scholarly journals Assessing Typhoon-Induced Canopy Damage Using Vegetation Indices in the Fushan Experimental Forest, Taiwan

2020 ◽  
Vol 12 (10) ◽  
pp. 1654
Author(s):  
Jonathan Peereman ◽  
James Aaron Hogan ◽  
Teng-Chiu Lin
Keyword(s):  
Vegetation Cover ◽  
Forest Canopy ◽  
Vegetation Index ◽  
Forest Cover ◽  
Spatial Scales ◽  
Landsat Imagery ◽  
Vegetation Indices ◽  
Vegetation Heterogeneity ◽  
Induced Changes ◽  
Fushan Experimental Forest

Cyclonic windstorms profoundly affect forest structure and function throughout the tropics and subtropics. Remote sensing techniques and vegetation indices (VIs) have improved our ability to characterize cyclone impacts over broad spatial scales. Although VIs are useful for understanding changes in forest cover, their consistency on detecting changes in vegetation cover is not well understood. A better understanding of the similarities and differences in commonly used VIs across disturbance events and forest types is needed to reconcile the results from different studies. Using Landsat imagery, we analyzed the change between pre- and post-typhoon VI values (ΔVIs) of four VIs for five typhoons (local name of cyclones in the North Pacific) that affected the Fushan Experimental Forest of Taiwan. We found that typhoons varied in their effect on forest canopy cover even when they had comparable trajectories, wind speeds, and rainfall. Most VIs measured a decrease in forest cover following typhoons, ranging from −1.18% to −19.87%; however, the direction of ΔVI–topography relationships varied among events. All typhoons significantly increased vegetation heterogeneity, and ΔVI was negatively related to pre-typhoon VI across all typhoons. Four of the five typhoons showed that more frequently affected sites had greater VI decreases. VIs ranged in their sensitivity to detect typhoon-induced changes in canopy coverage, and no single VI was most sensitive across all typhoons. Therefore, we recommend using VIs in combination—for example Normalized Difference Infrared Index (NDII) and Enhanced Vegetation Index (EVI), when comparing cyclone-disturbance-induced changes in vegetation cover among disturbances and across forests.

scholarly journals Investigation on Zagros forests cover changes under the recent droughts using satellite imagery

2019 ◽  
Vol 65 (No. 1) ◽  
pp. 9-17 ◽  
Author(s):  
Marjan Goodarzi ◽  
Mehdi Pourhashemi ◽  
Zahra Azizi
Keyword(s):  
Vegetation Index ◽  
Forest Cover ◽  
Landsat Imagery ◽  
Vegetation Indices ◽  
Annual Rainfall ◽  
Forest Cover Change ◽  
Water Index ◽  
Forest Dieback ◽  
Zagros Forests ◽  
Average Annual Temperature

Oak decline phenomenon has recently led to considerable dieback within Zagros forests, western Iran. In the present study, Landsat imagery (2005 to 2016) and synoptic station data were used to study the forest dieback in Dorood, Lorestan province. Sixteen vegetation indices were calculated and values in each year were obtained. The correlations between the index and climatic parameters of rainfall, temperature and relative humidity were investigated. Results showed that the correlation of some indices with rainfall and the correlation of other indices with temperature were more than 70%. Optimized soil adjusted vegetation index had 80% correlation with annual rainfall and the modification of normalized difference water index was correlated with average annual temperature by 75%. Using the numerical value changes of the indices, a map of forest cover change was prepared in four classes; healthy, weak, moderate and severe dieback and the process of its change were compared with the trend of variations in regard with rainfall values in the study period. There was a close relationship between changes in the area of forest cover dieback and rainfall and temperature values.

scholarly journals Vegetation Indices Do Not Capture Forest Cover Variation in Upland Siberian Larch Forests

2018 ◽  
Vol 10 (11) ◽  
pp. 1686 ◽  
Author(s):  
Michael Loranty ◽  
Sergey Davydov ◽  
Heather Kropp ◽  
Heather Alexander ◽  
Michelle Mack ◽  
...  
Keyword(s):  
Carbon Storage ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Forest Cover ◽  
Global Climate ◽  
Vegetation Indices ◽  
Temporal Trends ◽  
Siberian Larch ◽  
Field Observations ◽  
Larch Forests

Boreal forests are changing in response to climate, with potentially important feedbacks to regional and global climate through altered carbon cycle and albedo dynamics. These feedback processes will be affected by vegetation changes, and feedback strengths will largely rely on the spatial extent and timing of vegetation change. Satellite remote sensing is widely used to monitor vegetation dynamics, and vegetation indices (VIs) are frequently used to characterize spatial and temporal trends in vegetation productivity. In this study we combine field observations of larch forest cover across a 25 km2 upland landscape in northeastern Siberia with high-resolution satellite observations to determine how the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) are related to forest cover. Across 46 forest stands ranging from 0% to 90% larch canopy cover, we find either no change, or declines in NDVI and EVI derived from PlanetScope CubeSat and Landsat data with increasing forest cover. In conjunction with field observations of NDVI, these results indicate that understory vegetation likely exerts a strong influence on vegetation indices in these ecosystems. This suggests that positive decadal trends in NDVI in Siberian larch forests may correspond primarily to increases in understory productivity, or even to declines in forest cover. Consequently, positive NDVI trends may be associated with declines in terrestrial carbon storage and increases in albedo, rather than increases in carbon storage and decreases in albedo that are commonly assumed. Moreover, it is also likely that important ecological changes such as large changes in forest density or variable forest regrowth after fire are not captured by long-term NDVI trends.

Assessment of the State of the Territories of Closed Waste Disposal Facilities by Vegetation Indices

2021 ◽  
Vol 25 (9) ◽  
pp. 30-37
Author(s):  
N.N. Sliusar ◽  
A.P. Belousova ◽  
G.M. Batrakova ◽  
R.D. Garifzyanov ◽  
M. Huber-Humer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Waste Disposal ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Cost Effective ◽  
Google Earth ◽  
The State ◽  
Landsat 8 ◽  
The Earth

The possibilities of using remote sensing of the Earth data to assess the formation of phytocenoses at reclaimed dumps and landfills are presented. The objects of study are landfills and dumps in the Perm Territory, which differed from each other in the types and timing of reclamation work. The state of the vegetation cover on the reclaimed and self-overgrowing objects was compared with the reference plots with naturally formed herbage of zonal meadow vegetation. The process of reclamation of the territory of closed landfills was assessed by the presence and homogeneity of the vegetation layer and by the values of the vegetation index NDVI. To identify the dynamics of changes in the vegetation cover, we used multi-temporal satellite images from the open resources of Google Earth and images in the visible and infrared ranges of the Landsat-5/TM and Landsat-8/OLI satellites. It is shown that the data of remote sensing of the Earth, in particular the analysis of vegetation indices, can be used to assess the dynamics of overgrowing of territories of reclaimed waste disposal facilities, as well as an additional and cost-effective method for monitoring the restoration of previously disturbed territories.

scholarly journals Uncertainty of Vegetation Green-Up Date Estimated from Vegetation Indices Due to Snowmelt at Northern Middle and High Latitudes

2020 ◽  
Vol 12 (1) ◽  
pp. 190 ◽  
Author(s):  
Ruyin Cao ◽  
Yan Feng ◽  
Xilong Liu ◽  
Miaogen Shen ◽  
Ji Zhou
Keyword(s):  
Western Europe ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Scales ◽  
Vegetation Indices ◽  
High Latitudes ◽  
Large Spatial Scale ◽  
Arid Grasslands ◽  
Seasonal Snow Cover ◽  
Spring Snowmelt

Vegetation green-up date (GUD), an important phenological characteristic, is usually estimated from time-series of satellite-based normalized difference vegetation index (NDVI) data at regional and global scales. However, GUD estimates in seasonally snow-covered areas suffer from the effect of spring snowmelt on the NDVI signal, hampering our realistic understanding of phenological responses to climate change. Recently, two snow-free vegetation indices were developed for GUD detection: the normalized difference phenology index (NDPI) and normalized difference greenness index (NDGI). Both were found to improve GUD detection in the presence of spring snowmelt. However, these indices were tested at several field phenological camera sites and carbon flux sites, and a detailed evaluation on their performances at the large spatial scale is still lacking, which limits their applications globally. In this study, we employed NDVI, NDPI, and NDGI to estimate GUD at northern middle and high latitudes (north of 40° N) and quantified the snowmelt-induced uncertainty of GUD estimations from the three vegetation indices (VIs) by considering the changes in VI values caused by snowmelt. Results showed that compared with NDVI, both NDPI and NDGI improve the accuracy of GUD estimation with smaller GUD uncertainty in the areas below 55° N, but at higher latitudes (55°N-70° N), all three indices exhibit substantially larger GUD uncertainty. Furthermore, selecting which vegetation index to use for GUD estimation depends on vegetation types. All three indices performed much better for deciduous forests, and NDPI performed especially well (5.1 days for GUD uncertainty). In the arid and semi-arid grasslands, GUD estimations from NDGI are more reliable (i.e., smaller uncertainty) than NDP-based GUD (e.g., GUD uncertainty values for NDGI vs. NDPI are 4.3 d vs. 7.2 d in Mongolia grassland and 6.7 d vs. 9.8 d in Central Asia grassland), whereas in American prairie, NDPI performs slightly better than NDGI (GUD uncertainty for NDPI vs. NDGI is 3.8 d vs. 4.7 d). In central and western Europe, reliable GUD estimations from NDPI and NDGI were acquired only in those years without snowfall before green-up. This study provides important insights into the application of, and uncertainty in, snow-free vegetation indices for GUD estimation at large spatial scales, particularly in areas with seasonal snow cover.

scholarly journals Response of Forest to Climatic Events and Human Management at Fort Leavenworth, Kansas

2002 ◽  
pp. 1-24
Author(s):  
James S. Aber ◽  
Juliet Wallace ◽  
Matthew C. Nowak
Keyword(s):  
Tree Ring ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Forest Cover ◽  
Aerial Photographs ◽  
Forest Understory ◽  
Drought Severity ◽  
Upland Forest ◽  
Bottomland Forest ◽  
Bottomland Forests

Characteristics and temporal changes in forest cover from 1987 to 1997 were documented on the basis of remote sensing for two study forests at Fort Leavenworth, northeastern Kansas. Eight Landsat 5 Thematic Mapper (TM) datasets from the month of July cover the study period, which included a major drought in 1988-1989 and flooding along the Missouri River in 1993. Other data sources included kite aerial photographs, digital orthophotos, tree-ring cores, climatic records, and ground observations. Three study areas were evaluated from Landsat TM datasets: (1) the entire Fort Leavenworth area; (2) an upland, hardwood forest composed mainly of oaks; and (3) a bottomland, softwood forest dominated by cottonwood. Normalized Difference Vegetation Index (NDVI) values were derived from these three study sets and subjected to image differencing and principal-component analysis. The TM band 5:4 ratio was also analyzed for the two study forests. Values and trends derived from Landsat imagery were compared to data on tree-ring growth in upland oaks and regional climatic events. Annual growth of tree rings in upland oaks is tied closely to precipitation and the Palmer Drought Severity Index (PDSI); however, changes in NDVI values lag one to two years behind the onset of climatic events, particularly drought episodes. During the first year of drought (1988), vegetation cover in the upland and bottomland forests reacted in different ways: with a slight decline in the upland forest and a slight increase in the bottomland forest. The increased vegetation in the bottomland forest presumably resulted from more understory growth in dry hollows and potholes. In the second year of drought (1989), both forests suffered a marked decline in vegetation cover. NDVI values reached their minima for all categories (whole area, upland forest, and bottomland forest) in 1990, even though precipitation and tree-ring growth increased substantially that year. We conclude that changes in Landsat-derived NDVI values are out of phase with climatic events and variations in tree-ring growth for both upland and bottomland forests in northeastern Kansas and northwestern Missouri. Overall change (1987 to 1997) for NDVI values is down slightly for all categories of evaluation. This probably reflects reduced precipitation throughout the study period compared to the long-term average. Changes in vegetation took place mainly on the forest margins. Such changes are thought to result from microclimatic stress at forest edges. The bottomland study forest also was impacted by severe flooding in 1993. Routine human activities may have resulted in minor changes along the margins of both study forests. The bottomland forest was affected by intentional burning of the adjacent prairie in April 2000. Cottonwood trees at the forest edge were killed or injured by the prairie fire, which penetrated the forest understory some distance.

scholarly journals Temporal Influences of Vegetation Cover (C) Dynamism on MUSLE Sediment Yield Estimates: NDVI Evaluation

Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2707
Author(s):  
David Gwapedza ◽  
Denis Arthur Hughes ◽  
Andrew Robert Slaughter ◽  
Sukhmani Kaur Mantel
Keyword(s):  
Sediment Yield ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Scale Validation ◽  
Spatial Scales ◽  
Temporal Variations ◽  
Early Summer ◽  
Landsat 8 ◽  
Temporal Scales ◽  
Normalised Difference Vegetation Index

Vegetation cover is an important factor controlling erosion and sediment yield. Therefore, its effect is accounted for in both experimental and modelling studies of erosion and sediment yield. Numerous studies have been conducted to account for the effects of vegetation cover on erosion across spatial scales; however, little has been conducted across temporal scales. This study investigates changes in vegetation cover across multiple temporal scales in Eastern Cape, South Africa and how this affects erosion and sediment yield modelling in the Tsitsa River catchment. Earth observation analysis and sediment yield modelling are integrated within this study. Landsat 8 imagery was processed, and Normalised Difference Vegetation Index (NDVI) values were extracted and applied to parameterise the Modified Universal Soil Loss Equation (MUSLE) vegetation (C) factor. Imagery data from 2013–2018 were analysed for an inter-annual trend based on reference summer (March) images, while monthly imagery for the years 2016–2017 was analysed for intra-annual trends. The results indicate that the C exhibits more variation across the monthly timescale than the yearly timescale. Therefore, using a single month to represent the annual C factor increases uncertainty. The modelling shows that accounting for temporal variations in vegetation cover reduces cumulative simulated sediment by up to 85% across the inter-annual and 30% for the intra-annual scale. Validation with observed data confirmed that accounting for temporal variations brought cumulative sediment outputs closer to observations. Over-simulations are high in late autumn and early summer, when estimated C values are high. Accordingly, uncertainties are high in winter when low NDVI leads to high C, whereas dry organic matter provides some protection from erosion. The results of this study highlight the need to account for temporal variations in vegetation cover in sediment yield estimation but indicate the uncertainties associated with using NDVI to estimate C factor.

scholarly journals Normalized Difference Vegetation Index (NDVI) analysis for vegetation cover in Leuser Ecosystem area, Sumatra, Indonesia

2021 ◽  
Vol 22 (3) ◽  
Author(s):  
Adisti Permatasari Putri Hartoyo ◽  
ARZYANA SUNKAR ◽  
RAHMATULLOH RAMADANI ◽  
SYAHLAN FALUTHI ◽  
SYAFITRI HIDAYATI
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Forest Cover ◽  
Normalized Difference Vegetation Index ◽  
Agroforestry System ◽  
Conservation Strategy ◽  
Infrastructure Development ◽  
Market Demand ◽  
Conservation Area ◽  
Vegetation Diversity

Abstract. Hartoyo APP, Sunkar A, Ramadani R, Faluthi S, Hidayati S. 2021. Normalized Difference Vegetation Index (NDVI) analysis for vegetation cover in Leuser Ecosystem area, Sumatra, Indonesia. Biodiversitas 22: 1160-1171. About 2 Mha of 24.3 Mha degraded area in Indonesia is inside conservation area. The Leuser Ecosystem Area (LEA) is the largest conservation area in Malesian forest that plays essential role in biodiversity and ecosystem services conservation efforts. It is the last habitat on earth where Sumatran tigers, elephants, orangutans, and rhinoceros are found together. However, LEA faces many threats, such as infrastructure development, and industrial palm oil plantation. Additionally, vegetation cover data as an approach to monitor forest cover changes in LEA is still lacking and baseline data regarding composition, structure as well as vegetation diversity in LEA is very limited. The objectives of this study were to analyze vegetation cover using Normalized Difference Vegetation Index (NDVI) in LEA and its relation to agroforestry structure, composition and diversity in Agusen Village, Gayo Lues District and Alur Durin Village, East Aceh District, Aceh Province belonging to LEA. Based on the NDVI analysis, the largest area in LEA belonged to class 5, meaning that the most area in LEA was dominated by high dense vegetation (1,870,116.40 ha). The average accuracy and standard error of NDVI analysis were 83.33% and 2.62. LEA is an effective buffer for maintaining forest ecosystems and increasing the local communities' welfare through agroforestry system. Agroforestry structures in agroforestry practices, both in Agusen Village and Alur Durin Village did not reflect reverse-J curve, meaning that enrichment planting for increasing numbers of individual and species was necessary. Management of agroforestry system depends on the landowners or managers and their selection of shade tree species with high economic value with market demand such as C. arabica, T. cacao with A. moluccanus, L. leucocephala, H. brasiliensis, D. zibethinus, etc. Trees that produce non-timber products are also an alternative way for conservation strategy and sustainable utilization.

scholarly journals The Use of Unmanned Aerial Vehicles to Determine Differences in Vegetation Cover: A Tool for Monitoring Coastal Wetland Restoration Schemes

2020 ◽  
Vol 12 (24) ◽  
pp. 4022
Author(s):  
Jonathan Dale ◽  
Niall G. Burnside ◽  
Charley Hill-Butler ◽  
Maureen J. Berg ◽  
Conor J. Strong ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Coastal Wetland ◽  
Morphological Variability ◽  
Vegetation Indices ◽  
Vegetation Coverage ◽  
Surface Model ◽  
Anthropogenic Pressures ◽  
Vegetation Development ◽  
Combining Data

Managed realignment (MR) sites are being implemented to compensate for the loss of natural saltmarsh habitat due to sea level rise and anthropogenic pressures. However, MR sites have been recognised to have lower morphological variability and coverage of saltmarsh vegetation than natural saltmarsh sites, which have been linked with the legacy of the historic (terrestrial) land use. This study assesses the relationship between the morphology and vegetation coverage in three separate zones, associated with the legacy of historic reclamation, of a non-engineered MR site. The site was selected due to the phased historical reclamation, and because no pre-breaching landscaping or engineering works were carried out prior to the more recent and contemporary breaching of the site. Four vegetation indices (Excess Green Index, Green Chromatic Coordinate, Green-Red Vegetation Index, and Visible Atmospherically Resistant Index) were calculated from unmanned aerial vehicle imagery; elevation, slope, and curvature surface models were calculated from a digital surface model (DSM) generated from the same imagery captured at the MR site. The imagery and DSM summarised the three zones present within the MR site and the adjacent external natural marsh, and were used to examine the site for areas of differing vegetation cover. Results indicated statistically significant differences between the vegetation indices across the three zones. Statistically significant differences in the vegetation indices were also found between the three zones and the external natural saltmarsh. However, it was only in the zone nearest the breach, and for three of the four indices, that a moderate to strong correlation was found between elevation and the vegetation indices (r = 0.53 to 0.70). This zone was also the lowest in elevation and exhibited the lowest average value for all indices. No relationship was found between the vegetation indices and either the slope or curvature in any of the zones. The approach outlined in this paper provides coastal managers with a relatively low-cost, low-field time method of assessing the areas of vegetation development in MR sites. Moreover, the findings indicate the potential importance of considering the historic morphological and sedimentological changes in the MR sites. By combining data on the areas of saltmarsh colonisation with a consideration of the site’s morphological and reclamation history, the areas likely to support saltmarsh vegetation can be remotely identified in the design of larger engineered MR sites maximising the compensation for the loss of saltmarsh habitat elsewhere.

Airborne Hyperspectral Imaging for Monitoring Geothermal Activity Through Vegetation

2021 ◽  
Author(s):  
Cecilia Rodriguez-Gomez ◽  
Gabor Kereszturi ◽  
Robert Reeves ◽  
Andrew Rae ◽  
Reddy Pullanagari ◽  
...  
Keyword(s):  
Remote Sensing ◽  
New Zealand ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Thermal Infrared ◽  
Geothermal Activity ◽  
Vegetation Health ◽  
Infrared Data

<p>Remote sensing techniques are used to explore geothermal areas. They can offer spatial, temporal and spectral information to map lithological boundaries and hydrothermal alteration in a fast and cheap manner. However, some geothermal areas are densely covered by vegetation, which can hamper remote sensing monitor efforts for geothermal areas.</p><p>Vegetation cover in geothermal areas can reflect the subsurface activity, reacting to interactions between soil’s chemical conditions, heat and gas emissions. An example of such is kanuka (i.e. kunzea ericoides), an endemic shrub of geothermal areas in the Taupo Volcanic Zone (TVZ), New Zealand, which has been used as an indicator species for ground-based geothermal studies. This study assesses the use of airborne hyperspectral and thermal data over the Waiotapu Geothermal Field, TVZ, New Zealand, analysing kanuka shrub surface cover and its spectral response to geothermal activity. To explore the capability in hyperspectral remote sensing for geothermal site mapping and exploration, a series of vegetation indices, including; Anthocyanin Reflectance Index, Atmospherically Resistant Vegetation Index, Moisture Stress Index, Normalised Difference Vegetation Index, Simple Ratio Index, Vogelmann Index and Water Band Index were calculated from narrow bandwidth high-resolution hyperspectral.</p><p>The spectral response of vegetation was then analysed to explore the effects of geothermal heat, offering surrogate information on vegetation health. Vegetation indices results were compared against the thermal infrared data by visual interpretation and quantitative analyses, which shows strong spatial correlation among the vegetation cover type and heat distribution. Furthermore, exponential trendlines produced the best fit between vegetation indices and thermal infrared data. This correlation indicates soil temperatures affect the vegetation health (e.g. chlorophyll concentrations, newly forming leaves, water content). This relationship can highlight that there is valuable information in airborne hyperspectral data to complement exploration efforts, such as heat flux mapping. We conclude kanuka shrub has the potential to be employed as a proxy in exploration and monitoring of geothermal areas in New Zealand from remote sensing platforms.</p>

scholarly journals Landscape Representation by a Permanent Forest Plot and Alternative Plot Designs in a Typhoon Hotspot, Fushan, Taiwan

2020 ◽  
Vol 12 (4) ◽  
pp. 660 ◽  
Author(s):  
Jonathan Peereman ◽  
James Aaron Hogan ◽  
Teng-Chiu Lin
Keyword(s):  
Forest Dynamics ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landscape Heterogeneity ◽  
Spatial Scales ◽  
Vegetation Indices ◽  
Permanent Plots ◽  
Forest Plot ◽  
Landsat 8 ◽  
Four Square

Permanent forest dynamics plots have provided valuable insights into many aspects of forest ecology. The evaluation of their representativeness within the landscape is necessary to understanding the limitations of findings from permanent plots at larger spatial scales. Studies on the representativeness of forest plots with respect to landscape heterogeneity and disturbance effect have already been carried out, but knowledge of how multiple disturbances affect plot representativeness is lacking—particularly in sites where several disturbances can occur between forest plot censuses. This study explores the effects of five typhoon disturbances on the Fushan Forest Dynamics Plot (FFDP) and its surrounding landscape, the Fushan Experimental Forest (FEF), in Taiwan where typhoons occur annually. The representativeness of the FFDP for the FEF was studied using four topographical variables derived from a digital elevation model and two vegetation indices (VIs), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII), calculated from Landsat-5 TM, Landsat-7 ETM+, and Landsat-8 OLI data. Representativeness of four alternative plot designs were tested by dividing the FFDP into subplots over wider elevational ranges. Results showed that the FFDP neither represents landscape elevational range (<10%) nor vegetation cover (<7% of the interquartile range, IQR). Although disturbance effects (i.e., ΔVIs) were also different between the FFDP and the FEF, comparisons showed no under- or over-exposure to typhoon damage frequency or intensity within the FFDP. In addition, the ΔVIs were of the same magnitudes in the plots and the reserve, and the plot covered 30% to 75.9% of IQRs of the reserve ΔVIs. Unexpectedly, the alternative plot designs did not lead to increased representation of damage for 3 out of the 4 tested typhoons and they did not suggest higher representativeness of rectangular vs. square plots. Based on the comparison of mean Euclidian distances, two rectangular plots had smaller distances than four square or four rectangular plots of the same area. Therefore, this study suggests that the current FFDP provides a better representation of its landscape disturbances than alternatives, which contained wider topographical variation and would be more difficult to conduct ground surveys. However, upscaling needs to be done with caution as, in the case of the FEF, plot representativeness varied among typhoons.

Sign in / Sign up

Export Citation Format

Share Document