## 1 Introduction
From our perspective of PAs as habitats for various species, the quality trends, whether improving or deteriorating, are an important reflection of the changing effectiveness of conservation efforts. We utilize an integrated approach of RS and GIS techniques to conduct monitoring of PA quality changes. This allows for more cost-effective and extensive spatio-temporal analyses compared to conventional methods based on localized ground surveys. Furthermore, our multi-factorial integrated assessment model employs a weighted summation method, which can effectively mitigate the limitations of single-factor evaluation lacking accuracy. This represents a methodological innovation in our research. These represent the innovations in our research ideology and analytical methodology. Thus far, many appraisals of conservation effectiveness have focused solely on either a single category of PA [21; 29] or involved a plethora of disparate PA categories [19; 30; 31; 32; 33; 34]. Therefore, our study aims to analyze differences in the effects of various PA types through a comparative analysis of two major types (forest and wetland PAs). By concentrating the analysis on these two predominant habitat types, we can gain deeper insights into the relative performance of PAs designated for forests versus wetlands in terms of conservation outcomes and effectiveness. Land use change is a pivotal factor driving global environmental change [35; 36]. It provides a direct perspective on the impact of human activities on surface ecosystems [37], effectively mirroring the ecosystem status of PAs [38]. Landscape pattern serves as a vital means to depict landscape structure [39]. Landscape indexes encapsulate essential information about landscape patterns, and can serve as macro-scale surrogate indicators to perceive and comprehend changes in species and their habitats [21]. Landscape indexes have been extensively employed to assess the effectiveness of PAs [40; 41; 42]. The spatial and temporal variation of the Normalized Difference Vegetation Index (NDVI) is one of the most cost-effective methods for assessing ecological effectiveness [34; 43; 44; 45]. The Google Earth Engine (GEE) Cloud Platform can swiftly and accurately process remote sensing images using its robust data calculation function. It can also obtain the best-quality images through minimum cloud amount screening for research [46; 47; 48], thereby yielding the results of time series changes of NDVI. Consequently, employing the aforementioned factors to evaluate the effectiveness of PAs in a more systematic manner holds significant guiding implications. Frequently, forests are often referred to as the \"lungs of the earth\", playing a pivotal role in the global carbon cycle, regulating the climate system, and protecting biodiversity [49; 50; 51; 52]. Wetlands, on the other hand, are commonly referred to as the \"kidney of the earth\", providing a habitat for a vast array of species [53], maintaining the ecological balance [54; 55; 56], and mitigating the impact of extreme weather and other outside destructive forces on surrounding areas [57]. Both ecosystems are critically important, yet are under severe threat [51; 53]. The establishment of forest ecosystem PAs and wetland ecosystem PAs is a key approach to protecting forests and wetlands, and a universally powerful tool to reverse the rapid imbalance and degradation of these two ecosystems [58]. Given disparities in the developmental status and geographic contexts of these two PA types, they may encounter differing degrees of threat and exhibit variations in biodiversity composition and structure [30]. Consequently, their responses and outcomes regarding conservation management could diverge as well. Therefore, to what extent do differences exist in the conservation effectiveness between these two types of PAs? What are the driving factors contributing to these differences?