IKONOS Images Research Articles

ANALISIS SPASIAL PERUBAHAN TUTUPAN LAHAN DI DAS WAE BATUGANTONG, KOTA AMBON

The development of developed land over the last 15 years in Ambon City has affected land cover changes in the Wae Batutangan watershed. This is a result of higher population growth and the need for residential land, which is increasing every year. This study aimed to analyze land cover changes in the Wae Batu Hang watershed in 2012, 2017, and 2022. The method used in this study was a spatial comparison of land cover changes in 2012 and 2017, as well as in 2017 and 2022. Land cover data is obtained from the results of this study. Visual interpretation and digitization where land cover in 2012 was obtained from IKONOS images, and land cover in 2017 and 2022 were obtained from SPOT 6 images. Results of this study showed that the cover of built-up land and open land continues to increase in the area, along with population growth and the high demand for land in watersheds.

Bathymetry from satellite images: a proposal for adapting the band ratio approach to IKONOS data

The acquisition of bathymetric data in shallower waters is difficult to attain using traditional survey methods because the areas to investigate may not be accessible to hydrographic vessels, due to the risk of grounding. For this reason, the use of satellite detection of depth data (satellite-derived bathymetry, SDB) constitutes a particularly useful and also economically advantageous alternative. In fact, this approach based on analytical modelling of light penetration through the water column in different multispectral bands allows to cover a big area against relatively low investment in time and resources. Particularly, the empirical method named band ratio method (BRM) is based on the degrees of absorption at different bands. The accuracy of the SDB is not comparable with that of traditional surveys, but we can certainly improve it by choosing satellite images with high geometric resolution. This article aims to investigate BRM applied to high geometric resolution images, IKONOS-2, concerning the Bay of Pozzuoli (Italy), and improve the accuracy of results performing the determination of the relation between band ratio and depth. Two non-linear functions such as the exponential function and the 3rd degree polynomial (3DP) are proposed, instead of regression line, to approximate the relationship between the values of the reflectance ratios and the true depth values collected in measured points. Those are derived from an Electronic Navigational Chart produced by the Italian Hydrographic Office. The results demonstrate that the adopted approach allows to enhance the accuracy of the SDB, specifically, 3DP supplies the most performing bathymetric model derived by multispectral IKONOS-2 images.

Grand Design for Merapi Irrigation Channel System Using Watershed and River Region Approaches based on Community Development in Yogyakarta, Indonesia

This paper proposes a grand design for Merapi Irrigation Channel, an irrigation channel system spanning from Turi to Cangkringan District in Yogyakarta, Indonesia. This design is based on Geographic Information System research to assess the feasibility of the system from two aspects, namely geophysics using site selection approaches (watershed and river region) and community aspirations, analyze potential water resource availability to support its sustainability, and construct a grand design. Primary data acquired from IKONOS image interpretation were used to collect geophysical and ?eld data for parameter mapping, and spatial data were modeled using Geographic Information System technology to determine paths, directions, and routes. Community aspirations were obtained by structured interviews with key informants and focus group discussions with community groups in village units. Results showed that the land carrying capacity geophysically allowed Krasak–Bedog Rivers as the permanent discharge of 123.78 m3/s of water, which is consistent with community aspirations for high irrigation water discharge in traversed districts. Overall, 19 rivers and 9 arti?cial retentions in the channel system respectively produced maximum discharges of 123.78 and 12 m3/s. The grand design included the construction of a 41 km long Merapi Channel from Turi (speci?cally Wonokerto Village) at 500 masl to Cangkringan (Glagaharjo) at 450 masl.

A comparative study for the assessment of Ikonos satellite image-fusion techniques

I<span>Image-fusion provide users with detailed information about the urban and rural environment, which is useful for applications such as urban planning and management when higher spatial resolution images are not available. There are different image fusion methods. This paper implements, evaluates, and compares six satellite image-fusion methods, namely wavelet 2D-M transform, gram schmidt, high-frequency modulation, high pass filter (HPF) transform, simple mean value, and PCA. An Ikonos image (Panchromatic-PAN and multispectral-MULTI) showing the northwest of Bogotá (Colombia) is used to generate six fused images</span>: MULTI<sub>Wavelet 2D-M</sub>, MULTI<sub>G-S</sub>, MULTI<sub>MHF</sub>, MULTI<sub>HPF</sub>, MULTI<sub>SMV</sub>, and MULTI<sub>PCA</sub>. <span>In order to assess the efficiency of the six image-fusion methods, the resulting images were evaluated in terms of both spatial quality and spectral quality. To this end, four metrics were applied, namely the correlation index, erreur relative globale adimensionnelle de synthese (ERGAS), relative average spectral error (RASE) and the Q index. The best results were obtained for the </span> MULTI<sub>SMV</sub> image, which exhibited spectral correlation higher than 0.85, a Q index of 0.84, and the highest scores in spectral assessment according to ERGAS and RASE, 4.36% and 17.39% respectively.

Assessment and Monitoring of Salinity of Soils After Tsunami in Nagapattinam Area Using Fuzzy Logic-Based Classification

AbstractThe research attempts to know the salinity level and how much agricultural land was affected by the 2004 Indian Ocean tsunami in the study area. Nagapattinam province of Tamilnadu, which was strongly hit by the 2004 Indian Ocean tsunami, is selected as the demonstration site. IKONOS and QuickBird image for the periods 2003 (pre-tsunami, IKONOS), 2004 (immediate tsunami, IKONOS) and 2006 (post-tsunami, QuickBird) have been used in this study. The purpose of this research is to detect the effects of the tsunami in the study area. Multispectral images were obtained in order to detect the salt-affected soils and compare the data with images after the tsunami. The near-infrared reflectance spectra contain significant information related to soil components. The development of remote sensing techniques can support the monitoring and efficient mapping of salinity level in soil for environmental assessment. In this research, we have implemented the techniques in four levels: image preprocessing, extracting land region, feature extraction and image classification. Image preprocessing is the first step in the image processing chain and is usually necessary prior to image classification and analysis. In the second level, land regions are extracted from the study area using the Soil-Adjusted Vegetation Indices. In the feature extraction level, we have extracted 15 salinity features from the image data and performed the feature selection method to select the best five features of soil. In the classification stage, the salt-affected land region is classified into three classes (C1, highly saline; C2, saline; and C3, non-saline) using the fuzzy logic method. In the immediate tsunami and after-tsunami data, 95.15- and 121.4-hectare area, respectively, were detected as a salt-affected area.

High-Resolution Satellite Stereo Matching by Object-Based Semiglobal Matching and Iterative Guided Edge-Preserving Filter

This letter presents an object-based stereo matching and content-based guided disparity map refinement for high-resolution satellite stereo images. The proposed method uses the superpixels for object-based cost filtering and then object-based semiglobal matching. In the proposed object-based stereo matching strategy, two improvements including homogeneity weight for cost filtering, and weighted cost aggregation by image objects are considered. After cost aggregation, the generated disparity map is computed using the winner takes all and then refined with a new iterative guided edge-preserving filter. The proposed method with Census cost function has been implemented on high-resolution satellite stereo images and then is compared with the LiDAR ground truth. Moreover, the proposed method is compared with five state-of-the-art stereo matching methods. The experimental results on along-track satellite stereo images from Pleiades and IKONOS images and cross-track multidate images from WV-III demonstrate that the proposed method significantly improves the result of stereo matching.

An Adaptive Change Threshold Selection Method Based on Land Cover Posterior Probability and Spatial Neighborhood Information

Change threshold selection (CTS) plays an important role in land cover change detection. The traditional CTS methods are mainly proposed by using the information contained in grayscale histogram distributions or pixel neighborhoods. However, land cover is highly spatially heterogeneous, and changes in different land cover types are characterized by different magnitudes. Unfortunately, few CTS studies have considered the effects of both land cover type and spatial heterogeneity on CTS, potentially leading to false alarms or missed alarms. To address this challenge, we propose an adaptive CTS method based on land cover posterior probability and spatial neighborhood information (LCSN). First, the posterior probability of the change magnitude in each land cover type is calculated according to a Bayesian criterion to integrate the land cover type information. Second, the posterior probability is calculated using a bilateral filtering method to construct the spatial surface based on the land cover type and spatial neighborhood information. Finally, the degree of difference between the spatial surface and the change magnitude map is taken as the final threshold. The proposed LCSN method is verified with Landsat 8-Operational Land Imager (OLI) images and IKONOS images. The experimental results show that the LCSN method is effective in reducing the pseudo changes and identifying changes in land cover types with low grayscale values in the corresponding change magnitude maps.

An Improved Approach Based on Terrain-dependent Mathematical Models for Georeferencing Pushbroom Satellite Images

Recently, linear features in remotely sensed imagery have gained much attention because of their unique characteristics compared to other control features. For georeferencing high-resolution satellite images, the observations in the mathematical equations (slope and y-intercept) of the corresponding control lines in the two spaces are considered the same based on recent studies. However, the use of such assumptions causes error and reduces the accuracy of registration. The aim of this article is to present a methodology based on a quasi-observation assumption in the mathematical equations in the process of georeferencing. Experimental results for IKONOS and GeoEye images over two different cities of Iran indicate that the quasi-observation assumption can increase the average registration accuracy up to 48.96% and 24.77% using 3D-affine and rational function models, respectively. This improvement in accuracy increases the processing time by 31.48% over traditional approaches; however, the proposed methodology can be regarded as an efficient solution.

Mapping Different Types of Shorelines from Coarse-Resolution Imagery: Fuzzy Classification Method Can Deliver Greater Accuracy

Hossain, M.S.; Muslim, A.M.; Pour, A.B.; Mohamad, M.N.; Alam, S.M.R.; Nadzri, M.I., and Khalil, I., 2021. Mapping different types of shorelines from coarse-resolution imagery: Fuzzy classification method can deliver greater accuracy. Journal of Coastal Research, 37(2), 433–441. Coconut Creek (Florida), ISSN 0749-0208.Coastal zones are among the most structurally complex ecosystems, though their complex shorelines are threatened because of both natural and anthropocentric influences. There are a smaller number of studies that dealt with developing remote-sensing techniques for detecting and mapping different shoreline types (ST) using coarse-resolution imagery. This study examined fuzzy c-means (FCM), wavelet interpolation, and fuzzy maximum likelihood to map shorelines over Seberang Takir (Malaysia) for different STs. These three fuzzy classification methods were applied to simulated IKONOS (with 16- and 32-m spatial resolutions) image covering the four STs. The positional accuracy of shorelines was assessed in terms of root mean square error (RMSE). The visual inspection and RMSE values showed that variations in accuracies were evident, predominantly due to differences in STs; fuzzy algorithm improved the accuracy. FCM can predict shoreline positions with greater accuracy than the other two methods.

Contributions of multispectral images to the study of land cover in wet depressions of eastern Tunisia

Eastern Tunisia is dotted with varied wetlands such as lagoons, sabkhas, garaas… These wetlands, at the interface between land and water, are rich, diverse and dynamic environments. Remote sensing shows potential and efficiency in the detection and characterization of these environments, although these humid depressions are difficult to define. The implementation of new optical satellite sensors characterized by High and Very High Spatial Resolution (HSR and VHSR) and by a high temporal repetitiveness, completes the field data and allows to envisage a delineation of the wet depression and a detailed detection of its land use. The purpose of this article is to determine whether the VHSR image, with its very rich spatial resolution, offers an added value for the detection of these diversified depressions, in comparison with the HSR image with richer spectral resolution. The results of the analysis show that the Ikonos image at VHSR compensates for the spectral richness of Sentinel-2 images at HSR. The “object-oriented” classification method, widely used today in image processing for various applications, appears to be more suitable than the “pixel-based” method in VHSR image classification. The Normalized Difference Water Index (NDWI) and the Transformed Soil Adjusted Vegetation Index (TSAVI) have multiple interests in wetland detection, although moisture masks show some inefficiency in improving the quality of classifications.

Classificação supervisionada das áreas de influência da mata ciliar nas barragens Goitá e Tapacurá mediante Spring

Na atualidade, as matas ciliares vêm sendo degradadas pelas atividades antrópicas, contribuindo ao assoreamento, contaminação e degradação do meio ambiente. A lei Nº 12.651 dispõe a existência de uma área mínima de preservação entorno aos corpos de água, sendo necessário o monitoramento espacial e temporal. Este monitoramento pode ser feito mediante múltiplas aplicações SIG (Sistemas de Informação Geográfica) disponíveis para avaliar os câmbios na cobertura, dentre eles o SPRING. Neste sentido, o objetivo do trabalho foi avaliar o desempenho do software SPRING no mapeamento e classificação supervisionada das áreas sob influência dos reservatórios Goitá e Tapacurá, utilizando imagens IKONOS e Google Earth para os anos 2009 e 2019. Para a realização da classificação foram definidas duas áreas de influência da mata ciliar circundante às duas barragens alocadas na bacia do rio Capibaribe. Foram definidas seis classes de uso e ocupação do solo. A classificação foi feita utilizando o método de máxima verossimilidade, comparando os pixels da imagem um a um, com um limiar de classificação de 99%. Os resultados determinaram a existência de 2.2975 km2 de mata ciliar na barragem Goitá, apresentando maior mudança na área da cana de açúcar com acréscimo de 4.01%. Para a barragem Tapacurá foi determinada 1.8858 km2 de mata ciliar, tendo maior mudança na área de pastos com acréscimo de 8.43%. Ambas áreas mostraram incrementos substanciais na classe Mata (Goitá: 23.41% e Tapacurá: 17.83%). A utilização do software SPRING permitiu a classificação supervisionada da mata ciliar nestas barragens, determinando seis coberturas de importância.

Analysing Urban Development Patterns in a Conflict Zone: A Case Study of Kabul

A large part of the population in low-income countries (LICs) lives in fragile and conflict-affected states. Many cities in these states show high growth dynamics, but little is known about the relation of conflicts and urban growth. In Afghanistan, the Taliban regime, which lasted from 1996 to 2001, caused large scale displacement of the population. People from Afghanistan migrated to neighboring countries like Iran and Pakistan, and all developments came to a halt. After the US invasion in October 2001, all the major cities in Afghanistan experienced significant population growth, in particular, driven by the influx of internally displaced persons. Maximum pressure of this influx was felt by the capital city, Kabul. This rapid urbanization, combined with very limited capacity of local authorities to deal with this growth, led to unplanned urbanization and challenges for urban planning and management. This study analyses the patterns of growth between 2001 and 2017, and the factors influencing the growth in the city of Kabul with the help of high-resolution Earth Observation-based data (EO) and spatial logistic regression modelling. We analyze settlement patterns by extracting image features from high-resolution images (aerial photographs of 2017) and terrain features as input to a random forest classifier. The urban growth is analyzed using an available built-up map (extracted from IKONOS images for the year 2001). Results indicate that unplanned settlements have grown 4.5 times during this period, whereas planned settlements have grown only 1.25 times. The unplanned settlements expanded mostly towards the west and north west parts of the city, and the growth of planned settlements happened mainly in the central and eastern parts of the city. Population density and the locations of military bases are the most important factors that influence the growth, of both planned and unplanned settlements. The growth of unplanned settlement occurs predominantly in areas of steeper slopes on the hillside, while planned settlements are on gentle slopes and closer to the institutional areas (central and eastern parts of the city). We conclude that security and availability of infrastructure were the main drivers of growth for planned settlements, whereas unplanned growth, mainly on hillsides, was driven by the availability of land with poor infrastructure.

Shoreline Changes due to Abrasion in Pekalongan Utara Sub District Year 2003-2018

<em>The purpose of this research are: 1) analyze the shoreline change in the coastal areas of North Pekalongan Sub-district from 2003 to 2018; 2) Analyze the land use change in the coastal areas of North Pekalongan from 2003 to 2018; This type of research is a qualitative descriptive with a spatial approach. Data obtained from interpretation of IKONOS image from Google Earth in 2003, year 2013, and year 2018. The research steps were: (1) interpretation of Google Earth IKONOS image year 2003-2018, (2) Overlay of shoreline map of year 2003 and year 2018, (3) overlay of land use maps of year 2003- year 2018. The results of the study were: (1) The shoreline of North Pekalongan Sub-district was retreat 7.261 meters up to 94.383 meters. (2) The largest land use changes in North Pekalongan sub district was the change of land to a flooded land of 624.379 ha or 52.556% of land in the area.</em>

Remote sensing data fusion using fruit fly optimization

The image fusion (pan-sharpening) aims to generate a multispectral image with the maximum of spatial details of the panchromatic image and the spectral characteristics of the multispectral images. The generalized intensity-hue-saturation (GIHS) can produce fused images with high spatial details, but the spectral characteristics of the fused images need improvement. This article introduces an adaptive fusion method based on GIHS using the Fruit Fly Optimization Algorithm (FOA) in two stages. First, we suggest to use it to compute the optimal band weights which reduces the large difference between the intensity component and the panchromatic image. Second, we propose to apply the FOA to compute the modulation parameters that estimate the amount of spatial details to be added to the multispectral images. In this regard, an objective function that combines the coefficient of the correlation (CC) with the spatial coefficient of the correlation (SCC) is suggested. This method is tested on Pleiades, IKONOS, and ALSAT-2A images and we compared it with some existing fusion methods. The CC, the Structural Similarity Index (SSIM), The Root Mean Square Error (RMSE), the Quality with No Reference (QNR), the Relative Global Synthesis Error Metric (ERGAS) and the Relative Average Spectral Error (RASE) are used for quantitative analysis. The best values of CC, RMSE, ERGAS, RASE and QNR on all used datasets are given by the proposed method. The quantitative results and the visual analysis demonstrated that the proposed approach outperforms the four comparison methods in terms of spatial and spectral quality.

Comparative Analysis for the Detection of Marine Vessels from Satellite Images Using FCM and Marker-Controlled Watershed Segmentation Algorithm

The security of maritime activity is enhanced by the detection of marine vessels. Satellite images are used to detect the marine vessels irrespective of extreme weather conditions. Marine vessels can be detected efficiently using image segmentation algorithms. Many researchers have applied Haar-like classifier, convolution neural network, artificial neural network techniques to detect the marine vessels. In this work two different methodologies such as fuzzy C means (FCM) and marker-controlled watershed segmentation algorithms are developed and demonstrated to detect the marine vessels from satellite images. The marker-controlled watershed algorithm can effectively visualize an image in three dimensions and easily segments three-dimensional images. On the other hand, the number of iterations needed to achieve a specific clustering exercise in FCM is very less. It calculates the distance between the pixels and the cluster centres in the spectral domain to calculate the membership function. Experiments are carried out using IKONOS image of 4-m resolution. The average users accuracy of FCM algorithm and marker-controlled watershed algorithm is 91.29% and 95.79%, respectively. The results obtained show that there is an increase in accuracy for marker-controlled watershed algorithm when compared to FCM algorithm.

Application of remote sensing to identify Copper\u2013Lead\u2013Zinc deposits in the Heiqia area of the West Kunlun Mountains, Chinas

The harsh natural environment and inaccessibility of the West Kunlun Mountains are barriers for their investigation via field geology. Remote sensing technology has the advantage of being efficient on a macroscale and not being restricted by terrain or road conditions in sparsely vegetated areas with exposed bedrock. This work focuses on copper–lead–zinc deposits in the Heiqia area in the West Kunlun Mountains as a case study to illustrate the application of IKONOS remote sensing images as major data sources to fabricate a standard image map, the extraction of information on ore-controlling factors and mineralization through the use of image enhancement methods, and the interpretation of remote sensing data to identify mineral resources. Alteration anomaly information was extracted from ASTER data, verified via field survey and sampling, and used to develop a remote sensing model for utilization in future prospecting efforts. The results of the survey showed that in IKONOS (band 3, 2, and 1 synthesis) images, the copper mineralization zone exhibits interlaced gray-white, blue-gray, and blue tones in a narrow strip-like pattern, while the lead–zinc mineralization zone shows gray-white, light gray-yellow, and yellowish-brown tones in a strip-like pattern. The main remotely sensed alteration anomalies are characteristic of hydroxyl. Six hydroxyl anomalies were delineated in the study area, of which five were found to be copper–lead–zinc deposits. The location of the ore bodies coincides well with the ASTER anomalies extracted. Two Cu–Pb–Zn mineralization belts are present in the study area. The ore-bearing rock series of belt No. I is phyllite interbedded with metasandstone, and the ore comprises mainly copper deposits supplemented by lead–zinc deposits. Belt No. II is in limestone and consists mainly of lead–zinc deposits supplemented by copper deposits. A remote sensing geological prospecting model for structurally altered Wenquangou Group copper–lead–zinc deposits with a genesis related to hot water basins is established. This provides a basis for future prospecting for similar minerals in the West Kunlun metallogenic belt.

Regularization-based modulation transfer function compensation for optical satellite image restoration using joint statistical model in curvelet domain

The heavy-tailed properties of modulation transfer function (MTF) typically introduce noise and an aliasing effect in the MTF compensation (MTFC) effort to improve the spatial quality of optical satellite images. These degradative effects compromise the image’s signal-to-noise ratio (SNR). Consequently, users must evaluate the relative importance of image sharpness versus SNR for their applications to decide whether MTFC processing is appropriate. We propose a high-fidelity MTFC method that executes an optimal trade-off between noise regularization and detail preservation in the image restoration process to address this problem. To this end, we exploit the merit of image prior characteristics in both local smoothness and nonlocal self-similarity properties of an image in a hybrid domain (viz., space spatial and frequency) to design effective regularization terms that reflect these image properties. Furthermore, we establish a simple joint statistical model in the curvelet domain to combine these two properties. To make this regularization-based MTFC method tractable and robust, we employ the multiobjective bilevel optimization approach to solve the severely ill-posed inverse problem of MTFC. We conduct extensive experiments to evaluate the proposed regularization-based MTFC method using synthetically blurred images simulated from the level 2A product of IKONOS and real satellite images with unknown blur. Quantitative measurements of image quality reveal that the proposed method produces competitive restoration results with minimum computational complexity and exhibits a good convergence property. These experimental results show that the proposed method can find a compromise between regularizing noise and preserving image fidelity.

GEO-INFORMATIC APPLICATION IN ANOMALIES ASSESSMENT IN THE MASTER PLAN OF IDAH TOWN, KOGI STATE, NIGERIA

Abstract
 Nigerian urban environments are characterized by uncontrolled organic and consciously created urban settlements driven by a host of factors. This study used geo-informatic technology to assess the anomalies in the Master Plan of Idah town between 2005 and 2015. The Master Plan of Idah town and high resolution IKONOS images were acquired to provide data for the study while geo-informatic technology was use in the data analysis. The results show that in 2005 residential land use exceeded the expected area in the Master Plan by 1,474,270.6 m2, commercial land use is not accordingly developed having an area of 3,290 m2 encroached upon, that of industrial is encroached upon by 569,766.8 m2, educational is encroached upon by 769,766.8 m2, recreational is encroached upon by 1,521 m2, health is encroached upon by 62,802 m2, religious is encroached upon by 8,026 m2 while government and civic land is encroached upon by 9,265 m2, and transport is encroached upon by 50,333 m2. The study further revealed that the anomalies in the urban Master Plan of Idah town advanced in 2015 with the residential land use exceeded the expected land area designated for it in 2005 by 8,142,704.7 m2 while other land uses that were not fully developed were also encroached upon. Despite the detailed master plan was prepared for Idah town covering the period 1974 to 2005 the implementation is absolutely void. The study recommended that, the Master Plan should be urgently renew to avoid further land use development resulting in more anomalies.

Study on the Coastline Evolution in Sopot (2008–2018) Based on Landsat Satellite Imagery

The coastline is the boundary between the water surface in a reservoir or watercourse and the land, which is characterised by high instability and functional diversity. For these reasons, research on coastal monitoring has been conducted for several decades. Currently, satellite images performed with synthetic aperture radars (SARs) are used to determine its course and variability together with high-resolution multispectral imagery from satellites such as IKONOS, QuickBird, and WorldView, or moderate-resolution multispectral images from Landsat satellites. This paper analysed the coastline variability in Sopot (2008–2018) based on Landsat satellite imagery. Furthermore, based on multispectral images obtained, it was determined how the beach surface in Sopot changed. Research has shown that the coastline keeps moving away from the land every year. This was particularly noticeable between 2008 and 2018 when the coastline moved on average 19.1 m towards the Baltic Sea. Moreover, it was observed that the area of the sandy beach in Sopot increased by 14 170.6 m2, which translates into an increase of 24.7% compared to 2008. The probable cause of the continuous coastline shift towards the sea and the increase of the beach surface is the oceanographic phenomenon called tombolo, which occurred in this area as a result of the construction of a yacht marina near the coast.

Seascape metrics, spatio-temporal change, and intensity of use for the spatial conservation prioritization of a Caribbean marine protected area

An effective management tool for the spatial prioritization of the Cozumel Reefs National Park (CRNP), using a systematic approach, is an urgent need for the conservation of one of the most important Marine Protected Areas (MPAs) in Mexico. In this study, we integrated a model using seascape metrics, the spatio-temporal variation of substrate coverage, and frequency of reef use for SCUBA diving, to systematically identify priority areas for conservation within the CRNP. We identified seven habitat types: sand beds, reef, two habitats of macroalgae beds (high and low cover), and seagrass beds with three different densities, using a supervised classification on a SPOT 7 (2017) satellite image (overall accuracy of 84%, Kappa of 0.79). Matlab and Fragstats were used to calculate seascape metrics (habitat β-diversity, shape complexity and connectivity). A change detection analysis, comparing the SPOT 7 with an Ikonos image, was utilized to estimate seascape change between 2004 and 2017. Interviews with diving operators were also obtained, to calculate the frequency of visits to different reefs within the CRNP. A weighted overlay analysis, integrating the metrics mentioned above, was used to identify priority areas for conservation. We identified five areas, where management strategies need to be strengthened (e.g. limiting the number of visitors). The current subzonation of the protected area does not reflect the conservation priorities identified by our analysis. However, given that the CRNP management plan is in the process of being updated, the obtained results can help the CRNP managers improve the conservation efforts based on a systematic and robust approach, ensuring the design and implementation of cost-effective strategies for the conservation of the MPA.