2.2 Marco teórico
2.2.2 Proceso de extracción aceite de palma africana
Automated measurement of eroding bank volume from high-resolution aerial imagery and DEM analysis
Within Chapter 2, we described AIMM, a channel migration model that is designed to make full use of the high-resolution data found within aerial imagery, and compared its results with the results of one of the most prevalent techniques in the field, hand delineation.
For the three stream reaches of the South Fork Iowa River watershed included in (Tomer and Van Horn, 2018) we found that AIMM and the hand-delineation results agreed on 81% of all pixels classified, while the delineations agreed with each other 88% of the time. We also found that average Cohen’s Kappa score the comparison between AIMM and the
delineations was equal to 0.62, which indicates a high level of agreement, even when the random accuracy due to chance is taken into account. Finally, our estimation of net volume loss produced using AIMM was only 0.19%, or 323 m3 less than the estimate reported by Tomer and Horn (2018).
While we do believe that comparing AIMM to hand delineations is a satisfactory means by which to assess the utility of AIMM, it is important to note that agreement with delineations is not the same as accuracy. Both delineations and AIMM are prone to some sources of error, and these must be accounted when using either methods. We do believe however that the automated nature of AIMM, and the ease with which the AIMM results can be reproduced, and higher prevalence of random rather than systematic error make AIMM a competitive alternative to hand delineation.
Overall, we believe that AIMM is an effective model for determining volumes and areas of erosion and deposition from high-resolution aerial imagery. As the resolution of aerial imagery increases, models that combine accuracy with computational efficiency, such
as AIMM, will need to be used if the full extent and detail of the data within these images are to be utilized. Although random error is present in AIMM, increasing the area of analysis should allow positive and negative biases to balance each other. AIMM is a powerful tool, and we believe that it can be effectively implemented to derive bank erosion and deposition estimates of relatively small waterways on a statewide scale.
Estimation of sediment and phosphorus contribution of eroding banks within the Nishnabotna watershed
Within Chapter 3, the AIMM model was used in conjunction with a random sample of 18 soil cores, stratified by stream order, to estimate channel migration’s contribution to the sediment and P budget of the Nishnabotna River in SW Iowa. We found that between the years of 2009 and 2018 there was a net input of 8.7x105 tons of sediment and 4.1x105 kg of Phosphorus sourced from channel migration per year within the Nishnabotna watershed. This is also equal to 0.38 tons of sediment and 0.18 kg of total phosphorus per meter of channel length analyzed. These values fell within the range found in previous studies, even though the Nishnabotna watershed is orders of magnitude larger than the watersheds considered in previous studies.
As this is the first time the AIMM model has been used to predict sediment and total P contributions of eroding banks within a large watershed, we also suggest that this study represents an efficient method for constraining contributions from streambanks to watershed sediment and nutrient budgets. Finally, our volume analysis also indicated that the
proportional contribution to net volume loss by stream order is inversely related to the total channel length of each order within the watershed. These results indicate that large order reaches are of greater importance than previously thought, and that future conservation and
research that attempts to decrease riparian contributions to sediment and P budgets should focus on larger downstream reaches than are currently considered.
Management Implications
The excess sediment and nutrients flowing into Midwestern watersheds is a major issue that is damaging to the ecological health of our rivers, and to the essential infrastructure that we have placed within our floodplains. We are beginning to understand the role that stream and riverbank erosion play within the sediment and nutrient budgets of our
watersheds, but the lack of consistent and extensive bank erosion data has severely hindered our ability to account for streambank erosion within the Iowa nutrient reduction strategy.
With the creation of the AIMM model however, we believe that we have a consistent and objective methodology for quantifying these fluxes across the entire state. Within Chapter 2 we showed that AIMM has results comparable to the those of hand-delineation techniques, and in Chapter 3 we showed how it can be used to estimate the contributions of eroding banks to the sediment and P budgets of watersheds greater than 10000 km2 in area. Chapter 3’s results also indicate that large river channels are the main source of bank erosion within the Nishnabotna watershed. If this is also found to be true within the other major watersheds of Iowa, this would suggest that conservation efforts aimed at reducing bank erosion should focus on reducing bank erosion within larger channels. Caution should however be taken when equating the stabilization of banks and channels with a reduction of erosion since the effect that the stabilization of a reach has on its neighboring reaches is still poorly
understood. More research will need to be done in order to assess the effect of stabilization and other erosion reduction practices have on overall sediment budgets, but models such as AIMM may also provide an objective and efficient method for discerning the role that these practices play in reducing bank erosion.
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APPENDIX CONFUSION MATRICES
Table A.1 Confusion Matrices and agreement statistics for Beaver Creek
Beaver Creek
Table A.2 Confusion Matrices and agreement statistics for South Fork Iowa River
Table A.3 Confusion Matrices and agreement statistics for Tipton Creek