Assessing the effectiveness of protected areas is crucial for refining conservation policies, yet it remains challenging in remote, biodiversity-rich tropics due to logistical constraints. To address this challenge, we developed a remote sensing-based Pressure–State–Benefit (PSB) framework comprising four dimensions: human activity, ecosystem pattern, ecosystem quality, and ecosystem services. Using the Hainan Tropical Rainforest National Park (HTRNP) as a case study, we evaluated conservation effectiveness at the park scale by comparing ecological changes before (2015–2020) and after (2020–2025) its establishment. We further compared the Core Protection Zone (CPZ) and the General Control Zone (GCZ) to reveal differences in conservation outcomes under distinct management regimes. The establishment of HTRNP effectively curbed cropland and built-up land expansion. During 2020–2025, landscape patterns improved, characterized by enhanced connectivity and reduced fragmentation. Concurrently, fractional vegetation cover increased at an accelerated rate, indicating improved ecosystem quality, while key ecosystem services showed upward trends. Notably, the CPZ maintained superior ecological conditions with steady improvements under strict protection, whereas the GCZ, being more sensitive to human disturbances, exhibited fluctuating outcomes. This study demonstrates the effectiveness of HTRNP in enhancing regional ecosystem conditions and highlights the importance of zoned management. Furthermore, the proposed PSB framework offers a transferable approach for comprehensively assessing conservation effectiveness in other protected areas.

Protected Areas (PAs) are essential for global biodiversity conservation, yet assessing their management and conservation effectiveness remains a formidable challenge. While remote sensing (RS) enables synoptic, large-scale monitoring, existing reviews are often fragmented—either decoupled from PA governance contexts or confined to isolated threat mappings. To bridge this gap, this review proposes a dual-dimensional analytical framework integrating management effectiveness and conservation effectiveness. Based on the literature retrieved via a systematic Boolean search in the Web of Science, we first establish a multi-source RS technology stack covering space-borne optical, radar, nighttime-light, LiDAR and cloud-computing platforms. We then review RS applications within the two dimensions. For management effectiveness, we trace the progression from single-disturbance tracking to integrated pressure assessments using counterfactual matching designs. For conservation effectiveness, we synthesize advances in biodiversity dynamics, ecosystem pattern integrity, quality evaluation, and ecosystem service quantification. Our review demonstrates that RS has become a major component of large-scale PA evaluation, revealing measurable successes while highlighting the risk of overestimating conservation outcomes when relying solely on greenness-based proxies. Persistent challenges include the lack of standardized, biome-specific assessment frameworks and the disconnect between biophysical monitoring and socio-ecological drivers. Addressing these gaps requires unified evaluation systems aligned with the Kunming-Montreal Global Biodiversity Framework, AI-driven multi-sensor fusion, and the operationalization of RS into routine PA governance to support the “30 × 30” target and ecological product value realization.