Remote Sensing of Bush Encroachment on Commercial Cattle Farms in Semi-Arid Rangelands in Namibia

Bush encroachment is one of the most extensive changes in land cover in semi-arid rangelands and an urgent problem for cattle farming, rapidly reducing the productivity of the rangeland. Despite the severity of these consequences, a complete and accurate assessment of bush encroached areas is still missing at large. This study aims at assessing bush encroachment on commercial cattle farms in central Namibia by employing remote sensing methods to distinguish between areas covered by bush and open rangeland. The authors use different classification techniques and vegetation indices to characterize the nature of vegetation cover. Their analysis shows that results are sensitive to specific classifications of indices. As an accuracy assessment could not be run on these results the authors could not analyze which classification approximates real bush encroachment best. Hence, this study highlights the need for further analysis. Ground truth data, in the form of field mappings, high resolution aerial photographs or local expert knowledge are needed to gain further insights and produce reliable results.

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Agricultural Land Suitability Assessment Using Satellite Remote Sensing-Derived Soil-Vegetation Indices

Land ◽

10.3390/land10020223 ◽

2021 ◽

Vol 10 (2) ◽

pp. 223

Author(s):

Rubaiya Binte Mostafiz ◽

Ryozo Noguchi ◽

Tofael Ahamed

Keyword(s):

Remote Sensing ◽

Satellite Remote Sensing ◽

Agricultural Land ◽

Expert Knowledge ◽

Vegetation Indices ◽

Land Suitability ◽

Assessment System ◽

Yield Estimation ◽

Suitability Assessment ◽

Land Suitability Assessment

Satellite remote sensing technologies have a high potential in applications for evaluating land conditions and can facilitate optimized planning for agricultural sectors. However, misinformed land selection decisions limit crop yields and increase production-related costs to farmers. Therefore, the purpose of this research was to develop a land suitability assessment system using satellite remote sensing-derived soil-vegetation indicators. A multicriteria decision analysis was conducted by integrating weighted linear combinations and fuzzy multicriteria analyses in a GIS platform for suitability assessment using the following eight criteria: elevation, slope, and LST vegetation indices (SAVI, ARVI, SARVI, MSAVI, and OSAVI). The relative priorities of the indicators were identified using a fuzzy expert system. Furthermore, the results of the land suitability assessment were evaluated by ground truthed yield data. In addition, a yield estimation method was developed using indices representing influential factors. The analysis utilizing equal weights showed that 43% of the land (1832 km2) was highly suitable, 41% of the land (1747 km2) was moderately suitable, and 10% of the land (426 km2) was marginally suitable for improved yield productions. Alternatively, expert knowledge was also considered, along with references, when using the fuzzy membership function; as a result, 48% of the land (2045 km2) was identified as being highly suitable; 39% of the land (2045 km2) was identified as being moderately suitable, and 7% of the land (298 km2) was identified as being marginally suitable. Additionally, 6% (256 km2) of the land was described as not suitable by both methods. Moreover, the yield estimation using SAVI (R2 = 77.3%), ARVI (R2 = 68.9%), SARVI (R2 = 71.1%), MSAVI (R2 = 74.5%) and OSAVI (R2 = 81.2%) showed a good predictive ability. Furthermore, the combined model using these five indices reported the highest accuracy (R2 = 0.839); this model was then applied to develop yield prediction maps for the corresponding years (2017–2020). This research suggests that satellite remote sensing methods in GIS platforms are an effective and convenient way for agricultural land-use planners and land policy makers to select suitable cultivable land areas with potential for increased agricultural production.

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GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models

Applied Sciences ◽

10.3390/app10062039 ◽

2020 ◽

Vol 10 (6) ◽

pp. 2039 ◽

Cited By ~ 25

Author(s):

Viet-Ha Nhu ◽

Saeid Janizadeh ◽

Mohammadtaghi Avand ◽

Wei Chen ◽

Mohsen Farzin ◽

...

Keyword(s):

Expert Knowledge ◽

Characteristic Curve ◽

Absolute Error ◽

Hybrid Models ◽

Gully Erosion ◽

Susceptibility Mapping ◽

Aerial Photographs ◽

River Channels ◽

Grazing Land ◽

Semi Arid

Gully erosion destroys agricultural and domestic grazing land in many countries, especially those with arid and semi-arid climates and easily eroded rocks and soils. It also generates large amounts of sediment that can adversely impact downstream river channels. The main objective of this research is to accurately detect and predict areas prone to gully erosion. In this paper, we couple hybrid models of a commonly used base classifier (reduced pruning error tree, REPTree) with AdaBoost (AB), bagging (Bag), and random subspace (RS) algorithms to create gully erosion susceptibility maps for a sub-basin of the Shoor River watershed in northwestern Iran. We compare the performance of these models in terms of their ability to predict gully erosion and discuss their potential use in other arid and semi-arid areas. Our database comprises 242 gully erosion locations, which we randomly divided into training and testing sets with a ratio of 70/30. Based on expert knowledge and analysis of aerial photographs and satellite images, we selected 12 conditioning factors for gully erosion. We used multi-collinearity statistical techniques in the modeling process, and checked model performance using statistical indexes including precision, recall, F-measure, Matthew correlation coefficient (MCC), receiver operatic characteristic curve (ROC), precision–recall graph (PRC), Kappa, root mean square error (RMSE), relative absolute error (PRSE), mean absolute error (MAE), and relative absolute error (RAE). Results show that rainfall, elevation, and river density are the most important factors for gully erosion susceptibility mapping in the study area. All three hybrid models that we tested significantly enhanced and improved the predictive power of REPTree (AUC=0.800), but the RS-REPTree (AUC= 0.860) ensemble model outperformed the Bag-REPTree (AUC= 0.841) and the AB-REPTree (AUC= 0.805) models. We suggest that decision makers, planners, and environmental engineers employ the RS-REPTree hybrid model to better manage gully erosion-prone areas in Iran.

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