scholarly journals The Use of Remote Sensing Data to Estimate Land Area with Forest Vegetation Cover in the Context of Selected Forest Definitions

Forests ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1489
Author(s):  
Tomasz Hycza ◽  
Agnieszka Kamińska ◽  
Krzysztof Stereńczak
Keyword(s):  
Remote Sensing ◽  
Kyoto Protocol ◽  
Vegetation Cover ◽  
Canopy Cover ◽  
Basal Area ◽  
Forest Vegetation ◽  
Forest Area ◽  
Process Time ◽  
Projection Area ◽  
Triangular Grid

(1) Background: Like many other countries, Poland is obliged to report forest area to the Climate Convention (UNFCCC Kyoto Protocol) and the Food and Agriculture Organization of the United Nations (FAO/UN). Differences between national and international forest definitions lead to differences between actual and reported forest area. Remote sensing is a useful tool for estimating forest area for reporting purposes. One of the most important parts of the estimation is the choice of a basal area to calculate the percentage of vegetation cover. (2) Methods: Height, crown projection area, and minimum complex area were used to classify the area with forest vegetation. Percentage canopy cover was determined using three different methods based on segmentation polygons, triangular grid and canopy height model pixels. The accuracy of the above methods was verified by manual vectorization performed on a selected set of test plots in the Milicz study area according to the international definitions. The differences were examined using three statistical metrics. (3) Conclusions: This paper compares for the first time methods for determining the area for which canopy cover is calculated (using data from (ALS) and discusses the differences between them in the context of accuracy (the correspondence between the results and the reference data) and the complexity of the process (time and effort required to perform the analysis). This is important in the context of reporting, estimating carbon stocks and biodiversity to mitigate the effects of climate change. Method 2 proved to be the most accurate method, Method 1 was found to be the worst option. Accuracy was better in the case of the Kyoto Protocol definition.

scholarly journals Rapid Evaluation and Validation Method of Above Ground Forest Biomass Estimation Using Optical Remote Sensing in Tundi Reserved Forest Area, India

2021 ◽  
Vol 10 (1) ◽  
pp. 29
Author(s):  
Praveen Kumar ◽  
Akhouri P. Krishna ◽  
Thorkild M. Rasmussen ◽  
Mahendra K. Pal
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Remote Sensing Data ◽  
Test Site ◽  
Forest Area ◽  
Relational Model ◽  
Biomass Estimation ◽  
Optical Remote Sensing ◽  
Ground Sample ◽  
Sensing Data

Optical remote sensing data are freely available on a global scale. However, the satellite image processing and analysis for quick, accurate, and precise forest above ground biomass (AGB) evaluation are still challenging and difficult. This paper is aimed to develop a novel method for precise, accurate, and quick evaluation of the forest AGB from optical remote sensing data. Typically, the ground forest AGB was calculated using an empirical model from ground data for biophysical parameters such as tree density, height, and diameter at breast height (DBH) collected from the field at different elevation strata. The ground fraction of vegetation cover (FVC) in each ground sample location was calculated. Then, the fraction of vegetation cover (FVC) from optical remote sensing imagery was calculated. In the first stage of method implementation, the relation model between the ground FVC and ground forest AGB was developed. In the second stage, the relational model was established between image FVC and ground FVC. Finally, both models were fused to derive the relational model between image FVC and forest AGB. The validation of the developed method was demonstrated utilizing Sentinel-2 imagery as test data and the Tundi reserved forest area located in the Dhanbad district of Jharkhand state in eastern India was used as the test site. The result from the developed model was ground validated and also compared with the result from a previously developed crown projected area (CPA)-based forest AGB estimation approach. The results from the developed approach demonstrated superior capabilities in precision compared to the CPA-based method. The average forest AGB estimation of the test site obtained by this approach revealed 463 tons per hectare, which matches the previous estimate from this test site.

scholarly journals Mangrove forest structure and composition along urban gradients in Puerto Rico

2018 ◽  
Author(s):  
Benjamin Branoff ◽  
Sebastian Martinuzzi
Keyword(s):  
Puerto Rico ◽  
Surface Water ◽  
Water Chemistry ◽  
Forest Structure ◽  
Vegetation Cover ◽  
Mangrove Forest ◽  
Canopy Cover ◽  
Basal Area ◽  
Surface Water Chemistry ◽  
Nitrogen Concentrations

Urban forests are repeatedly characterized as distinct in composition and structure in comparison with their non-urban counterparts. This holds true for mangroves, although previous studies lack quantified representations of urbanness as well as any inclusion of hydrology or water chemistry, which are important influences on mangrove forest structure, composition, and function. This study uses LiDAR and ground-based measurements of mangroves within well quantified urban gradients in Puerto Rico to test for the relative importance of urbanization alongside flooding metrics and surface water chemistry in explaining observed patterns of forest structure and composition. In simple regression, urban metrics were the most powerful predictors of forest composition but not structure. Results show higher tree diversity but lower mangrove diversity in the most urban forests. Structural measurements, however, were best explained by flooding, surface water chemistry, and non-urban land cover metrics. Nitrogen concentrations best explained stem density and tree size, while flooding metrics best explained stand biomass and basal area, and surrounding vegetation cover best explained canopy cover and height metrics. In multiple regression, land cover and surface water chemistry were more important than flooding, with population density again being the most important variable in explaining mangrove forest diversity. Results show that urbanization is an important influence on mangrove composition and basal area, leading to higher tree diversity and lower basal area, consistent with patterns in terrestrial forests. But urban mangrove forests are also lower in mangrove diversity and tend to have representation only by Laguncularia racemosa. Nitrogen concentrations and surrounding vegetation cover, both of which are indirectly influenced by urbanization, were positively related to tree size and canopy cover and height, respectively. These tests suggest urbanization is an important influence on mangrove forest structure and composition, but that flooding and water chemistry must also be considered when managing these forests.

Forest vegetation cover assessment on Mona Island, Puerto Rico

2012 ◽  
Author(s):  
Thomas J. Brandeis ◽  
Elvia J. Meléndez-Ackerman ◽  
Eileen H. Helmer
Keyword(s):  
Puerto Rico ◽  
Vegetation Cover ◽  
Forest Vegetation ◽  
Mona Island

scholarly journals New Orthophoto Generation Strategies from UAV and Ground Remote Sensing Platforms for High-Throughput Phenotyping

2021 ◽  
Vol 13 (5) ◽  
pp. 860
Author(s):  
Yi-Chun Lin ◽  
Tian Zhou ◽  
Taojun Wang ◽  
Melba Crawford ◽  
Ayman Habib
Keyword(s):  
Remote Sensing ◽  
Control Strategies ◽  
Target Function ◽  
Canopy Cover ◽  
Surface Model ◽  
Flowering Date ◽  
Sensing Platforms ◽  
Geolocation Accuracy ◽  
New Strategies

Remote sensing platforms have become an effective data acquisition tool for digital agriculture. Imaging sensors onboard unmanned aerial vehicles (UAVs) and tractors are providing unprecedented high-geometric-resolution data for several crop phenotyping activities (e.g., canopy cover estimation, plant localization, and flowering date identification). Among potential products, orthophotos play an important role in agricultural management. Traditional orthophoto generation strategies suffer from several artifacts (e.g., double mapping, excessive pixilation, and seamline distortions). The above problems are more pronounced when dealing with mid- to late-season imagery, which is often used for establishing flowering date (e.g., tassel and panicle detection for maize and sorghum crops, respectively). In response to these challenges, this paper introduces new strategies for generating orthophotos that are conducive to the straightforward detection of tassels and panicles. The orthophoto generation strategies are valid for both frame and push-broom imaging systems. The target function of these strategies is striking a balance between the improved visual appearance of tassels/panicles and their geolocation accuracy. The new strategies are based on generating a smooth digital surface model (DSM) that maintains the geolocation quality along the plant rows while reducing double mapping and pixilation artifacts. Moreover, seamline control strategies are applied to avoid having seamline distortions at locations where the tassels and panicles are expected. The quality of generated orthophotos is evaluated through visual inspection as well as quantitative assessment of the degree of similarity between the generated orthophotos and original images. Several experimental results from both UAV and ground platforms show that the proposed strategies do improve the visual quality of derived orthophotos while maintaining the geolocation accuracy at tassel/panicle locations.

scholarly journals Leveraging TLS as a Calibration and Validation Tool for MLS and ULS Mapping of Savanna Structure and Biomass at Landscape-Scales

2021 ◽  
Vol 13 (2) ◽  
pp. 257 ◽  
Author(s):  
Shaun R. Levick ◽  
Tim Whiteside ◽  
David A. Loewensteiner ◽  
Mitchel Rudge ◽  
Renee Bartolo
Keyword(s):  
Remote Sensing ◽  
Vegetation Structure ◽  
Laser Scanning ◽  
Canopy Cover ◽  
Structural Diversity ◽  
Point Clouds ◽  
Large Area ◽  
Calibration And Validation ◽  
Savanna Vegetation ◽  
Landscape Scales

Savanna ecosystems are challenging to map and monitor as their vegetation is highly dynamic in space and time. Understanding the structural diversity and biomass distribution of savanna vegetation requires high-resolution measurements over large areas and at regular time intervals. These requirements cannot currently be met through field-based inventories nor spaceborne satellite remote sensing alone. UAV-based remote sensing offers potential as an intermediate scaling tool, providing acquisition flexibility and cost-effectiveness. Yet despite the increased availability of lightweight LiDAR payloads, the suitability of UAV-based LiDAR for mapping and monitoring savanna 3D vegetation structure is not well established. We mapped a 1 ha savanna plot with terrestrial-, mobile- and UAV-based laser scanning (TLS, MLS, and ULS), in conjunction with a traditional field-based inventory (n = 572 stems > 0.03 m). We treated the TLS dataset as the gold standard against which we evaluated the degree of complementarity and divergence of structural metrics from MLS and ULS. Sensitivity analysis showed that MLS and ULS canopy height models (CHMs) did not differ significantly from TLS-derived models at spatial resolutions greater than 2 m and 4 m respectively. Statistical comparison of the resulting point clouds showed minor over- and under-estimation of woody canopy cover by MLS and ULS, respectively. Individual stem locations and DBH measurements from the field inventory were well replicated by the TLS survey (R2 = 0.89, RMSE = 0.024 m), which estimated above-ground woody biomass to be 7% greater than field-inventory estimates (44.21 Mg ha−1 vs 41.08 Mg ha−1). Stem DBH could not be reliably estimated directly from the MLS or ULS, nor indirectly through allometric scaling with crown attributes (R2 = 0.36, RMSE = 0.075 m). MLS and ULS show strong potential for providing rapid and larger area capture of savanna vegetation structure at resolutions suitable for many ecological investigations; however, our results underscore the necessity of nesting TLS sampling within these surveys to quantify uncertainty. Complementing large area MLS and ULS surveys with TLS sampling will expand our options for the calibration and validation of multiple spaceborne LiDAR, SAR, and optical missions.

scholarly journals Estimating Forest Canopy Cover by Multiscale Remote Sensing in Northeast Jiangxi, China

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 433
Author(s):  
Xiaolan Huang ◽  
Weicheng Wu ◽  
Tingting Shen ◽  
Lifeng Xie ◽  
Yaozu Qin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Forest Canopy ◽  
Canopy Cover ◽  
Google Earth ◽  
Tree Canopy ◽  
Landsat Data ◽  
High Resolution Data ◽  
Modis Ndvi ◽  
Tree Canopy Cover

This research was focused on estimation of tree canopy cover (CC) by multiscale remote sensing in south China. The key aim is to establish the relationship between CC and woody NDVI (NDVIW) or to build a CC-NDVIW model taking northeast Jiangxi as an example. Based on field CC measurements, this research used Google Earth as a complementary source to measure CC. In total, 63 sample plots of CC were created, among which 45 were applied for modeling and the remaining 18 were employed for verification. In order to ascertain the ratio R of NDVIW to the satellite observed NDVI, a 20-year time-series MODIS NDVI dataset was utilized for decomposition to obtain the NDVIW component, and then the ratio R was calculated with the equation R = (NDVIW/NDVI) *100%, respectively, for forest (CC >60%), medium woodland (CC = 25–60%) and sparse woodland (CC 1–25%). Landsat TM and OLI images that had been orthorectified by the provider USGS were atmospherically corrected using the COST model and used to derive NDVIL. R was multiplied for the NDVIL image to extract the woody NDVI (NDVIWL) from Landsat data for each of these plots. The 45 plots of CC data were linearly fitted to the NDVIWL, and a model with CC = 103.843 NDVIW + 6.157 (R2 = 0.881) was obtained. This equation was applied to predict CC at the 18 verification plots and a good agreement was found (R2 = 0.897). This validated CC-NDVIW model was further applied to the woody NDVI of forest, medium woodland and sparse woodland derived from Landsat data for regional CC estimation. An independent group of 24 measured plots was utilized for validation of the results, and an accuracy of 83.0% was obtained. Thence, the developed model has high predictivity and is suitable for large-scale estimation of CC using high-resolution data.

scholarly journals Adjudicating Perspectives on Forest Structure: How Do Airborne, Terrestrial, and Mobile Lidar-Derived Estimates Compare?

2021 ◽  
Vol 13 (12) ◽  
pp. 2297
Author(s):  
Jonathon J. Donager ◽  
Andrew J. Sánchez Meador ◽  
Ryan C. Blackburn
Keyword(s):  
Ponderosa Pine ◽  
Laser Scanning ◽  
Canopy Cover ◽  
Basal Area ◽  
Tree Height ◽  
Absolute Error ◽  
Forest Monitoring ◽  
Individual Tree ◽  
Structural Configurations ◽  
Individual Trees

Applications of lidar in ecosystem conservation and management continue to expand as technology has rapidly evolved. An accounting of relative accuracy and errors among lidar platforms within a range of forest types and structural configurations was needed. Within a ponderosa pine forest in northern Arizona, we compare vegetation attributes at the tree-, plot-, and stand-scales derived from three lidar platforms: fixed-wing airborne (ALS), fixed-location terrestrial (TLS), and hand-held mobile laser scanning (MLS). We present a methodology to segment individual trees from TLS and MLS datasets, incorporating eigen-value and density metrics to locate trees, then assigning point returns to trees using a graph-theory shortest-path approach. Overall, we found MLS consistently provided more accurate structural metrics at the tree- (e.g., mean absolute error for DBH in cm was 4.8, 5.0, and 9.1 for MLS, TLS and ALS, respectively) and plot-scale (e.g., R2 for field observed and lidar-derived basal area, m2 ha−1, was 0.986, 0.974, and 0.851 for MLS, TLS, and ALS, respectively) as compared to ALS and TLS. While TLS data produced estimates similar to MLS, attributes derived from TLS often underpredicted structural values due to occlusion. Additionally, ALS data provided accurate estimates of tree height for larger trees, yet consistently missed and underpredicted small trees (≤35 cm). MLS produced accurate estimates of canopy cover and landscape metrics up to 50 m from plot center. TLS tended to underpredict both canopy cover and patch metrics with constant bias due to occlusion. Taking full advantage of minimal occlusion effects, MLS data consistently provided the best individual tree and plot-based metrics, with ALS providing the best estimates for volume, biomass, and canopy cover. Overall, we found MLS data logistically simple, quickly acquirable, and accurate for small area inventories, assessments, and monitoring activities. We suggest further work exploring the active use of MLS for forest monitoring and inventory.

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