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Articoli di riviste sul tema "Multisource forest inventory"

Autore: Grafiati

Pubblicato: 31 agosto 2024

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1

Castilla, Guillermo, Ronald J. Hall, Rob Skakun, et al. "The Multisource Vegetation Inventory (MVI): A Satellite-Based Forest Inventory for the Northwest Territories Taiga Plains." Remote Sensing 14, no. 5 (2022): 1108. http://dx.doi.org/10.3390/rs14051108.

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Sustainable forest management requires information on the spatial distribution, composition, and structure of forests. However, jurisdictions with large tracts of noncommercial forest, such as the Northwest Territories (NWT) of Canada, often lack detailed forest information across their land base. The goal of the Multisource Vegetation Inventory (MVI) project was to create a large area forest inventory (FI) map that could support strategic forest management in the NWT using optical, radar, and light detection and ranging (LiDAR) satellite remote sensing anchored on limited field plots and airb

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2

Kandel, PN. "Monitoring above-ground forest biomass: A comparison of cost and accuracy between LiDAR assisted multisource programme and field-based forest resource assessment in Nepal." Banko Janakari 23, no. 1 (2013): 12–22. http://dx.doi.org/10.3126/banko.v23i1.9463.

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Analyzing forest monitoring costs and accuracy of forest carbon stock estimates are important criteria in the framework of Reducing Emission from Deforestation and Forest Degradation (REDD), because Monitoring, Reporting and Verification (MRV) system has been seen as an investment that aims to generate financial benefits to forest owners. Thus, comparisons of cost efficiency and accuracy were carried out between the LiDAR (Light Detection and Ranging) Assisted Multisource Programme (LAMP) and the field-based multisource Forest Resource Assessment (FRA) applied in the 23500 km2 Terai Arc Landsc

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3

Malambo, Lonesome, Sorin C. Popescu, Jim Rakestraw, Nian-Wei Ku, and Tunde A. Owoola. "Regional Stem Volume Mapping: A Feasibility Assessment of Scaling Tree-Level Estimates." Forests 14, no. 3 (2023): 506. http://dx.doi.org/10.3390/f14030506.

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Spatially detailed monitoring of forest resources is important for sustainable management but limited by a lack of field measurements. The increasing availability of multisource datasets offers the potential to characterize forest attributes at finer resolutions with regional coverage. This study aimed to assess the potential of mapping stem volume at a 30 m scale in eastern Texas using multisource datasets: airborne lidar, Landsat and LANDFIRE (Landscape Fire and Resource Management Planning Tools Project) datasets. Gradient-boosted trees regression models relating total volume, estimated fro

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4

Katila, M., J. Heikkinen, and E. Tomppo. "Calibration of small-area estimates for map errors in multisource forest inventory." Canadian Journal of Forest Research 30, no. 8 (2000): 1329–39. http://dx.doi.org/10.1139/x99-234.

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A multisource inventory method has been applied in the Finnish National Forest Inventory (NFI) since 1990.The method utilizes satellite images and digital map data,in addition to field measurements, and produces estimates of allfield parameters for computation units as well as thematic maps. Information from base mapsis employed in delineating forestry land from other land use classes.The map data are not necessarily up-to-date and often containsignificant errors. This paper introduces a statistical calibration method aimed atreducing the effect of map errors on multisource forest resourceesti

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5

Tuominen, Sakari, Stuart Fish, and Simo Poso. "Combining remote sensing, data from earlier inventories, and geostatistical interpolation in multisource forest inventory." Canadian Journal of Forest Research 33, no. 4 (2003): 624–34. http://dx.doi.org/10.1139/x02-199.

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Multisource forest inventory with two-phase sampling offers several advantages in the forest management planning when compared with the traditional visual inventory by stands. For example, by combining data from remote sensing imagery with field measurements, it is possible to estimate the forest characteristics of large areas at a more reasonable cost than by using the traditional visual inventory by stands. In this study, the k-nearest-neighbours estimation (k-nn), stand inventory data, and geostatistical interpolation were combined for estimation of five forest variables (mean diameter, mea

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6

Katila, Matti, and Erkki Tomppo. "Selecting estimation parameters for the Finnish multisource National Forest Inventory." Remote Sensing of Environment 76, no. 1 (2001): 16–32. http://dx.doi.org/10.1016/s0034-4257(00)00188-7.

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7

Irulappa-Pillai-Vijayakumar, Dinesh Babu, Jean-Pierre Renaud, François Morneau, Ronald E. McRoberts, and Cédric Vega. "Increasing Precision for French Forest Inventory Estimates using the k-NN Technique with Optical and Photogrammetric Data and Model-Assisted Estimators." Remote Sensing 11, no. 8 (2019): 991. http://dx.doi.org/10.3390/rs11080991.

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Multisource forest inventory methods were developed to improve the precision of national forest inventory estimates. These methods rely on the combination of inventory data and auxiliary information correlated with forest attributes of interest. As these methods have been predominantly tested over coniferous forests, the present study used this approach for heterogeneous and complex deciduous forests in the center of France. The auxiliary data considered included a forest type map, Landsat 8 spectral bands and derived vegetation indexes, and 3D variables derived from photogrammetric canopy hei

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8

Zhu, Yan, Zhongke Feng, Jing Lu, and Jincheng Liu. "Estimation of Forest Biomass in Beijing (China) Using Multisource Remote Sensing and Forest Inventory Data." Forests 11, no. 2 (2020): 163. http://dx.doi.org/10.3390/f11020163.

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Forest biomass reflects the material cycle of forest ecosystems and is an important index to measure changes in forest structure and function. The accurate estimation of forest biomass is the research basis for measuring carbon storage in forest systems, and it is important to better understand the carbon cycle and improve the efficiency of forest policy and management activities. In this study, to achieve an accurate estimation of meso-scale (regional) forest biomass, we used Ninth Beijing Forest Inventory data (FID), Landsat 8 OLI Image data and ALOS-2 PALSAR-2 data to establish different fo

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9

Räty, Minna, Juha Heikkinen, and Annika Kangas. "Assessment of sampling strategies utilizing auxiliary information in large-scale forest inventory." Canadian Journal of Forest Research 48, no. 7 (2018): 749–57. http://dx.doi.org/10.1139/cjfr-2017-0414.

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The National Forest Inventory of Finland (NFI) produces national- and regional-level statistics for sustainability assessment and strategical-level decision making. So far, the regional-level statistics are based on a systematic sampling design with geographical stratification. Auxiliary information such as remote sensing is not used for design or estimation at the regional level, but it is used at the small-area level, i.e., for municipality-level results. To improve the cost efficiency of the NFI, possibilities for using auxiliary data in both the design and estimation are of interest. We as

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10

Zhang, Fanyi, Xin Tian, Haibo Zhang, and Mi Jiang. "Estimation of Aboveground Carbon Density of Forests Using Deep Learning and Multisource Remote Sensing." Remote Sensing 14, no. 13 (2022): 3022. http://dx.doi.org/10.3390/rs14133022.

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Forests are crucial in carbon sequestration and oxygen release. An accurate assessment of forest carbon storage is meaningful for Chinese cities to achieve carbon peak and carbon neutrality. For an accurate estimation of regional-scale forest aboveground carbon density, this study applied a Sentinel-2 multispectral instrument (MSI), Advanced Land Observing Satellite 2 (ALOS-2) L-band, and Sentinel-1 C-band synthetic aperture radar (SAR) to estimate and map the forest carbon density. Considering the forest field-inventory data of eastern China from 2018 as an experimental sample, we explored th

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11

Ehlers, Dekker, Chao Wang, John Coulston, et al. "Mapping Forest Aboveground Biomass Using Multisource Remotely Sensed Data." Remote Sensing 14, no. 5 (2022): 1115. http://dx.doi.org/10.3390/rs14051115.

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The majority of the aboveground biomass on the Earth’s land surface is stored in forests. Thus, forest biomass plays a critical role in the global carbon cycle. Yet accurate estimate of forest aboveground biomass (FAGB) remains elusive. This study proposed a new conceptual model to map FAGB using remotely sensed data from multiple sensors. The conceptual model, which provides guidance for selecting remotely sensed data, is based on the principle of estimating FAGB on the ground using allometry, which needs species, diameter at breast height (DBH), and tree height as inputs. Based on the concep

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12

Majasalmi, Titta, Stephanie Eisner, Rasmus Astrup, Jonas Fridman, and Ryan M. Bright. "An enhanced forest classification scheme for modeling vegetation–climate interactions based on national forest inventory data." Biogeosciences 15, no. 2 (2018): 399–412. http://dx.doi.org/10.5194/bg-15-399-2018.

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Abstract. Forest management affects the distribution of tree species and the age class of a forest, shaping its overall structure and functioning and in turn the surface–atmosphere exchanges of mass, energy, and momentum. In order to attribute climate effects to anthropogenic activities like forest management, good accounts of forest structure are necessary. Here, using Fennoscandia as a case study, we make use of Fennoscandic National Forest Inventory (NFI) data to systematically classify forest cover into groups of similar aboveground forest structure. An enhanced forest classification schem

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13

Hu, Tianyu, YingYing Zhang, Yanjun Su, Yi Zheng, Guanghui Lin, and Qinghua Guo. "Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data." Remote Sensing 12, no. 10 (2020): 1690. http://dx.doi.org/10.3390/rs12101690.

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Mangrove forest ecosystems are distributed at the land–sea interface in tropical and subtropical regions and play an important role in carbon cycles and biodiversity. Accurately mapping global mangrove aboveground biomass (AGB) will help us understand how mangrove ecosystems are affected by the impacts of climatic change and human activities. Light detection and ranging (LiDAR) techniques have been proven to accurately capture the three-dimensional structure of mangroves and LiDAR can estimate forest AGB with high accuracy. In this study, we produced a global mangrove forest AGB map for 2004 a

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14

Katila, M., and E. Tomppo. "Stratification by ancillary data in multisource forest inventories employing k-nearest-neighbour estimation." Canadian Journal of Forest Research 32, no. 9 (2002): 1548–61. http://dx.doi.org/10.1139/x02-047.

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Abstract (sommario):

The Finnish multisource national forest inventory (MS-NFI) utilizes optical area satellite images and digital maps in addition to field plot data to produce georeferenced information, thematic maps, and small-area statistics. In the early version, forestry land (FRYL) was taken directly from the numerical map data. Such data may be outdated and can contain significant errors, for example, the FRYL area is typically overestimated and the mean volume is underestimated. A statistical calibration method has been introduced to reduce the map errors on multisource forest resource estimates. It is ba

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15

Tian, Xin, Jiejie Li, Fanyi Zhang, Haibo Zhang, and Mi Jiang. "Forest Aboveground Biomass Estimation Using Multisource Remote Sensing Data and Deep Learning Algorithms: A Case Study over Hangzhou Area in China." Remote Sensing 16, no. 6 (2024): 1074. http://dx.doi.org/10.3390/rs16061074.

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The accurate estimation of forest aboveground biomass is of great significance for forest management and carbon balance monitoring. Remote sensing instruments have been widely applied in forest parameters inversion with wide coverage and high spatiotemporal resolution. In this paper, the capability of different remote-sensed imagery was investigated, including multispectral images (GaoFen-6, Sentinel-2 and Landsat-8) and various SAR (Synthetic Aperture Radar) data (GaoFen-3, Sentinel-1, ALOS-2), in aboveground forest biomass estimation. In particular, based on the forest inventory data of Hang

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16

Halme, Merja, and Erkki Tomppo. "Improving the accuracy of multisource forest inventory estimates to reducing plot location error — a multicriteria approach." Remote Sensing of Environment 78, no. 3 (2001): 321–27. http://dx.doi.org/10.1016/s0034-4257(01)00227-9.

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17

Zuo, Shudi, Shaoqing Dai, Xiaodong Song, et al. "Determining the Mechanisms that Influence the Surface Temperature of Urban Forest Canopies by Combining Remote Sensing Methods, Ground Observations, and Spatial Statistical Models." Remote Sensing 10, no. 11 (2018): 1814. http://dx.doi.org/10.3390/rs10111814.

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The spatiotemporal distribution pattern of the surface temperatures of urban forest canopies (STUFC) is influenced by many environmental factors, and the identification of interactions between these factors can improve simulations and predictions of spatial patterns of urban cool islands. This quantitative research uses an integrated method that combines remote sensing, ground surveys, and spatial statistical models to elucidate the mechanisms that influence the STUFC and considers the interaction of multiple environmental factors. This case study uses Jinjiang, China as a representative of a

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18

Gašparović, Mateo, and Damir Klobučar. "Mapping Floods in Lowland Forest Using Sentinel-1 and Sentinel-2 Data and an Object-Based Approach." Forests 12, no. 5 (2021): 553. http://dx.doi.org/10.3390/f12050553.

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The impact of floods on forests is immediate, so it is necessary to quickly define the boundaries of flooded areas. Determining the extent of flooding in situ has shortcomings due to the possible limited spatial and temporal resolutions of data and the cost of data collection. Therefore, this research focused on flood mapping using geospatial data and remote sensing. The research area is located in the central part of the Republic of Croatia, an environmentally diverse area of lowland forests of the Sava River and its tributaries. Flood mapping was performed by merging Sentinel-1 (S1) and Sent

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19

Kandel, Pem Narayan. "Estimation of Above Ground Forest Biomass and Carbon Stock by Integrating Lidar, Satellite Image and Field Measurement in Nepal." Journal of Natural History Museum 28 (December 19, 2015): 160–70. http://dx.doi.org/10.3126/jnhm.v28i0.14191.

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For the first time in South Asia, the model-based Lidar Assisted Multisource Program (LAMP) was tested in 23500 km2 TAL area of Nepal by integrating 5% LiDAR sampling, wall-to-wall Rapid Eye satellite image and a representative field inventory to estimate Above Ground Biomass (AGB) and carbon stock. The average 1.26/m2LiDAR point density recorded by the scanner was used to measure canopy height and build a model using LiDAR variables and model coefficients. The developed LAMP model successfully estimated the AGB of the study area. The research tells that the study area comprises almost 50% for

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20

Liu, Huaxin, Qigang Jiang, Yue Ma, et al. "Object-Based Multigrained Cascade Forest Method for Wetland Classification Using Sentinel-2 and Radarsat-2 Imagery." Water 14, no. 1 (2022): 82. http://dx.doi.org/10.3390/w14010082.

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The development of advanced and efficient methods for mapping and monitoring wetland regions is essential for wetland resources conservation, management, and sustainable development. Although remote sensing technology has been widely used for detecting wetlands information, it remains a challenge for wetlands classification due to the extremely complex spatial patterns and fuzzy boundaries. This study aims to implement a comprehensive and effective classification scheme for wetland land covers. To achieve this goal, a novel object-based multigrained cascade forest (OGCF) method with multisenso

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21

Zhang, Qi, Lihua Xu, Maozhen Zhang, et al. "Uncertainty Analysis of Remote Sensing Pretreatment for Biomass Estimation on Landsat OLI and Landsat ETM+." ISPRS International Journal of Geo-Information 9, no. 1 (2020): 48. http://dx.doi.org/10.3390/ijgi9010048.

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The accurate quantification of biomass helps to understand forest productivity and carbon cycling dynamics. Research on uncertainty during pretreatment is still lacking despite it being one of the major sources of uncertainty and an essential step in biomass estimation. In this study, we investigated pretreatment uncertainty and conducted a comparative study on the uncertainty of three optical imagery preprocessing stages (radiometric calibration, atmospheric and terrain correction) in biomass estimation. A combination of statistical models (random forest) and multisource data (Landsat enhance

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22

Su, Ying, Matteo Mura, Xiaoman Zheng, et al. "More Accurately Estimating Aboveground Biomass in Tropical Forests With Complex Forest Structures and Regions of High‐Aboveground Biomass." Journal of Geophysical Research: Biogeosciences 129, no. 6 (2024). http://dx.doi.org/10.1029/2023jg007864.

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AbstractAccurately estimating aboveground biomass (AGB) in tropical forests is vital for managing the threats posed by deforestation, degradation, and climate change. However, challenges persist in accurately estimating AGB in high AGB regions. This study aims to accurately estimate the AGB of regions with high AGB by using spatial statistical analyses based on AGB estimates made by machine‐learning fusion of multisource data. We hypothesize that incorporating dominant auxiliary factors in the analysis increases the estimation accuracy. This study focuses on tropical forests located in Longyan

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23

Shrestha, Him Lal. "Comparison of Different Resolution Satellite Imageries for Forest Carbon Quantification." Journal on Geoinformatics, Nepal, June 15, 2016, 23–26. http://dx.doi.org/10.3126/njg.v15i1.51180.

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The current trend of the monitoring of the forest involves the measurement of aboveground forest biomass carbon using the multisource forest inventory techniques. The multisource forest inventory techniques involve the multiple data inputs such as GIS, Remote sensing, GPS, field measurement and existing information. The remote sensing data are useful for the quantification of aboveground forest carbon using the spectral and spatial characteristics of the data. The application of remote sensing data for the forest carbon quantification may enhance the efficiency in terms of resource allocation,

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24

Ometto, Jean Pierre, Eric Bastos Gorgens, Francisca Rocha de Souza Pereira, et al. "A biomass map of the Brazilian Amazon from multisource remote sensing." Scientific Data 10, no. 1 (2023). http://dx.doi.org/10.1038/s41597-023-02575-4.

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AbstractThe Amazon Forest, the largest contiguous tropical forest in the world, stores a significant fraction of the carbon on land. Changes in climate and land use affect total carbon stocks, making it critical to continuously update and revise the best estimates for the region, particularly considering changes in forest dynamics. Forest inventory data cover only a tiny fraction of the Amazon region, and the coverage is not sufficient to ensure reliable data interpolation and validation. This paper presents a new forest above-ground biomass map for the Brazilian Amazon and the associated unce

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25

Katila, Matti. "Empirical errors of small area estimates from the multisource National Forest Inventory in Eastern Finland." Silva Fennica 40, no. 4 (2006). http://dx.doi.org/10.14214/sf.324.

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26

de Novaes Vianna, Luiz Fernando, Fábio Martinho Zambonim, and Cristina Pandolfo. "Potential cultivation areas of Euterpe edulis (Martius) for rainforest recovery, repopulation and açai production in Santa Catarina, Brazil." Scientific Reports 13, no. 1 (2023). http://dx.doi.org/10.1038/s41598-023-32742-x.

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AbstractEuterpe edulis is an endangered palm species that provides the most important non-timber forest product exploited in its natural habitat, the Brazilian Atlantic Forest hotspot1,4. From 1991 to 2017, pasturelands, agriculture, and monoculture of tree plantations were responsible for 97% of Atlantic Forest deforested areas in Brazil and Santa Catarina was one of the Brazilian states with the greatest loss of forest area14. In the last decade, E. edulis fruits reached their highest commercial value, producing the southeastern equivalent of Amazonian ‘‘açai’’ (Euterpe oleracea)5,7,8. As a

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