Current Vegetation Conditions Research Articles

A Deep Learning-Based Approach to Predict Large-Scale Dynamics of Normalized Difference Vegetation Index for the Monitoring of Vegetation Activities and Stresses Using Meteorological Data

Vegetation activities and stresses are crucial for vegetation health assessment. Changes in an environment such as drought do not always result in vegetation drought stress as vegetation responses to the climate involve complex processes. Satellite-based vegetation indices such as the Normalized Difference Vegetation Index (NDVI) have been widely used to monitor vegetation activities. As satellites only carry information for understanding past and current vegetation conditions, there is a need to model vegetation dynamics to make future predictions. Although many other factors are related, we attempt to predict the vegetation activities and stresses via simulating NDVI, based on only meteorological data and using a deep learning method (bidirectional long short-term memory model, BiLSTM). The BiLSTM is a sequence processing model that can predict NDVI by establishing the relationship between meteorological variables and vegetation activities. Experimental results show that the predicted NDVI is consistent with the reference data (R2 = 0.69 ± 0.28). The best accuracy was achieved in the deciduous forest (R2 = 0.87 ± 0.16). The vegetation condition index (VCI) calculated from the BiLSTM-predicted NDVI also agreed with the satellite-based ones (R2 = 0.70 ± 0.28). Both the monitored and predicted VCI indicated an upward but insignificant trend of vegetation activity in the past decade and increased vegetation stresses in the early growing season over northern China. Based on meteorological data, the deep learning-based solution shows the potential for not only retrospective analysis, but also future prediction of vegetation activities and stresses under varied climate conditions as compared with remote sensing data.

Developing a new testate amoeba hydrological transfer function for permafrost peatlands of NW Siberia

The vast areas of permafrost peatlands in the West Siberian Lowland (WSL) hold a significant amount of carbon currently at risk due to human-caused global warming. The rapid thawing of about 20–60% of the frozen soils of Western Siberia adds to the destruction of the region's signature palsa landscape and the development of thermokarst lakes or fens. Due to the complex interactions between climate and the ecosystem that shape its formation and degradation, permafrost reacts in different ways to environmental changes. Understanding recent changes in Western Siberia requires knowledge of previous environmental transitions, which needs to be enhanced further. This study, aimed to create a hydrological testate amoebae (TA) transfer function (TF) from the Khanymei area in the WSL to reconstruct past hydrological conditions in permafrost peatlands. In addition, this research intended to examine the influence of TA communities living in lichens (genus Cladonia) on the performance of TF by integrating these data with a calibration data set. In the summer of 2019, 76 modern samples of Sphagnum, Cladonia, and vascular plants from permafrost and non-permafrost sites were collected. Moreover, one peat core was extracted from the same area to reconstruct the depth to water table (DWT) quantitatively. The Shannon's diversity index, a quantitative measure of species richness, was calculated for each sample to estimate the TA species diversity in a community. Non-metric multidimensional scaling ordination was applied to the dataset to explore the similarities in TA communities between samples and determine whether the DWT controls the TA community structure. Results showed that the typical transition of TA species along the microtopographic (wet–dry) gradient is influenced by the presence of lichens on the surface of the peatland. Statistical analysis confirmed that TA communities in Cladonia are less affected by DWT. Therefore, the TF was constructed on the entire dataset (n = 75) and the dataset with Cladonia excluded (n = 55). Both these TFs showed satisfactory results. Even though the TF constructed on the entire dataset showed fewer predictive abilities, it provided a complete picture of the current vegetation conditions of Khanymei peatlands, where lichens are an essential element of the ecosystem. Therefore, it is assumed that TF with local environment traits may be more suitable for quantitative reconstruction, particularly in the newly explored permafrost area of Western Siberia.

A Hierarchical Binary Process Model to Assess Deviation from Desired Ecological Condition across a Broad Forested Landscape in Alabama

This work describes the development and analysis of a spatially explicit environmental model to estimate the current, ecological, condition class of a managed forest landscape in the southern United States. The model could be extendable to other similar temperate forest landscapes, yet is characterized as a problem-specific, hierarchical, binary process model given the explicit relationships it recognizes between the management of southern United States pine-dominated natural forests and historical ecological conditions. The model is theoretical, based on informed proposals of the landscape processes that influence the ecological condition, and their relationship to perceived ecological condition. The modeling effort is based on spatial data that describe the historical forest community classes, forest plan provisions, fire history, silvicultural treatments, and current vegetation conditions, and six potential ecological condition classes (ECC) are assigned to lands. A case study was provided involving a large national forest, and validation of the outcomes of the modelling effort suggested that the overall accuracy when predicting the exact ecological condition class was about 46%, while the overall accuracy ±1 class was about 81%. For large, heterogeneous forest areas, issues remain in estimating the input variables relatively accurately, particularly the pine basal area.

Structure and composition of tree community in the upstream area of Batang Mahat Watershed, Lima Puluh Kota District, West Sumatra, Indonesia

Abstract. Ekawaty E, Yonariza, Ekaputra EG, Arbain A. 2021. Structure and composition of tree community in the upstream area of Batang Mahat Watershed, Lima Puluh Kota District, West Sumatra, Indonesia. Biodiversitas 23: 687-696. Land-use change at the upstream area of Batang Mahat Watershed is assumed to cause flood during the rainy season and is related to vegetation community which has been heavily exploited. Information regarding the current vegetation condition in this area has never been studied; therefore, this study aims to investigate the structure and composition of the tree community in the upstream area of Batang Mahat at Jorong Kubu Baru Nagari, Baruh Gunung, Bukit Barisan District. Furthermore, sampling was implemented using three 20x50 m belt-transects with 10 m intervals between each sample. The results showed 1) 116 identified and three unidentified tree samples belonging to 40 families; 2) The trees with the highest important value index (IVI) in transect 1 were Rhodoleia championii (63.339%) and Calophylum soulatteri (23.697%), in transect 2 Voacanga foetida (48.568%) and Schima wallichii (32.756%), and in transect 3 Schima wallichii (42.964%) and Voacanga foetida (40.713%); 3) The saplings with the highest IVI in transect 1 were Timonius sp. (33.111%) and Alstonia sp. (21.345%), in transect 2 Hancea pengangensis (24.551%) and Sterculia sp. (21.199%), as well as transect 3 Voacanga foetida (42.862%), and Macaranga lowii (30.844%); 4) Both vegetation categories have considerably high diversity (H’ tree 3.13-3.2, H’ saplings 3.04-3.36), and were moderately distributed across studied transects (E1 = 2.025, E2 = 2.089, E3 = 2.016). From the results, it can be concluded that the forest in the upstream area of the Batang Mahat watershed, which was located in Jorong Kubu Baru, Nagari Baruh Gunung, was still in the good category.

Forest fire action on vegetation from the perspective of trend analysis in future climate change scenarios for a Brazilian savanna region

The Cerrado region has adapted to fires because of its climate and vegetation conditions, but with increasing frequency, regional fires will begin to degrade the biome. Therefore, research is necessary to determine whether the occurrence of fires is beneficial or degrading to the environment, and if the fire origin is natural or anthropic. The objective of this study was to evaluate the vulnerability of vegetation to different scenarios of future climate change. The study area was the Ecological Station of Uruçuí-Una, located in the south of Piaui. The methodology consisted in identifying burnt areas in terms of their recurrence; evaluating the correlation between burnt areas and precipitation and temperature; analyzing burnt areas and the Mann Kendall trend for vegetation for the period 2001 to 2018. Based on the current vegetation conditions, the vegetation and land use were estimated for the year 2100 through the Markov chain and the vulnerable areas were estimated observing different scenarios of climate change of the Coupled Model Intercomparison Project Fase 6 (CMIP6). The results indicated that fires with frequencies above 5 in the interval of 18 years have a negative Pearson correlation and the Mann-Kendall analysis, which implies a state of vegetation degradation. Regarding the future climate scenarios, savanna sites with temperatures above 38 °C, a vegetation index below 0.4, and monthly precipitation less than 90 mm were those with the highest level of vulnerability. Vulnerability mapping can help to improve public policies for fire management for the sustainable development of this region.

An assessment and local perception of sand deposits induced desertification in Sokoto State, Nigeria

Sokoto State is one of the states in Nigeria that are prone to desertification. This study attempts to identify and delimit areas affected by desertification in the three northernmost Local Government Areas of Sokoto State, namely: Illela, Gada and Tangaza. Field study was carried out to verify the incident and occurrence of desertification and the menace of sand deposition. A simple random sampling method with questionnaire, combined with Focused Group Discussions and Key Informant Interviews was used to derive the local knowledge about sand transport and deposition, and, its socio-environmental impacts. The Digital Elevation Model of Tangaza, Gada, and Illela Local Government Areas for the year 2008 and 2014 were derived from ASTER images. The result indicated that the Gidan Kaura hill grew by about 30 m within the six-year period. This agreed with an overwhelming belief by 95% of the respondents that the area continues to suffer from sandstorms and sand deposition. This was visibly confirmed by the presence of sand dunes in most of the areas visited in the three local government areas. Normalized Difference Vegetation Index derived from Landsat images of the year 2013 and 2018 gave further insight into the current vegetation conditions. Key words: Northern Nigeria, desertification, sustainability, geospatial analyses, environmental impacts.

LANDFIRE Remap Prototype Mapping Effort: Developing a New Framework for Mapping Vegetation Classification, Change, and Structure

LANDFIRE (LF) National (2001) was the original product suite of the LANDFIRE program, which included Existing Vegetation Cover (EVC), Height (EVH), and Type (EVT). Subsequent refinements after feedback from data users resulted in updated products, referred to as LF 2001, that now served as LANDFIRE’s baseline datasets and are the basis for all subsequent LANDFIRE updates. These updates account for disturbances and vegetation transition changes that may not represent current vegetation conditions. Therefore, in 2016 LANDFIRE initiated the Remap prototype to determine how to undertake a national-scale remap of the LANDFIRE primary vegetation datasets. EVC, EVH, and EVT were produced (circa 2015) via modeling for ecologically variable prototyping areas in the Pacific Northwest (NW) and Grand Canyon (GC). An error analysis within the GC suggested an overall accuracy of 52% (N = 800) for EVT, and a goodness of fit of 51% (N = 38) for percent cover (continuous EVC) and 53% (N = 38) for height (continuous EVH). The prototyping effort included a new 81-class map using the National Vegetation Classification (NVC) within the NW. This paper presents a narrative of the innovative methodologies in image processing and mapping used to create the new LANDFIRE vegetation products.

Spatial modeling and inventories for prioritizing investment into oak-hickory restoration

Oak (Quercus spp.) and hickory (Carya spp.) forests in the eastern United States provide a host of ecosystem services as their mast are prized by wildlife, the timber is a valued commodity, and they are generally more tolerant of extreme weather events under a changing climate. They are, however, undergoing a severe decline in prominence throughout the region, yielding to more mesic and shade-tolerant species, largely red maple (Acer rubrum). Two decades of research in Ohio have shown that silviculture and/or natural disturbances that reduce understory shade during seedling establishment and early growth, followed by canopy opening and competition management through prescribed fire and partial cutting, can encourage oak and hickory regeneration, most successfully on drier ridges and south- and southwest-facing slopes. We employed an ecological classification and mapping approach to prioritize areas across a 17-county region (∼22,000 km2) that may be more receptive, and thus more cost effective, to successful oak regeneration following silvicultural treatment. The ecomapping effort was comprised of two parts; a GIS model of the terrain, and a stand inventory of current vegetation condition coupled with the SILVAH decision-support system to recommend needed silvicultural treatments. The GIS model is based primarily on topography as vegetation patterns in the project area are largely driven by landscape position and soil moisture regimes. It uses transformed aspect, slope angle, topographic position index, and slope position as inputs to define six classes of landtype phases: ridge, southwest upper slopes, southwest lower slopes, northeast upper slopes, northeast lower slopes, and bottomland. The first three and following two classes, respectively, were hierarchically nested to form Dry Oak Forest and Dry-mesic Mixed Oak Hardwood Forest classes at the landtype level. Dry Oak Forests require the least silvicultural intervention to sustain or restore oak, while the other two landtypes normally require serious intervention to sustain oak into the future. To determine whether sufficient stocking is present for adequate regeneration, we use forest inventory data to represent current vegetation conditions including both overstory and understory stocking. Overall, these tools allow managers to identify ‘zones of investment’, i.e., those stands with the bulk of the area in the Dry Oak Forest landtype and with some level of advance oak regeneration, which will have a greater likelihood of growing into oak-dominated stands with minimal investment of scarce funding resources.

A Simulation Study to Estimate Effects of Wildfire and Forest Management on Hydrology and Sediment in a Forested Watershed, Northwestern U.S.

Abstract. Suitable fuel reduction treatments are needed in the Colville National Forest, Washington, to reduce the risk of severe wildfire. This study aimed to identify high-risk erosion hillslopes following wildfire to aid in forest fuel reduction planning and to evaluate the effects of fuel treatments on the watershed hydrological response. The specific objectives were (1) to assess the soil burn severity associated with wildfires and use that information to identify critical hillslopes for forest fuel treatments, and (2) to evaluate the potential changes in water yield and peak flows from pre-treatment (undisturbed forest) to post-treatment (thinning and prescribed burn) conditions, in the East Deer Creek Watershed (EDCW), a subwatershed of the Colville National Forest. Assessments were made using a modeling approach for hypothetical wildfire and fuel treatment scenarios. FlamMap, a fire behavior model, was used to predict the spatial distribution of wildfire intensity for a hypothetical event under current vegetation conditions. WEPP simulations were subsequently completed to obtain sediment and water yields based on fire intensity and topography. WEPP erosion estimations following a simulated wildfire showed hillslope sediment yield varying from 0 to 49.4 Mg ha-1 year-1 from the 777 hillslopes, which were ranked in descending order of sediment yield to identify critical hillslopes for fuel treatments. The WEPP model calibrated for a nearby gauged watershed was then applied to the EDCW for pre-treatment and post-treatment conditions. At the watershed scale, the increase in water yield from pre-treatment to post-treatment conditions ranged from 0.7% to 5.6% on hillslopes delivering 10% to 50% of the predicted post-fire sediment. Simulated water balance components at the treated hillslopes showed substantial changes. Surface runoff, subsurface lateral flow, and deep percolation increased 150% (5 mm), 50% (9 mm), and 40% (41 mm), respectively, whereas evapotranspiration (ET) decreased 23% (124 mm). The relative differences between pre- and post-harvest peak flows showed no clear trends as treatment area increased. The results suggest that thinning and prescribed burns to treated hillslopes in the EDCW may lead to an increase in water yield and significant alterations in hydrological processes. Keywords: Fuel treatments, Modeling, Peak flows, Sediment, Water yield, Wildfire.

CANOPY PROFILING FOR VEGETATION MAPPING IN SOUTH-WESTERN AUSTRALIAN FORESTED ECOSYSTEMS

Abstract. Anthropogenic climate change is already impacting native vegetation world-wide. Thus accurate mapping of current vegetation condition is necessary for benchmarking and conservation planning. We examine the potential for the mapping of native vegetation of forested ecosystems in south-western Australia using LiDAR data. Airborne LiDAR (distance between data points 1.2 m) and RGB imagery was acquired with a discrete 4-return Leica ALS 50-II system in April 2011 and vertical canopy profiles determined in Boyagin Nature Reserve. Elevation, slope and geomorphological descriptions of the terrain in combination with vertical canopy profiles based on presence/absence of returns within voxels were derived from the LiDAR data and processed at a spatial resolution of 4.0 meters. Based on these profiles, crown height and depth, ground cover, mean intensity of crown returns, presence of understory and number of vegetation layers were determined for each grid cell. Unsupervised classification revealed distinctive canopy profiles. Vegetation is strongly linked to geomorphology in this old landscape. Thus Kwongan shrubland occurs on the plateaus, Allocasuarina heugeliana woodland on the fringes of rock outcrops, Eucalyptus astringens and E. accedens woodland on breakaways and E. wandoo and Corymbia calophylla woodland in more fertile valley systems. The vegetation types present within distinctive spatial clusters were determined in two field visits. Vegetation types were mapped with an object-based image analysis approach at geomorphological, vegetation and tree scales using the geomorphology of the terrain and structural, textural and reflective characteristics of the canopy. All vertical profiles identified were present on each geomorphological unit. Tree species with a distinctive vertical profile such as Eucalyptus astringens and Allocasuarina heugeliana were defined and distinguished in combination with object-based geomorphological and spatial characteristics. Vegetation types were mapped accurately with a kappa coefficient of 0.80. We conclude that vertical profiles and geomorphology of the terrain derived from commercially available discrete return LiDAR systems provides a valuable means to benchmark the mapping of native vegetation in the forested ecosystems of south-western Australia.

ASSESSMENT OF THE VEGETATION COVER CONDITIONS FOR THE CENTRAL PART OF THE MURMANSK REGION BASED ON FIELD AND REMOTE SENSING DATA

Research of the forest ecosystems dynamics of northwestern Russia on the Kola Peninsula (the Imandra Lake watershed) under the influence of strong anthropogenic impacts caused by the industrial complex “Severonikel” over the last 70 years was carried out. Statistical analysis was used for comparison and interpolation of field data, multispectral remote sensing data (MRSD), and digital elevation model (DEM). From this analysis, the classification of natural and anthropogenic classes of the vegetation and land cover was developed; the model highlighted the key driving forces behind the spatial differentiation of vegetation (altitudinal climate gradients, anthropogenic disturbance, water supply, and development of the natural vegetation communities). In addition, the map of the current vegetation conditions at a scale of 1:100 000 was created. This map characterizes the large part of the Lapland Nature Reserve, the territory of the Khibiny mountains, as well as the polluted area near the metallurgical plant.

Using ecological forest site mapping for long-term habitat suitability assessments in wildlife conservation—Demonstrated for capercaillie ( Tetrao urogallus)

Although key factors for vegetation composition and structure, site and soil condition have received little attention as predictors of habitat suitability in wildlife ecology to date. Using the example of capercaillie ( Tetrao urogallus), an indicator species for open, well-structured forest habitats, we evaluated the potential use of ecological forest site mapping for the identification of areas where the preferred vegetation structures are supported by the prevailing soil conditions. These are sites that we, therefore, expected to be of long-term relevance to the species. Employing an expert model, we evaluated data from the state's forest site and soil condition database including soil texture, soil type, humus type, nutrient status and hydrological regime. The data were aggregated to a soil condition index ( I s), quantifying the potential to support capercaillie-relevant vegetation types. First, we assessed the correlation of I s with the current vegetation conditions in the study area. Subsequently, we evaluated the short-term (15 years) and long-term (100 years) changes to the capercaillie distribution with respect to I s at different spatial scales and calculated a threshold for the prediction of long-term capercaillie presence. Although the vegetation structure in the study area was largely influenced by forestry, I s was correlated with all investigated vegetation structure variables that determine habitat suitability for capercaillie. Soil condition proved to be an important indirect predictor of long-term capercaillie inhabitation, performing best when considering an area exceeding the size of a large capercaillie home range. The long-term range contractions corresponded significantly with the spatial pattern of suitable soil conditions, the short-term development demonstrated a similar trend. The results indicated an ongoing retreat from degenerating secondary habitats, created by historic land use techniques on sites with low I s, to sites with high I s values, which should be considered when defining priority areas for conservation. Forest ecological site mapping provides detailed, area-wide data that allow for a spatially explicit evaluation of the vegetation development. Therefore, it can facilitate the integration of conservation targets in forest management, for species dependent upon specific vegetation conditions.

Estimation of possible climate change impacts on water availability, extreme flow events and soil moisture in the Goulburn and Ovens Basins, Victoria

A rainfall-runoff model (IHACRES) is applied on a daily timestep to a large area of the state of Victoria, Australia. Successful calibrations of this dynamic lumped parameter model were performed for 5 rivers contributing streamflow to the Ovens Basin, and for 9 rivers of the Goulburn Basin. This is the first application of the model on such a scale, involving two basins where the total drainage area of the catchments modelled is about 6,500 km2. The models were tested by simulation over the entire common period of observation for the 14 catchments under consideration. The results show that the models closely simulate the observed streamflow. The effect of historical climate variability on streamflow was investigated. The models were used for estimation of the potential impact of climatic change on water availability for irrigation for different climate scenarios developed in the Division of Atmospheric Research, CSIRO. This allows conditional estimates to be made of water supply in these basins for the periods 2030 and 2070 under current vegetation conditions. Projecting the future hydrologic regime in this region is extremely important, in particular for supporting irrigation management of the Basin. The problem of estimating the impact of climate change on the probability of extreme events of the hydrological regime was analysed. Flood frequency was found to increase for the scenarios providing the maximum amount of water; to 50% at 2030 and 100% at 2070. The probability of flood events for the ‘dry’ scenarios rapidly decreases for these dates. Drought frequency, as defined by a soil wetness index, increased 35% for the ‘dry’ scenario at 2030 and 80% for this scenario at 2070.