Mangrove forests provide wide ranges of roles supporting aquatic and terrestrial biota and providing ecosystem services to humans. Over the past century, mangroves have been impacted by human activities, leading to widespread habitat loss and degradation, while efforts to restorator and rehabilitate were not very successful in most projects. However, it will always be more effective to ensure the conservation of existing mangrove habitats. Accordingly, an adequate assessment method with appropriate evaluation components is required to monitor the habitat status. The current assessment approaches more often address the structural conditions of trees and mangrove ‘forests’ which are too insensitive to pressures. There are other indicator variables that describe processes and attributes that underpin mangroves’ structures and functions. These indicator variables are likely to be more sensitive to the impacts resulted by human activities. They can therefore be used to trigger a warning and management process when conditions inside the habitat start to go wrong. The approach in this thesis was to develop an assessment method which used quantitative indicators, as indicator-based approaches and measurable information interest the stakeholders who are managing and reporting habitat status more than purely descriptive assessments. This approach initially started from a literature review and was supplemented with the use of systematic expert judgments. During this process, the competence of a suite of potential indicator variables was obtained. This competency included the variables’ ability to reflect mangrove health and their capacity for delivering ecosystem services. I also considered other criteria such as ecosystem integrity, easy, fast and cost-effectiveness of measurement as well as the time scale for the indicators to respond the pressures impacts (Chapter 2). The indicator variables must be sensitive to anthropogenic pressures, which is another critical criterion to be considered. To address this, there was a need for a scale representing the degree of human pressure / influence. This was dealt by quantifying human activities as proxies of human pressures. They were measured in Pressure Groups (PG) including land-use, hydro-morphology alteration and water quality. This method was implemented in a case study of the Moreton Bay, South East Queensland, Australia. The results reported in Chapter 3 are dimensionless quantified pressure data providing an early detection of vulnerability in mangrove communities. The results can be also used for the calibration of assessment models in future studies concerning how biotic and abiotic indicators are reflecting the pressures. In Chapter 4, the sensitivities of the competent potential indicator variables (using the results of Chapter 2) were tested against the quantified human activities (using the results of Chapter 3). I provided a set of sites ranging from the least to the highest level of anthropogenic pressure in the Moreton Bay study area. The values of the potential variables to different levels of human activities assist the selection of sensitive mangrove health metrics. The habitat characteristics such as vegetation indices in canopy level, and sediment features i.e. metals, nutrients and Chl-a showed a degree of sensitivity to human activities and accordingly were identified as capable variables for an early warning process. Identifying the appropriate health metrics may lead to the development of an Index of Mangrove Ecosystem Integrity (IMEI); this is discussed in Chapter 5. The identified indicator/field variables may be amongst the most sensitive variables to human activities. This is because the field data collection was done in those mangrove forests which are still quite stable (see Chapter 4). A higher number of variables could be included in models by extending this field work to more degraded habitats and in areas with larger magnitude of human activities. Consequently, other field variables may be discovered with lower levels of sensitivity to human activities. These sets of variables can be used as higher levels of warning process. These health metrics will assist in developing IMEI.