scholarly journals Site productivity indices for native forests in southeast Queensland

2021 ◽  
Vol 932 (1) ◽  
pp. 012006
Author(s):  
V A Jay ◽  
M Neumann
Keyword(s):  
Remote Sensing ◽  
Growth And Yield ◽  
Site Quality ◽  
General Distribution ◽  
Biomass Growth ◽  
Site Productivity ◽  
Potential Productivity ◽  
Study Region ◽  
Productivity Indices ◽  
Southeast Queensland

Abstract Site productivity, or site quality, describes the potential biomass growth and yield of vegetation at a given location. Land managers have devised indices for site productivity using attributes related to plant yields or growth rates, and these have great utility when available spatially in maps. The main factors determining site productivity include climate, soil and terrain characteristics. Here we analysed four productivity indices (two based on remote sensing only, two based on modelling and algorithms using spatial datasets). The tested indices were available over a 150,000 km2 area of southeast Queensland Australia, a region dominated by Eucalyptus and Acacia species. We were interested in comparing the indices regarding underlying drivers, effects on vegetation types and the general distribution of site productivity across our study region. Our methods included histograms of spatial attribute intersection, and multivariate linear regression. Remote sensing has clear advantages in capturing current conditions, which potential productivity algorithms cannot depict. On the other hand, maps with productivity algorithms provide large-scale robust information on biomass growth/yield that is sensitive to the main drivers of plant growth (e.g. climate, soil).

Integrating remote sensing and climate data with process-based models to map forest productivity within west-central Alberta's boreal forest: Ecoleap-West

2006 ◽  
Vol 82 (2) ◽  
pp. 159-176 ◽  
Author(s):  
R J Hall ◽  
F. Raulier ◽  
D T Price ◽  
E. Arsenault ◽  
P Y Bernier ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Management Practices ◽  
Forest Productivity ◽  
Process Models ◽  
Growth And Yield ◽  
Area Index ◽  
Study Region

Forest yield forecasting typically employs statistically derived growth and yield (G&Y) functions that will yield biased growth estimates if changes in climate seriously influence future site conditions. Significant climate warming anticipated for the Prairie Provinces may result in increased moisture deficits, reductions in average site productivity and changes to natural species composition. Process-based stand growth models that respond realistically to simulated changes in climate can be used to assess the potential impacts of climate change on forest productivity, and hence can provide information for adapting forest management practices. We present an application of such a model, StandLEAP, to estimate stand-level net primary productivity (NPP) within a 2700 km2 study region in western Alberta. StandLEAP requires satellite remote-sensing derived estimates of canopy light absorption or leaf area index, in addition to spatial data on climate, topography and soil physical characteristics. The model was applied to some 80 000 stand-level inventory polygons across the study region. The resulting estimates of NPP correlate well with timber productivity values based on stand-level site index (height in metres at 50 years). This agreement demonstrates the potential to make site-based G&Y estimates using process models and to further investigate possible effects of climate change on future timber supply. Key words: forest productivity, NPP, climate change, process-based model, StandLEAP, leaf area index, above-ground biomass

scholarly journals Forest Potential Productivity Mapping by Linking Remote-Sensing-Derived Metrics to Site Variables

2020 ◽  
Vol 12 (12) ◽  
pp. 2056 ◽  
Author(s):  
Parinaz Rahimzadeh-Bajgiran ◽  
Chris Hennigar ◽  
Aaron Weiskittel ◽  
Sean Lamb
Keyword(s):  
Large Scale ◽  
Vegetation Indices ◽  
Growth Index ◽  
Important Variable ◽  
Forest Site ◽  
Biomass Growth ◽  
Site Productivity ◽  
Potential Productivity ◽  
Permanent Sample Plots ◽  
Sentinel 2

A fine-resolution region-wide map of forest site productivity is an essential need for effective large-scale forestry planning and management. In this study, we incorporated Sentinel-2 satellite data into an increment-based measure of forest productivity (biomass growth index (BGI)) derived from climate, lithology, soils, and topographic metrics to map improved BGI (iBGI) in parts of North American Acadian regions. Initially, several Sentinel-2 variables including nine single spectral bands and 12 spectral vegetation indices (SVIs) were used in combination with forest management variables to predict tree volume/ha and height using Random Forest. The results showed a 10–12 % increase in out of bag (OOB) r2 when Sentinel-2 variables were included in the prediction of both volume and height together with BGI. Later, selected Sentinel-2 variables were used for biomass growth prediction in Maine, USA and New Brunswick, Canada using data from 7738 provincial permanent sample plots. The Sentinel-2 red-edge position (S2REP) index was identified as the most important variable over others to have known influence on site productivity. While a slight improvement in the iBGI accuracy occurred compared to the base BGI model (~2%), substantial changes to coefficients of other variables were evident and some site variables became less important when S2REP was included.

scholarly journals A Dynamic Stand Growth Model System for Loblolly Pine Responding to Mid-Rotation Treatments

Forests ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 556
Author(s):  
Mauricio Zapata-Cuartas ◽  
Bronson P. Bullock ◽  
Cristian R. Montes ◽  
Michael B. Kane
Keyword(s):  
Growth Model ◽  
Loblolly Pine ◽  
Basal Area ◽  
Model System ◽  
Growth And Yield ◽  
Site Quality ◽  
Response To Treatment ◽  
Plantation Management ◽  
Vegetation Control ◽  
Dominant Height

Intensive loblolly pine (Pinus taeda L.) plantation management in the southeastern United States includes mid-rotation silvicultural practices (MRSP) like thinning, fertilization, competitive vegetation control, and their combinations. Consistent and well-designed long-term studies considering interactions of MRSP are required to produce accurate projections and evaluate management decisions. Here we use longitudinal data from the regional Mid-Rotation Treatment study established by the Plantation Management Research Cooperative (PMRC) at the University of Georgia across the southeast U.S. to fit and validate a new dynamic model system rooted in theoretical and biological principles. A Weibull pdf was used as a modifier function coupled with the basal area growth model. The growth model system and error projection functions were estimated simultaneously. The new formulation results in a compatible and consistent growth and yield system and provides temporal responses to treatment. The results indicated that the model projections reproduce the observed behavior of stand characteristics. The model has high predictive accuracy (the cross-validation variance explained was 96.2%, 99.7%, and 98.6%; and the prediction root mean square distance was 0.704 m, 19.1 trees ha−1, and 1.03 m2ha−1 for dominant height (DH), trees per hectare (N), and basal area (BA), respectively), and can be used to project the current stand attributes following combinations of MRSP and with different thinning intensities. Simulations across southern physiographic regions allow us to conclude that the most overall ranking of MRSP after thinning is fertilization + competitive vegetation control (Fert + CVC) > fertilization only (Fert) > competitive vegetation control only (CVC), and Fert + CVC show less than additive effect. Because of the model structure, the response to treatment changes with location, age of application, and dominant height growth as indicators of site quality. Therefore, the proposed model adequately represents regional growth conditions.

scholarly journals Phenological Analysis of Sub-Alpine Forest on Jeju Island, South Korea, Using Data Fusion of Landsat and MODIS Products

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 286
Author(s):  
Sang-Jin Park ◽  
Seung-Gyu Jeong ◽  
Yong Park ◽  
Sang-hyuk Kim ◽  
Dong-kun Lee ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Data Fusion ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Fusion Model ◽  
Fusion Algorithm ◽  
Study Region ◽  
Modis Imagery ◽  
Increasing Temperature

Climate change poses a disproportionate risk to alpine ecosystems. Effective monitoring of forest phenological responses to climate change is critical for predicting and managing threats to alpine populations. Remote sensing can be used to monitor forest communities in dynamic landscapes for responses to climate change at the species level. Spatiotemporal fusion technology using remote sensing images is an effective way of detecting gradual phenological changes over time and seasonal responses to climate change. The spatial and temporal adaptive reflectance fusion model (STARFM) is a widely used data fusion algorithm for Landsat and MODIS imagery. This study aims to identify forest phenological characteristics and changes at the species–community level by fusing spatiotemporal data from Landsat and MODIS imagery. We fused 18 images from March to November for 2000, 2010, and 2019. (The resulting STARFM-fused images exhibited accuracies of RMSE = 0.0402 and R2 = 0.795. We found that the normalized difference vegetation index (NDVI) value increased with time, which suggests that increasing temperature due to climate change has affected the start of the growth season in the study region. From this study, we found that increasing temperature affects the phenology of these regions, and forest management strategies like monitoring phenology using remote sensing technique should evaluate the effects of climate change.

Soil moisture and vegetation effects on the ASCAT backscatter-incidence angle dependence

2021 ◽  
Author(s):  
Isabella Pfeil ◽  
Wolfgang Wagner ◽  
Sebastian Hahn ◽  
Raphael Quast ◽  
Susan Steele-Dunne ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Radiative Transfer ◽  
Incidence Angle ◽  
Transfer Model ◽  
Study Region ◽  
Vegetation Effects ◽  
First Order ◽  
Vegetation Parameters ◽  
Low Incidence

<div> <p>Soil moisture (SM) datasets retrieved from the advanced scatterometer (ASCAT) sensor are well established and widely used for various hydro-meteorological, agricultural, and climate monitoring applications. Besides SM, ASCAT is sensitive to vegetation structure and vegetation water content, enabling the retrieval of vegetation optical depth (VOD; 1). The challenge in the retrieval of SM and vegetation products from ASCAT observations is to separate the two effects. As described by Wagner et al. (2), SM and vegetation affect the relation between backscatter and incidence angle differently.  At high incidence angles, the response from bare soil and thus the sensitivity to SM conditions is significantly weaker than at low incidence angles, leading to decreasing backscatter with increasing incidence angle. The presence of vegetation on the other hand decreases the backscatter dependence on the incidence angle. The dependence of backscatter on the incidence angle can be described by a second-order Taylor polynomial based on a slope and a curvature coefficient. It was found empirically that SM conditions have no significant effect on the steepness of the slope, and that therefore, SM and vegetation effects can be separated using the slope (2).  This is a major assumption in the TU Wien soil moisture retrieval algorithm used in several operational soil moisture products. However, recent findings by Quast et al. (3) using a first-order radiative transfer model for the inversion of soil and vegetation parameters from scatterometer observations indicate that SM may influence the slope, as the SM-induced backscatter increase is more pronounced at low incidence angles. </p> </div><div> <div> <p>The aim of this analysis is to revisit the assumption that SM does not affect the slope of the backscatter incidence angle relations by investigating if short-term variability, observed in ASCAT slope timeseries on top of the seasonal vegetation cycle, is caused by SM. We therefore compare timeseries and anomalies of the ASCAT slope to air temperature, rainfall and SM from the ERA5-Land dataset. We carry out the analysis in a humid continental climate (Austria) and a Mediterranean climate study region (Portugal). First results show significant negative correlations between slope and SM anomalies. However, correlations between temperature and slope anomalies are of a similar magnitude, albeit positive, which may reflect temperature-induced vegetation dynamics. The fact that temperature and SM are strongly correlated with each other complicates the interpretation of the results. Thus, our second approach is to investigate daily slope values and their change between dry and wet days. The results of this study shall help to quantify the uncertainties in ASCAT SM products caused by the potentially inadequate assumption of a SM-independent slope. </p> </div> <div> <p> </p> </div> <div> <p>(1) Vreugdenhil, Mariette, et al. "Analyzing the vegetation parameterization in the TU-Wien ASCAT soil moisture retrieval." IEEE Transactions on Geoscience and Remote Sensing 54.6 (2016): 3513-3531.</p> <p><span>(2) Wagner, Wolfgang, et al. "Monitoring soil moisture over the Canadian Prairies with the ERS scatterometer." IEEE Transactions on Geoscience and Remote Sensing 37.1 (1999): 206-216. </span></p> </div> <div> <p>(3) Quast, Raphael, et al. "A Generic First-Order Radiative Transfer Modelling Approach for the Inversion of Soil and Vegetation Parameters from Scatterometer Observations." Remote Sensing 11.3 (2019): 285.</p> </div> </div>

scholarly journals A Novel Approach to Modelling Stand-Level Growth of an Even-Aged Forest Using a Volume Productivity Index with Application to New Zealand-Grown Coast Redwood

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1155 ◽  
Author(s):  
Mark O. Kimberley ◽  
Michael S. Watt
Keyword(s):  
New Zealand ◽  
Growth Model ◽  
Growth Models ◽  
Stand Density ◽  
Site Index ◽  
Growth And Yield ◽  
Productivity Index ◽  
Site Quality ◽  
Spatial And Temporal Variation ◽  
Coast Redwood

Empirical growth models are widely used to predict the growth and yield of plantation tree species, and the precise estimation of site quality is an important component of these models. The most commonly used proxy for site quality in growth models is Site Index (SI), which describes the mean height of dominant trees at a specified base age. Although SI is widely used, considerable research shows significant site-dependent variation in height for a given volume, with this latter variable more closely reflecting actual site productivity. Using a national dataset, this study develops and describes a stand-level growth and yield model for even-aged New Zealand-grown coast redwood (Sequoia sempervirens). We used a novel modelling approach that quantifies site quality using SI and a volume-based index termed the 300 Index, defined as the volume mean annual increment at age 30 years for a reference regime of 300 stems ha−1. The growth model includes a number of interrelated components. Mean top height is modelled from age and SI using a polymorphic Korf function. A modified anamorphic Korf function is used to describe tree quadratic mean diameter (Dq) as a function of age, stand density, SI and a diameter site index. As the Dq model includes stand density in its formulation, it can predict tree growth for different stand densities and thinning regimes. The mortality model is based on a simple attritional equation improved through incorporation of the Reineke stand density index to account for competition-induced mortality. Using these components, the model precisely estimates stand-level volume. The developed model will be of considerable value to growers for yield projection and regime evaluation. By more robustly describing the site effect, the growth model provides researchers with an improved framework for quantifying and understanding the causes of spatial and temporal variation in plantation productivity.

scholarly journals Evapotranspiration Estimation Using Remote Sensing Technology Based on a SEBAL Model in the Upper Reaches of the Huaihe River Basin

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1599
Author(s):  
Linshan Tan ◽  
Kaiyuan Zheng ◽  
Qiangqiang Zhao ◽  
Yanjuan Wu
Keyword(s):  
Remote Sensing ◽  
Meteorological Data ◽  
Regional Scale ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Huaihe River ◽  
Infiltration Capacity ◽  
Mountain Area ◽  
Study Region ◽  
Sebal Model

Understanding the spatial and temporal variations of evapotranspiration (ET) is vital for water resources planning and management and drought monitoring. The development of a satellite remote sensing technique is described to provide insight into the estimation of ET at a regional scale. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) was used to calculate the actual ET on a daily scale from Landsat-8 data and daily ground-based meteorological data in the upper reaches of Huaihe River on 20 November 2013, 16 April 2015 and 23 March 2018. In order to evaluate the performance of the SEBAL model, the daily SEBAL ET (ETSEBAL) was compared against the daily reference ET (ET0) from four theoretical methods: the Penman-Monteith (P-M), Irmak-Allen (I-A), the Turc, and Jensen-Haise (J-H) method, the ETMOD16 product from the MODerate Resolution Imaging Spectrometer (MOD16) and the ETVIC from Variable Infiltration Capacity Model (VIC). A linear regression equation and statistical indices were used to model performance evaluation. The results showed that the daily ETSEBAL correlated very well with the ET0, ETMOD16, and ETVIC, and bias between the ETSEBAL with them was less than 1.5%. In general, the SEBAL model could provide good estimations in daily ET over the study region. In addition, the spatial-temporal distribution of ETSEBAL was explored. The variation of ETSEBAL was significant in seasons with high values during the growth period of vegetation in March and April and low values in November. Spatially, the daily ETSEBAL values in the mountain area were much higher than those in the plain areas over the study region. The variability of ETSEBAL in this study area was positively correlated with elevation and negatively correlated with surface reflectance, which implies that elevation and surface reflectance are the important factors for predicting ET in this study area.

scholarly journals Land and Forest Management by Land Use/ Land Cover Analysis and Change Detection Using Remote Sensing and GIS

2016 ◽  
Vol 9 (1) ◽  
pp. 63-77 ◽  
Author(s):  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Forest Management ◽  
Land Cover ◽  
Spatial Data ◽  
Management Strategies ◽  
Geographical Information ◽  
Spatial Data Analysis ◽  
Study Region ◽  
Rugged Terrain

Abstract Remote sensing and Geographical Information System (GIS) are the most effective tools in spatial data analysis. Natural resources like land, forest and water, these techniques have proved a valuable source of information generation as well as in the management and planning purposes. This study aims to suggest possible land and forest management strategies in Chakia tahsil based on land use and land cover analysis and the changing pattern observed during the last ten years. The population of Chakia tahsil is mainly rural in nature. The study has revealed that the northern part of the region, which offers for the settlement and all the agricultural practices constitutes nearly 23.48% and is a dead level plain, whereas the southern part, which constitute nearly 76.6% of the region is characterized by plateau and is covered with forest. The southern plateau rises abruptly from the northern alluvial plain with a number of escarpments. The contour line of 100 m mainly demarcates the boundary between plateau and plain. The plateau zone is deeply dissected and highly rugged terrain. The resultant topography comprises of a number of mesas and isolated hillocks showing elevation differences from 150 m to 385 m above mean sea level. Being rugged terrain in the southern part, nowadays human encroachment are taking place for more land for the cultivation. The changes were well observed in the land use and land cover in the study region. A large part of fallow land and open forest were converted into cultivated land.

Analysis of agronomic drought over North Africa using remote sensing satellite data

2021 ◽  
Author(s):  
Mehrez Zribi ◽  
Simon Nativel ◽  
Michel Le Page
Keyword(s):  
Remote Sensing ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
North Africa ◽  
Vegetation Cover ◽  
Remote Sensing Data ◽  
Study Region ◽  
Semi Arid Region ◽  
Anomaly Index

<p>This paper aims to analyze the agronomic drought in a highly anthropogenic  semi-arid region, North Africa. In the context of the Mediterranean climate, characterized by frequent droughts, North Africa is particularly affected. Indeed, in addition to this climatic aspect, it is one of the areas most affected by water scarcity in the world. Thus, understanding and describing agronomic drought is essential. The proposed study is based on remote sensing data from TERRA-MODIS and ASCAT satellite, describing the dynamics of vegetation cover and soil water content through NDVI and SWI indices. Two indices are analyzed, the Vegetation Anomaly Index (VAI) and the Moisture Anomaly Index (MAI). The dynamics of the VAI is analyzed for different types of regions (agircultural, forest areas). The contribution of vegetation cover is combined with the effect of soil water content through a new drought index combining the VAI and MAI. A discussion of this combination is proposed on different study areas in the study region. It illustrates the complementarity of these two informations in analysis of agronomic drought.</p>

scholarly journals Spatial variability of mean daily estimates of actual evaporation from remotely sensed imagery and surface reference data

2019 ◽  
Vol 23 (12) ◽  
pp. 4891-4907 ◽  
Author(s):  
Robert N. Armstrong ◽  
John W. Pomeroy ◽  
Lawrence W. Martz
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Land Surface ◽  
Management Practices ◽  
Reference Data ◽  
Meteorological Data ◽  
Net Radiation ◽  
Study Region ◽  
Evaporation Model ◽  
Actual Evaporation

Abstract. Land surface evaporation has considerable spatial variability that is not captured by point-scale estimates calculated from meteorological data alone. Knowing how evaporation varies spatially remains an important issue for improving parameterisations of land surface schemes and hydrological models and various land management practices. Satellite-based and aerial remote sensing has been crucial for capturing moderate- to larger-scale surface variables to indirectly estimate evaporative fluxes. However, more recent advances for field research via unmanned aerial vehicles (UAVs) now allow for the acquisition of more highly detailed surface data. Integrating models that can estimate “actual” evaporation from higher-resolution imagery and surface reference data would be valuable to better examine potential impacts of local variations in evaporation on upscaled estimates. This study introduces a novel approach for computing a normalised ratiometric index from surface variables that can be used to obtain more-realistic distributed estimates of actual evaporation. For demonstration purposes the Granger–Gray evaporation model (Granger and Gray, 1989) was applied at a rolling prairie agricultural site in central Saskatchewan, Canada. Visible and thermal images and meteorological reference data required to parameterise the model were obtained at midday. Ratiometric indexes were computed for the key surface variables albedo and net radiation at midday. This allowed point observations of albedo and mean daily net radiation to be scaled across high-resolution images over a large study region. Albedo and net radiation estimates were within 5 %–10 % of measured values. A daily evaporation estimate for a grassed surface was 0.5 mm (23 %) larger than eddy covariance measurements. Spatial variations in key factors driving evaporation and their impacts on upscaled evaporation estimates are also discussed. The methods applied have two key advantages for estimating evaporation over previous remote-sensing approaches: (1) detailed daily estimates of actual evaporation can be directly obtained using a physically based evaporation model, and (2) analysis of more-detailed and more-reliable evaporation estimates may lead to improved methods for upscaling evaporative fluxes to larger areas.

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