The main purpose of this paper is to assess and predict soil erosion and its dynamism observation at Kuala Lumpur metropolitan city using universal soil loss equation (USLE). To acquire USLE factors, multi-source datasets such as rainfall data of the past 20 years, DEM map, SPOT5 image and geological map of the study area were used to calculate spatial soil erosion of Kuala Lumpur metropolitan city. Results indicated that the highest spatial coverage (61.9%) occurred in very low and the lowest spatial coverage (3.8%) in severe soil loss class. Furthermore, a landslide inventory map of the same area was overlaid on the resultant soil erosion layer to demonstrate their correlation. A frequency ratio (FR) method was applied to determine the relationship between occurred landslides and each soil erosion class. The results indicated that the highest and the lowest FR equal to 0.8 and 2.1 and observed in very low and severe soil loss classes, respectively. Temporal assessment of soil erosion indicated that there is a decrement of soil loss content in severe class from 2000 to 2010. The result of the prognosis analysis showed that the spatial coverage of the study area in severe soil loss class equals to 48%. This result proved the significance of land use/land cover to curb soil erosion. The resultant maps may help the planners and managers to take proper decision for the conservation of soil as a noteworthy natural resource.
Basal stem rot (BSR) is a type of disease that induces oil palm death within a short span of the appearance of symptoms. BSR early detection would facilitate to curb this by adopting appropriate strategies. In this paper, a systematic review was undertaken to demonstrate the need for authentic health condition monitoring of oil palm plantations. The currently used remotely sensed (RS) techniques for BSR detection and classification were reviewed. Several kinds of RS techniques were exerted for BSR detection and its severity classification up to four levels. It was identified that applied geospatial technologies, including multispectral and hyperspectral remote sensing, terrestrial laser scanning, spatial maps, tomography images, intelligent e-nose and Microfocus X-ray fluorescence, were capable of distinguishing infected oil palms from the non-infected ones. Furthermore, some of them are able to categorize BSR severity level up to four levels as well as of its early detection.
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