Evaluating Protected Area Effectiveness: A Remote Sensing Review

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.

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Publication Details

Journal
Remote Sensing
Published
2026-10-28
DOI
https://doi.org/10.3390/rs18213710
Primary Topic
Conservation, Biodiversity, and Resource Management
Type
article
Field-Weighted Citation Impact
0.00

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article

Evaluating Protected Area Effectiveness: A Remote Sensing Review

Liu Zhihua, Qin Ye, Zhilong Zhao, Shicheng Li et al.
Remote Sensing
Conservation, Biodiversity, and Resource Management
article

Evaluating Protected Area Effectiveness: A Remote Sensing Review

Liu Zhihua, Qin Ye, Zhilong Zhao, Shicheng Li, Mingjie Song, Huimin Zhu, Binjie Liu, Meijiao Li
article en

Abstract

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.

Remote SensingVol. 18(21)
Shanxi University of Finance and Economics (CN), Chinese Academy of Sciences (CN), Ministry of Natural Resources (CN), China University of Geosciences (CN), Central China Normal University (CN), Institute of Geographic Sciences and Natural Resources Research (CN), Enshi Polytechnic (CN), Hebei Normal University (CN)
National Natural Science Foundation of China
Life on land
Openalex Percentile: Top 17%
Conservation, Biodiversity, and Resource Management
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