions or easy entry points to models should be encouraged to integrate non-modelers stakeholders into the modelling process since its inception. This should be considered particularly as academic forest modelers may be increasing the ecological and mathematical complexity of forest models.
“…In the case of tropical forest regions, such as the Amazon, these requirements are more difficult to meet. Indeed, in addition to difficult field access and to the cloud cover, the presence of forests, with an average height of about 30 m and a very irregular texture, makes it impossible to observe the ground with optical techniques (such as photogrammetry) and even with short wavelength radar interferometry (Polidori et al 2022). Therefore, those methods provide a model of the canopy elevation, which turns very uncertain the description of hydrography due to important elevation and slope errors, especially in areas with moderate relief.…”
Abstract. Relief mapping through dense tropical forest is a challenge, which can be met by processing P-band radar images. Digital terrain models (DTMs) obtained over three sites in the Amazon region (French Guiana and North Brazil) are evaluated according to two types of quality criteria: on the one hand, the accuracy of elevations and slopes, calculated by comparison with lidar surveys used as reference data; on the other hand, the hydrographic coherence of the DTM, revealed by its compliance to some universal rules like “all rivers flow downhill”, or the fact that landforms shaped by water have a fractal behaviour. The results depend on the scale, the effect of which is addressed. Overall, the results confirm the potential of P-band airborne radar for 3D characterisation of hydrography in tropical forested areas.
“…Os sensores mais utilizados para a extração de Modelos Digitais de Elevação (MDE) usam um comprimento de onda curto, são sensíveis ao dossel e não ao terreno, semelhantes nesse sentido à Fotogrametria. Segundo Polidori et al (2022), o grande problema desses dados é que a maioria dos MDT são, na verdade, MDS (Modelo Digital de Superfície, que se trata da representação dos dosséis em aglomerados de árvores) e, na maioria das aplicações desses modelos, os autores os utilizam como se fossem MDT.…”
O Modelo Digital de Terreno (MDT) é um importante produto usado em geociências, mas sua extração em áreas florestais densas ainda é um desafio. Uma das grandes dificuldades na extração de um MDT via sensores remotos é em áreas florestais densas, pois nestas áreas as informações geradas ficam degradadas, já que as técnicas mais utilizadas só conseguem medir a elevação do dossel e não do terreno, limitando a cartografia em áreas como a Amazônia. A técnica mais adequada para extração de um MDT é o LiDAR (Light Detection And Ranging) aerotransportado, porém há limitações de prazo e custo para grandes áreas. As ondas de radar de Banda P têm uma grande capacidade de penetração através da vegetação densa, tornando-a uma ferramenta promissora para modelagem digital do terreno em áreas florestais. Essa técnica foi usada para a última atualização da cartografia do estado do Amapá (Brasil). Este artigo tem como objetivo avaliar a qualidade geral de um MDT produzido usando interferometria radar de Banda P aerotransportado em relação a dados LiDAR (usados como referência) em 4 regiões do estado do Amapá, utilizando vários critérios. Além da comparação visual que confirma a semelhança geral dos dois produtos, foi avaliada a diferença em termos de elevação e declividade, com um erro médio em sua elevação de -0,52 m, enquanto possui um desvio padrão e RMSE médio abaixo de 3 metros, com uma diferença na declividade de aproximadamente -4° e um desvio padrão e RMSE médio de 4,74° e 6,14°, respectivamente. Este estudo fornece uma estimativa da precisão do MDT radar de Banda P, tanto em termos de elevação quanto de declividade, que são variáveis essenciais para caracterizar as formas do relevo.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.