These results provide valuable spatially explicit information about the ecological performance of the reforested urban areas. Where the work of others has been used, this is duly acknowledged in the text.
BACKGROUND
However, these attributes have been identified as limited indicators of the ecological performance of tree stands in landscapes (McElhinny et al. 2005). However, its spectral and spatial data characteristics remain a limitation in distinguishing finer-scale variations in tree stand attributes (Frampton et al. 2013).
AIM AND OBJECTIVES
The PLS technique compresses explanatory information derived from the predictor variables (i.e. S-2 data and topographic variables) into a few uncorrelated latent components that have maximum covariance with the response variable (i.e. SSC) (Maestre 2004, Carrascal et al. 2009). The PLS technique is particularly attractive for its ability to minimize non-explanatory noise, identify relevant predictor variables and is applicable in studies with small sample sizes (Haenlein and Kaplan 2004, Chin and Newsted 1999).
CONTRIBUTION AND SIGNIFICANCE OF THIS RESEARCH
DESCRIPTION OF STUDY AREA
THESIS OUTLINE
Chapter 5 discusses the main purpose, objectives, limitations and key findings of the entire study. Assessing the usefulness of topographic variables in predicting the structural complexity of tree stands in a reforested urban landscape.
INTRODUCTION
2001) established a link between SSC and local insect diversity, while McKenny et al. 2006) noted that SSC was useful for monitoring the effect of different forest management practices on eastern red-backed salamander populations in hardwood forests. These include the Structural Complexity Index, which uses characteristics of ground vegetation, shrubs, tree trunks and litter (Barnett et al. 1978), the Stand Diversity Index which uses variations in species richness, tree spacing, diameter at breast height (DBH) and crown size (Neumann and Starlinger 2001), the Structure Index based on covariance in height and DBH (Staudhammer and LeMay 2001) and the Structural Complexity Index (Holdridge 1967) based on canopy height, stem diameter, basal area and species richness.
METHODS AND MATERIALS
- Sampling Plots
- Data for Stand Structural Complexity
- Predictor Data
- Statistical analysis
- Predictive Model
- Ranking of predictor variable importance
- Assessment of Prediction Accuracy
The SEM techniques allow for the construction of latent variables as a function of the predictor variables. The PLS technique is informed by the variance in the response variable as a function of the predictor variables. Choosing the optimal number of latent variables is a critical step in the optimization of the PLS model (Mehmood et al. 2012).
Where VIPk is the importance of the kth topographic variable based on a PLS model with 𝑎 latent variables, K is the total number of topographic variables, 𝑤𝑎𝑘 is the corresponding loading. This is the accuracy of the final chosen PLS model from the cross-validation process. The smaller the NRMSE of cross-validation, the stronger the predictive power of the PLS model.
RESULTS
Relationships between structural complexity and topographic variables
Modelling stand structural complexity
DISCUSSION AND CONCLUSIONS
Discussion
According to Webb et al. 1999), slope gradient represents the level of relative disturbance in a landscape. In the southern hemisphere, north/east-facing slopes are often characterized by higher SSC than the south/west-facing slopes (Yirdaw et al. However, contrary to a number of studies (Homeier et al. 2010, Clark and Clark 2000, Joseph et al. 2012) , this study found a weak association between height and SSC.
29 Trunk age is known to significantly affect tree size (Boninsegna et al. 1989, Burley, Phillips, and Ooi 2007), however in this study, trunk age showed a weak positive correlation with SSC. As noted by Balvanera et al. 2002), the spatial extent of an area strongly determines the impact of bio-physical factors on SSC. In accordance with Gallardo-Cruz et al. 2009), this study proved that different topographic variables, characterized by different biophysical processes, have different influences on SSC.
Conclusions
For example, within the establishment and development years of replanting, species richness changes can be dramatically affected by tree mortality (Van Mantgem et al. 2009, Lutz and Halpern 2006), which can affect SSC. In a localized landscape, the present study has shown that topographic variables such as TWI and slope are strong determinants of SSC. Therefore, in this study, a combination of different topographic variables was useful for predicting SSC in the revegetated urban landscape.
In this study, the PLS technique and topographic datasets were useful in determining the SSC of a reforested landscape. Such determination is valuable in managing the urban environment and mitigating climate change, biodiversity loss and related impacts. Determining the structural complexity of tree stands using remotely sensed data and integrated topographic features in a reforested urban landscape.
INTRODUCTION
The emergence of remote sensing (RS) approaches provides spatially explicit, repeatable and quantitatively consistent ways to monitor the ecological performance of tree stands (Peerbhay et al. 2013, Wunderle et al. 2007). For example, the S2REP is an S-2-based VI that is sensitive to variation in leaf chlorophyll content, thus valuable in vegetation analysis (Frampton et al. 2013). Consequently, some studies have suggested the use of supplementary environmental variables such as soil fertility (Wolf et al. 2011), altitude (Gallardo-Cruz et al. 2009), soil moisture (Fries et al. 2009) and topography (Kuebler et al. 2016 ) to compensate for these limitations.
For example, the steepness of slopes determines soil erosion and deposition (Webb et al. 1999, Vorpahl et al. 2012), while the Topographic Wetness Index (TWI) represents the relative distribution of soil surface moisture based on terrain area. The Area Solar Radiation (ASR) represents the variation in solar exposure due to slope direction, which affects surface temperature (Fries et al. 2009) and microprecipitation (Rollenbeck 2006). On the other hand, elevation has been found to be correlated with soil moisture (Wilcke et al. 2011) and soil nutrient pooling (Oliveira-Filho et al. 2001). Previously, good quality DEMs, for deriving fine-scale topographic features, were not readily available.
METHODS AND MATERIALS
- Sampling plots
- Stand Structural Complexity data
- S-2 Imagery
- Topographic data
- Predictive Model
- Variable Importance in the Projection
- Prediction Accuracy
To determine the tree diameter, the tree diameter at ankle height (DAH) was used, as recommended in the literature (Van Leeuwen and Nieuwenhuis 2010, Maltamo et al. 2009, Pommerening 2002, Wolter et al. 2009). Where TWI is the topographic wetness index, FA is the flow accumulation and S is the slope. The main advantages of PLS are its relative ease of use and its ability to suppress multicollinearity in data and identify relevant predictor variables from numerous predictor variables, with their estimated magnitude of interest for the response variable (Wolter et al. 2009).
The VIP calculates the importance score of each predictor variable in explaining the SSC, which are then used to rank the predictive power of each predictor variable as expressed by equation 3.3 (Wold et al. 2001). Where VIPk represents the importance of the kth predictor variable based on a PLS model with latent components 𝑎, K is the total number of predictor variables, 𝑤𝑎𝑘 is the corresponding loading weight of the kth predictor variable on 𝑎𝑡ℎ, the latent component, and wa are the column vectors. Where RMSE is the root mean square error, N is the total number of comparisons of the predicted with the observed complexity index value, p is the predicted complexity index value, O is the observed complexity index value, NRMSECV is Normalized RMSE of Cross-Validation, 𝑥̅ is the mean Value of structural complexity of the observed stand.
RESULTS
Relationship between structural complexity with vegetation indices and
Where RMSE is the Root Mean Square Error, N is the total number in predicted to observed complexity index value comparisons, p is predicted complexity index value. O is the observed complexity index value, NRMSECV is the normalized RMSE of cross-validation, 𝑥̅ is the mean observed state structural complexity value.
Predicting stand structural complexity based on topographic variables and S-2-
DISCUSSION AND CONCLUSIONS
Discussion
The PLS technique, on the other hand, offered the possibility of exploiting these fine vegetative variations in the data by reducing the complex and interconnected S-2 VI data into interpretable SSC components while removing noise, which increased covariance with S-2 VI. (Wolter et al. 2009). The red edge band, a recent addition to VI, is known to be sensitive to steep changes in absorption and reflectance between the red spectral region and the near-infrared spectral region (Li et al. 2014). This can be attributed to the higher spatial resolution of the topographic data (2 m) compared to the S-2 data (10 m and 20 m).
TWI is determined by soil moisture's downward gravitational movement, which creates a downward gradient in both soil and nutrients (Homeier et al. 2010, Paoli and Curran 2007). While slope represents the level of relative disturbance within a landscape (Yirdaw et al. 2015, Takyu et al. 2002). The improved prediction accuracy of the combined S-2/topographic variables model is a result of the aforementioned explanatory power of the PLS technique and topographic information.
Conclusions
44 tended to falsely exaggerate the differences in SSC across the landscapes, which is not useful for accurately comparing spatial variation in SSC. This result supports the argument that topographic features can be valuable not only to predict SSC, but also to further improve the predictive power of remotely sensed data. The current study's use of the stand structural complexity index (SSCI) by Holdridge (1967) as an indicator of ecological performance makes it ideal for application in other study areas, as its input datasets are traditionally captured within forestry stocks.
Additionally, multiple studies have reliably used multispectral remotely sensed data to predict these inputs ( Wolter et al. 2008 , Yu et al. 2006 , Hudak et al. 2006 , Franco-Lopez et al. 2001). Overall, the present study has shown that the integration of S-2 data and topographic variables can be a viable alternative of SSC prediction, and useful for easily and inexpensively informing monitoring and evaluation systems of reforestation programs. .
INTRODUCTION
REVIEWING OF AIMS AND OBJECTIVES
The first aim and objectives
The second aim and objectives
LIMITATIONS & RECOMMENDATIONS
The weak Pearson correlations between the S-2 VI and the SSCI call for future studies to explore the relationship of the S-2 VI with other SSC indices. Due to the wide spectrum of SSCIs available, with their different combinations of vegetative attributes, these correlations can vary widely depending on the SSC index used. The performance of other regression techniques compared to this PLS technique should also be pursued in future studies.
CONCLUDING REMARKS
Ruijten (2008) A topography-based model of forest cover at the alpine treeline in the tropical Andes. Murthy (2012) Precipitation and elevation affect local-scale tree community distribution in the South. Huang (2014) Effect of soil fertility and topographic factors on woody plant communities in the karst mountains of Southwest Guangxi, China.
ISPRS Journal of Photogrammetry and Remote Sensing Precipitation Variability in the Reserva Biólogica San Francisco / Southern Ecuador. Balslev (2004) Tree species distribution and local habitat variation in the Amazon: large forest patch in eastern Ecuador. Yimer (2015) Influence of topographical aspect on the floristic diversity, structure and treeline of afromontaneous cloud forests in the Bale Mountains, Ethiopia.
