Nationwide native forest structure maps for Argentina based on forest inventory data, SAR Sentinel-1 and vegetation metrics from Sentinel-2 imagery

Silveira, Eduarda Martiniano de Oliveira; Radeloff, Volker C.; Martinuzzi, Sebastián; Pastur, Guillermo José Martínez; Bono, Julieta; Politi, Natalia; Lizarraga, Leonidas; Rivera, Luis; Ciuffoli, Lucía; Rosas, Yamina Micaela; Olah, Ashley M.; Gavier-Pizarro, Gregorio; Pidgeon, Anna M.

doi:10.1016/j.rse.2022.113391

Cited by 39 publications

(24 citation statements)

References 84 publications

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“…Forest structure variables were useful to predict the soil nutrient contents. The main variables were dominant height (as a proxy of site quality) and basal area (as a proxy of tree density) [30,38,44,104]. Both variables can be related to tree biomass, which results in the main organic source material for soil nutrients [40,41,100].…”

Section: Discussionmentioning

confidence: 99%

“…The climate in the study area is influenced by the oceans, Antarctica and the insularity that determine a uniform climate regime with a low range of annual temperature (7-10 • C) and rainfalls associated to the orography (500 to 700 mm yr −1 ) with abundant snowfall during the winter season [29,30,47]. The parent materials of the soils are metamorphic rocks modulated by glacial processes.…”

Section: Methodsmentioning

confidence: 99%

“…With these images, we defined altitude and aspect and slope; in addition, we used the soil organic carbon content (SOC, ton ha −1 ) developed for Patagonian forests [26]. Finally, we included forest landscape metrics derived from the normalized difference vegetation index (NDVI) [80], net primary productivity (NPP) [81] and forest structure variables (dominant height and BA) [30].…”

Section: Methodsmentioning

confidence: 99%

“…These methods were designed to overcome the limitations of the conventional soil mapping approach and to estimate soil properties based on relationships between soils and environmental variables obtained from terrain attributes (e.g., digital elevation models) and satellite imagery [7,17,27]. Recent advances in the mapping of forest structure and functionality for large areas combine field-based measurements with data from passive and active satellite sensors, including radar [28][29][30], at a much lower cost than traditional field inventories [31][32][33]. Many of the described methods are largely used in estimating SN and SP [4,5,7,10,[34][35][36].…”

Section: Introductionmentioning

confidence: 99%

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Nitrogenous and Phosphorus Soil Contents in Tierra del Fuego Forests: Relationships with Soil Organic Carbon, Climate, Vegetation and Landscape Metrics

Pastur

Acuña

Chaves

et al. 2023

Land

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Soil nitrogen (SN) and soil phosphorus (SP) contents support several ecosystem services and define the forest type distribution at local scale in Southern Patagonia. The quantification of nutrients during forest surveys requires soil samplings and estimations that are costly and difficult to measure. For this, predictive models of soil nutrients are needed. The objective of this study was to quantify SN and SP contents (30 cm depth) using different modelling approaches based on climatic, topographic and vegetation variables. We used data from 728 stands of different forest types for linear regression models to map SN and SP. The fitted models captured the variability of forest types well (R²-adj. 92–98% for SN and 70–87% for SP). The means were 9.3 ton ha−1 for SN and 124.3 kg ha−1 for SP. Overall, SN values were higher in the deciduous forests than those in the mixed evergreen, while SP was the highest in the Nothofagus pumilio forests. SN and SP are relevant metrics for many applications, connecting major issues, such as forest management and conservation. With these models, the quantification of SN and SP stocks across forests of different protection status (National Law 26,331/07) and national/provincial reserve networks is possible, contributing to the determination of nutrient contents at landscape level.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Nitrogenous and Phosphorus Soil Contents in Tierra del Fuego Forests: Relationships with Soil Organic Carbon, Climate, Vegetation and Landscape Metrics

Pastur

Acuña

Chaves

et al. 2023

Land

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“…While these data provide accurate estimates of the different tree components at stand level, modeling and mapping forest C in the landscape remains a challenge in Argentina. Fortunately, recent advances in mapping forest structure and functionality for large areas combine field-based measurements with data from passive and active satellite sensors including radar (e.g., [25][26][27]) provide measurements of forest attributes, plus the uncertainty in those measurements, over large areas at much lower cost than traditional field inventories [28][29][30]. In contrast to the advances in biomass and C stock estimations in the above-ground components of forests, soil components have largely been ignored.…”

Section: Introductionmentioning

confidence: 99%

Mapping Soil Organic Carbon Content in Patagonian Forests Based on Climate, Topography and Vegetation Metrics from Satellite Imagery

et al. 2022

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Soil organic carbon (SOC) content supports several ecosystem services. Quantifying SOC requires: (i) accurate C estimates of forest components, and (ii) soil estimates. However, SOC is difficult to measure, so predictive models are needed. Our objective was to model SOC stocks within 30 cm depth in Patagonian forests based on climatic, topographic and vegetation productivity measures from satellite images, including Dynamic Habitat Indices and Land Surface Temperature derived from Landsat-8. We used data from 1320 stands of different forest types in Patagonia, and random forest regression to map SOC. The model captured SOC variability well (R² = 0.60, RMSE = 22.1%), considering the huge latitudinal extension (36.4° to 55.1° SL) and the great diversity of forest types. Mean SOC was 134.4 ton C ha−1 ± 25.2, totaling 404.2 million ton C across Patagonia. Overall, SOC values were highest in valleys of the Andes mountains and in southern Tierra del Fuego, ranging from 53.5 to 277.8 ton C ha−1 for the whole Patagonia region. Soil organic carbon is a metric relevant to many applications, connecting major issues such as forest management, conservation, and livestock production, and having spatially explicit estimates of SOC enables managers to fulfil the international agreements that Argentina has joined.

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Assessment of Machine Learning Algorithms for Land Cover Classification in a Complex Mountainous Landscape

Amin,

Imtiaz,

Haroon

et al. 2024

J geovis spat anal

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Mapping land cover (LC) in mountainous regions, such as the Gilgit-Baltistan (GB) area of Pakistan, presents significant challenges due to complex terrain, limited data availability, and accessibility constraints. This study addresses these challenges by developing a robust, data-driven approach to classify LC using high-resolution Sentinel-2 (S-2) satellite imagery from 2019 within Google Earth Engine (GEE). The research evaluated the performance of various machine learning (ML) algorithms, including classification and regression tree (CART), maximum entropy (gmoMaxEnt), minimum distance (minDistance), support vector machine (SVM), and random forest (RF), without extensive hyperparameter tuning. Additionally, ten different scenarios based on various band combinations of S-2 data were used as input for running the ML models. The LC classification was performed using 2759 sample points, with 70% for training and 30% for validation. The results indicate that the RF algorithm outperformed all other classifiers under scenario S1 (using 10 bands), achieving an overall accuracy (OA) of 0.79 and a kappa coefficient of 0.76. The final RF-based LC mapping shows the following percentage distribution: barren land (46.7%), snow cover (22.9%), glacier (7.9%), grasses (7.2%), water (4.7%), wetland (2.9%), built-up (2.7%), agriculture (1.9%), and forest (1.2%). It is suggested that the best identified RF classifier within the GEE environment should be used for advanced multi-source data image classification with hyperparameter tuning to increase OA. Additionally, it is suggested to build the capacity of various stakeholders in GB for better monitoring of LC changes and resource management using geospatial big data.

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Nationwide native forest structure maps for Argentina based on forest inventory data, SAR Sentinel-1 and vegetation metrics from Sentinel-2 imagery

Cited by 39 publications

References 84 publications

Nitrogenous and Phosphorus Soil Contents in Tierra del Fuego Forests: Relationships with Soil Organic Carbon, Climate, Vegetation and Landscape Metrics

Nitrogenous and Phosphorus Soil Contents in Tierra del Fuego Forests: Relationships with Soil Organic Carbon, Climate, Vegetation and Landscape Metrics

Mapping Soil Organic Carbon Content in Patagonian Forests Based on Climate, Topography and Vegetation Metrics from Satellite Imagery

Assessment of Machine Learning Algorithms for Land Cover Classification in a Complex Mountainous Landscape

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