The mangrove forest ecosystem acts as a shield against the destructive tidal waves, preventing the coastal areas and other properties nearby from severe damages; this protective function certainly deserves attention from researchers to undertake further investigation and exploration. Mangrove forest provides different goods and services. The unique environmental factors affecting the growth of mangrove forest are as follows: distance from the sea or the estuary bank, frequency and duration of tidal inundation, salinity, and composition of the soil. These crucial factors may under certain circumstances turn into obstacles in accessing and managing the mangrove forest. One effective method to circumvent this shortcoming is by using remotely sensed imagery data, which offers a more accurate way of measuring the ecosystem and a more efficient tool of managing the mangrove forest. This paper attempts to review and discuss the usage of remotely sensed imagery data in mangrove forest management, and how they will improve the accuracy and precision in measuring the mangrove forest ecosystem. All types of measurements related to the mangrove forest ecosystem, such as detection of land cover changes, species distribution mapping and disaster observation should take advantage of the advanced technology; for example, adopting the digital image processing algorithm coupled with high-resolution image available nowadays. Thus, remote sensing is a highly efficient, low-cost and time-saving technique for mangrove forest measurement. The application of this technique will further add value to the mangrove forest and enhance its in-situ conservation and protection programmes in combating the effects of the rising sea level due to climate change.
Rainfall-runoff information is critical for water resource and river basin management. Runoff can be estimated by using two methods; gauged method (direct measurement) and ungauged method (indirect formula and equation). The in-situ measurement provides real-time and accurate yet required time-consuming operation and inaccessibility topography. Therefore, the runoff estimation modelling and equation was developed to overcome the limitation of in-situ measurement. SCS-CN is a simple model of ungauged method, where runoff volume (Q) resulting from rainfall (P) is formulated using equation of (Q= (P-Ia) 2 / (P-Ia + S). It was known as the best technique to be adopted for large basin study where time and manpower also accessibility are limited. SCS-CN method also is widely use in prediction software as it taken into consideration of the effects of soil, properties, land cover and antecedent moisture. Curve Number is well developed in USA for the agriculture purpose with many investigations to validate and calibrate the values of curve number. It was applied in numerous river basins in temperate and other regions e.g. US, Argentina, India, China, South Korea, Palestine and Malaysia. However, the reliability of the CN in the tropics is doubtable due to different land use characteristics, soil type, climate, geological features and rainfall pattern and variability. Based on the reviewed conceptual and applications of SCS-CN in temperate and tropics, numerous studies found the SCS-CN method is reliable and practical for runoff estimation in tropics region.