SPIN-Based Linear Temporal Logic Path Planning for Ground Vehicle Missions with Motion Constraints on Digital Elevation Models.

Linear temporal logic (LTL) formalism can ensure the correctness of mobile robot planning through concise, readable, and verifiable mission specifications. For uneven terrain, planning must consider motion constraints related to asymmetric slope traversability and maneuverability. However, even though model checker tools like the open-source Simple Promela Interpreter (SPIN) include search optimization techniques to address the state explosion problem, defining a global LTL property that encompasses both mission specifications and motion constraints on digital elevation models (DEMs) can lead to complex models and high computation times. In this article, we propose a system model that incorporates a set of uncrewed ground vehicle (UGV) motion constraints, allowing these constraints to be omitted from LTL model checking. This model is used in the LTL synthesizer for path planning, where an LTL property describes only the mission specification. Furthermore, we present a specific parameterization for path planning synthesis using a SPIN. We also offer two SPIN-efficient general LTL formulas for representative UGV missions to reach a DEM partition set, with a specified or unspecified order, respectively. Validation experiments performed on synthetic and real-world DEMs demonstrate the feasibility of the framework for complex mission specifications on DEMs, achieving a significant reduction in computation cost compared to a baseline approach that includes a global LTL property, even when applying appropriate search optimization techniques on both path planners.

Groundwater salinity modeling and mapping using machine learning approaches: a case study in Sidi Okba region, Algeria.

The groundwater salinization process complexity and the lack of data on its controlling factors are the main challenges for accurate predictions and mapping of aquifer salinity. For this purpose, effective machine learning (ML) methodologies are employed for effective modeling and mapping of groundwater salinity (GWS) in the Mio-Pliocene aquifer in the Sidi Okba region, Algeria, based on limited dataset of electrical conductivity (EC) measurements and readily available digital elevation model (DEM) derivatives. The dataset was randomly split into training (70%) and testing (30%) sets, and three wrapper selection methods, recursive feature elimination (RFE), forward feature selection (FFS), and backward feature selection (BFS) are applied to train the data. The resulting combinations are used as inputs for five ML models, namely random forest (RF), hybrid neuro-fuzzy inference system (HyFIS), K-nearest neighbors (KNN), cubist regression model (CRM), and support vector machine (SVM). The best-performing model is identified and applied to predict and map GWS across the entire study area. It is highlighted that the applied methods yield input variation combinations as critical factors that are often overlocked by many researchers, which substantially impacts the models' accuracy. Among different alternatives the RF model emerged as the most effective for predicting and mapping GWS in the study area, which led to the high performance in both the training (RMSE = 1.016, R = 0.854, and MAE = 0.759) and testing (RMSE = 1.069, R = 0.831, and MAE = 0.921) phases. The generated digital map highlighted the alarming situation regarding excessive GWS levels in the study area, particularly in zones of low elevations and far from the Foum Elgherza dam and Elbiraz wadi. Overall, this study represents a significant advancement over previous approaches, offering enhanced predictive performance for GWS with the minimum number of input variables.

A dataset of endorheic basins on detailed delineation and classification for the Qinghai-Tibet Plateau.

Endorheic basins are important geomorphological and ecological units on the Qinghai-Tibet Plateau (QTP), which is undergoing a rapid evolution of its lake system structure and drainage reorganization that is threatening local ecology, infrastructures and residuals owing to climate change. This dataset provides a detailed delineation and classification of endorheic basins on the QTP for understanding the complex dynamics under climate changes. A newly-developed algorithm, namely the Joint Elevation-Area Threshold (JEAT) algorithm (Liu et al, 2024), is applied for delineating endorheic basins based on digital elevation model (DEM). A total of 184 endorheic basins were divided, of which the permanent divide lines were characterized. All the endorheic basins were further categorized into five groups based on the hydraulic connectivity attributes, which have been commonly observed since 2000. The dataset also includes basic information such as drainage area, water surface area, and water storage volume of each endorheic basin. It is particularly beneficial for digital watershed analysis towards ecological restoration and water resource management on the environmentally vulnerable QTP.

An On-Site InSAR Terrain Imaging Method with Unmanned Aerial Vehicles.

An on-site InSAR imaging method carried out with unmanned aerial vehicles (UAVs) is proposed to monitor terrain changes with high spatial resolution, short revisit time, and high flexibility. To survey and explore a specific area of interest in real time, a combination of a least-square phase unwrapping technique and a mean filter for removing speckles is effective in reconstructing the terrain profile. The proposed method is validated by simulations on three scenarios scaled down from the high-resolution digital elevation models of the US geological survey (USGS) 3D elevation program (3DEP) datasets. The efficacy of the proposed method and the efficiency in CPU time are validated by comparing with several state-of-the-art techniques.

Estimation of soil health in the semi­arid regions of northwestern Iran using digital elevation model and remote sensing data.

Nowadays, neglecting soil conservation issues is one of the most critical factors in reducing soil health (SH). In this regard, to facilitate the estimation of the SH in northwestern Iran, 292 soil samples were taken from a depth of 0-30 cm of this area, and a wide range of soil properties were determined. Then, soil health indices (SHIs) were calculated. Simultaneously, the normalized difference vegetation index (NDVI), surface water capacity index (SWCI), and a digital elevation model (DEM) were obtained from satellite data. Finally, multiple linear regression (MLR) relationships between these parameters and SHIs were calculated. In this study, there was a highest significant positive correlation (P < 0.01) between IHI-LTDS and SWCI (0.71**), DEM (0.76**), and NDVI (0.73**). The MLR, with both the whole total (TDS) and minimal (MDS) dataset methods, which includes the aforementioned indices, strongly described the spatial variability of the Integrated Soil Health Index (IHI) (R2 = 0.78, AIC = - 416, RMSE = 0.05, and ρc = 0.76). According to the results of this study, it can be said that the development of SH estimation models using remote sensing extracted parameters can be one of the effective ways to reduce the cost and time of soil sampling in extensive areas.

Coastal Vulnerability Index sensitivity to shoreline position and coastal elevation parameters in the Niger Delta region, Nigeria.

It is imperative to assess coastal vulnerability to safeguard coastal areas against extreme events and sea-level rise. In the Niger Delta region, coastal vulnerability index assessment in the past focused on open-access parameters without comparing the open-access parameters, especially coastal elevation and shoreline change. This sensitivity to the shoreline method and open-access coastal elevation limits the information for the planning of coastal adaptation. The area under investigation is the Niger Delta, which is distinguished by its low-lying coastal plains and substantial ecological and economic significance. In light of the selected parameters, Sentinel-1 GRD images from 2015 to 2022 during high tidal conditions were used to delineate the shoreline position and change rate. Also, different open-access DEMs were used to derive the coastal elevation using the Geographic Information System (GIS) approach. The study employs 5 parameters, such as shorelines obtained from Sentinel-1 SAR images and several Digital Elevation Models (DEMs), geomorphology, mean sea level rise, significant wave height, and mean tide range, in conjunction with the initial Coastal Vulnerability Index (CVI) approach. The study reveals that the type of DEM used significantly influences the coastal elevation ranking and, subsequently, the CVI. Differences in shoreline change rate estimation methods (EPR and LRR) also impact the vulnerability rankings but to a lesser extent. The findings highlight that 40.1% to 58.9% of the Niger Delta coastline is highly or very highly vulnerable to sea-level rise, depending on the shoreline change rate or DEM used. The study underscores the potential of using CVI methods with open-access data in data-poor countries for identifying vulnerable coastal areas that may need protection or adaptation. Lastly, it points out the need for higher resolution DEMs.

Estimation of Soil Characteristics from Multispectral Sentinel-3 Imagery and DEM Derivatives Using Machine Learning.

In this paper, different machine learning methodologies have been evaluated for the estimation of the multiple soil characteristics of a continental-wide area corresponding to the European region, using multispectral Sentinel-3 satellite imagery and digital elevation model (DEM) derivatives. The results confirm the importance of multispectral imagery in the estimation of soil properties and specifically show that the use of DEM derivatives improves the quality of the estimates, in terms of R2, by about 19% on average. In particular, the estimation of soil texture increases by about 43%, and that of cation exchange capacity (CEC) by about 65%. The importance of each input source (multispectral and DEM) in predicting the soil properties using machine learning has been traced back. It has been found that, overall, the use of multispectral features is more important than the use of DEM derivatives with a ration, on average, of 60% versus 40%.

Topographic hydro-conditioning to resolve surface depression storage and ponding in a fully distributed hydrologic model.

Land surface depressions play a central role in the transformation of rainfall to ponding, infiltration and runoff, yet digital elevation models (DEMs) used by spatially distributed hydrologic models that resolve land surface processes rarely capture land surface depressions at spatial scales relevant to this transformation. Methods to generate DEMs through processing of remote sensing data, such as optical and light detection and ranging (LiDAR) have favored surfaces without depressions to avoid adverse slopes that are problematic for many hydrologic routing methods. Here we present a new topographic conditioning workflow, Depression-Preserved DEM Processing (D2P) algorithm, which is designed to preserve physically meaningful surface depressions for depression-integrated and efficient hydrologic modeling. D2P includes several features: (1) an adaptive screening interval for delineation of depressions, (2) the ability to filter out anthropogenic land surface features (e.g., bridges), (3) the ability to blend river smoothing (e.g., a general downslope profile) and depression resolving functionality. From a case study in the Goodwin Creek Experimental Watershed, D2P successfully resolved 86% of the ponds at a DEM resolution of 10 m. Topographic conditioning was achieved with minimum impact as D2P reduced the number of modified cells from the original DEM by 51% compared to a conventional algorithm. Furthermore, hydrologic simulation using a D2P processed DEM resulted in a more robust characterization on surface water dynamics based on higher surface water storage as well as an attenuated and delayed peak streamflow.

Flood risk assessment in the Karamana river basin, Kerala, using HEC-RAS.

The State of Kerala has frequently been facing a series of flooding phenomena that have adversely affected its multiple sectoral growths. The floods of 2018 have happened to be one of the most devastating floods that have occurred in the State of Kerala. It was seen that nearly thirteen out of fourteen districts in Kerala were tremendously affected during the 2018 August floods. The worst affected districts during the 2018 floods were Trivandrum, Pathanamthitta, Idukki, Thrissur, Ernakulam, and Kottayam. A sub-region near the Karamana basin located in the Trivandrum district is considered for the present study. The Karamana sub-region is a highly urbanized area that is also more or less prone to intense riverine flooding. The major rivers-Karamana and Killi-along with their respective tributaries, are the water bodies in the study region. Extensive urbanization, along with the overflowing of rivers during monsoon seasons, has paved the way for intense flooding in the region. This, in turn, necessitates developing a flood model for the sub-region. The development of an efficient flood model will aid in understanding the future challenges related to a flooding event in a region. In this study, the flood return probability water levels for the 5-year, 10-year, 25-year, 50-year, 100-year, 250-year, and 500-year were estimated for the Karamana sub-region. Besides, the flood risk zoning for the study area was conducted and elaborated as very high risk, high risk, moderate risk, and low risk for the different areas of the sub-region. Overall, the study can be helpful in identifying the most vulnerable areas to flooding in the Karamana region. By the proper identification of vulnerable areas in the region, proper planning and early warning measures can be devised and carried out by policymakers.

Remote sensing and nuclear techniques for high-resolution mapping and quantification of gully erosion in the highly erodible area of the Malcanska River Basin, Eastern Serbia.

Gully erosion leads to the formation of deep and wide channels that increase the risk of soil loss, flooding, and water pollution. In addition, this process reduces the productivity and viability of agricultural land and natural ecosystems. Preventing gully erosion is critical for maintaining ecological balance and preserving natural resources in certain areas. This paper presents a methodology integrating remote sensing and nuclear techniques to study gully erosion. The morphometric characterization of gullies using 360-degree camera photogrammetry was introduced as a new method in erosion research. This approach aims to investigate the suitability of unmanned aerial vehicle and terrestrial photogrammetry for modeling gullies, to study the variability of erosion processes in gullies at a small scale, and to compare the differences in erosion intensity between nearby gullies. The study's objectives include identifying the effective and economical method for gullies monitoring and providing a starting point for controlling and safeguarding gullies. Mainly erosion process was detected in the studied gullies, while deposition was identified at only 2 out of 39 sampling locations. The results showed an average soil redistribution rate of 16.2 t ha-1 yr-1 and coefficients of variation of 32%, 59%, and 91% for three investigated gullies. It was determined that aerial photogrammetry methods were not practical under the conditions prevailing in the study area. Highly detailed 3D models of the gullies were created using 360-degree photogrammetry. It was confirmed that the micro-relief obtained by photogrammetric modeling is an essential contribution to erosion research. The 360-degree camera photogrammetry serves as a reliable tool for analyzing the morphology of gullies and, in perspective, tracking changes in gully systems over time or monitoring the effectiveness of the applied protection measures.

How does the choice of DEMs affect catchment hydrological modeling?

The digital elevation models (DEMs) are the primary and most important spatial inputs for a wide range of hydrological applications. However, their availability from multiple sources and at various spatial resolutions poses a challenge in watershed modeling as they influence hydrological feature delineation and model simulations. In this study, we evaluated the effect of DEM choice on stream and catchment delineation and streamflow simulation using the SWAT model in four distinct geographic regions with diverse terrain surfaces. Performance evaluation metrics, including Willmott's index of agreement, and nRMSE combined with visual comparisons were employed to assess each DEM's performance. Our results revealed that the choice of DEM has a significant impact on the accuracy of stream and catchment delineation, while its influence on streamflow simulation within the same catchment was relatively minor. Among the evaluated DEMs, AW3D30 and COP30 performed the best, closely followed by MERIT, whereas TanDEM-X and HydroSHEDS exhibited poorer performance. All DEMs displayed better accuracy in mountainous and larger catchments compared to smaller and flatter catchments. Forest cover also played a role in accuracy, mainly due to its association with steep slopes. Our findings provide valuable insights for making informed data selection decisions in watershed modeling, considering the specific characteristics of the catchment and the desired level of accuracy.

A rapid flood inundation model for urban flood analyses.

An efficient inundation model is required for flood early warning systems in urban areas. A 2D flood model uses a governing shallow water equation, and this model is computationally expensive despite benefiting from parallel computing techniques. As an alternative to conventional flood models, cellular automata (CA) and DEM-based models (DBMs) have been studied. CA flood models simulate floods efficiently. However, a small time step is required to ensure model stability when the grid size decreases due to its diffusive characteristics. Conversely, DBM models produce results quickly, but they only show the maximum flood extent. Additionally, pre- and postprocessing are required, which take considerable time. This study proposes a hybrid inundation model that combines the two alternative approaches, and it efficiently produces a high- resolution flood map without complex pre- and postprocessing. The hybrid model is also integrated with a 1D drainage module, and thus, the model reliably simulates urban area floods.•The rapid flood inundation model integrates CA module to simulate temporal distribution of floods and DEM module to provide details of floods.•A 1D Saint Venant equation is also solved in the rapid flood inundation model to simulate the drainage sytems in urban areas.•Two-way coupling between 2D-surface and 1D-drainag models are considered in the rapid flood inundation model.

Waterline digital elevation model development to quantify inundation duration and coastal protection of tidal wetlands.

Coastal tidal wetlands are sufficiently acknowledged for the supplied vital ecosystem functions, including flood protection and biological conservation. Measuring and estimating reliable topographic data is essential for quantifying mangrove habitat quality. This study proposes a novel methodology for quickly constructing a digital elevation model (DEM) with an instantaneous waterline combined with tidal level records. Unmanned aerial vehicles (UAVs) enabled on-site waterline interpretation analysis. The results show that image enhancement improves the accuracy of waterline recognition and object-based image analysis has the highest accuracy. The waterline DEM (WDEM) performs a more accurate elevation production than UAV DEM, indicating that its application to habitat evaluation and prediction could be more reliable. Hydrodynamic simulations incorporated with the mangrove habitat model were utilized to calculate inundation duration, flow resistance, and vegetation dissipation potential according to the verified WDEM. The larger the mangrove coverage ratio, the stronger the flow resistance, which means that the protective consequence of the mangrove on the natural embankment is evident. The WDEM and nature-based solutions presented facilitate an adequate understanding of coastal protection and promote the potential ecosystem-based disaster risk reduction of mangrove wetlands.

Modelling hydrological factors from DEM using GIS.

Hydrological modelling is a precondition for many scientific researches such as species distribution models, ecological models, agricultural suitability models, climatological models, hydrological models, flood and flash flood models, landslide models etc. Even the topographic control over many hydrological factors has also been studied. Over time different hydrological models have been developed and extensively used. Recently, these models have been used to prepare different types of conditional factors that are widely used in hazard modelling such as floods, flash floods, landslides etc. Quantitative analysis of the Digital Elevation Model (DEM) according to different models by engaging Geographic Information Systems (GIS) supports users to extract various types of information about landscapes where hydrological and topographic information are most important. Methods to prepare hydrological factors namely TWI, TRI, SPI, STI, TPI, stream density and distance to stream by processing DEM in GIS are discussed in this paper. These common hydrological factors are extensively used in many scientific research papers either for modelling or to measure their relationship with other environmental factors.•Hydrological factors have great importance in understanding the landscape and are widely used in scientific research, especially geo-environmental hazard mapping.•Physically based hydrological methods are engaged in ArcMap 10.5 software.•Commonly used hydrological factors are processed using freely available DEM and ArcMap 10.5 software.

Application of geographical information system-based analytical hierarchy process modeling for flood susceptibility mapping of Krishna District in Andhra Pradesh.

Flooding is one of the most catastrophic natural disasters in terms of provoking socio-economic losses. The current study is to foster a flood susceptibility map of Krishna District in Andhra Pradesh (AP) through integrating remote sensing data, geographical information system (GIS), and the analytical hierarchy process (AHP). Eleven factors, including elevation, slope, aspect, land use/land cover (LULC), drainage density, topographic wetness index, stream power index, lithology, soil, precipitation, and distance from the streams, are considered for identifying and evaluating the spatial distribution of critical flood-susceptible regions. Thematic maps of different factors were derived in GIS using remote sensing data obtained from Sentinel-2A (satellite sensor), shuttle radar topography mission digital elevation model (SRTM DEM v3), and other scientific data products. An analytical hierarchy process is a mathematical approach for decision support, primarily based on the weight and rank of different causative factors. AHP technique is implemented for flood hazard modeling and ascertaining the Flood Hazard Index (FHI) to produce a flood susceptibility map. Different thematic maps weighed with the AHP framework are combined using overlay analysis to produce the flood susceptibility map of the study region. The outcomes of the study demonstrate the potential of GIS and AHP in providing a premise to recognize the vulnerable areas that are susceptible to flood. According to the findings, the Flood Hazard Index is 42% and the study region is classified into very high, high, moderate, low, and very low susceptible, respectively. Following that, historical flood data was used to validate the accuracy of the generated flood susceptibility map. This shows that a maximum of 90% of the data points are within floodplain.

Effects of digital elevation model data source on HSPF-based watershed-scale flow and water quality simulations.

Digital elevation models (DEMs) from different sources have been widely utilized in watershed modeling and environment management. Yet, little is known about how DEMs from different data sources affect modeling results and management decisions. This paper presents new insights into how the DEMs from three different sources affect model-simulated flow, nitrate (NO3), phosphorus (P), and sediment by using the BASINS/HSPF watershed modeling system. It was found that DEM source-induced uncertainties in simulation results are higher than the DEM resolution-induced uncertainties regardless of watershed slope or delineation method. Moreover, DEM source introduces higher uncertainties in simulation results for automatically delineated low-gradient watersheds than high gradient watersheds. Sediment and NO3 concentrations were the most and the least sensitive water quality parameters, respectively, to DEM sources. The uncertainties in simulation results may be reduced by using the manual method for watershed delineation but they cannot be completely eliminated. It is recommended that high precision (such as NED) DEMs be employed especially for flat watersheds. The findings provide guidelines for selection of DEM source based on available resources.

Modelling land use/land cover (LULC) change dynamics, future prospects, and its environmental impacts based on geospatial data models and remote sensing data.

The dynamic change in land use/land cover (LULC) caused by rapid urbanization has become a major concern in Lahore, causing a variety of socioeconomic and environmental issues relating to air quality. As a result, it is important to monitor existing LULC change detection, future projection, and the increase in atmospheric pollutants in Lahore. This research work makes use of Landsat, GIOVANNI, SRTM DEM, and vector data. The four key steps of the research approach are as follows: (i) LULC classification from 2000 to 2020 using Lansat data and semi-automatic classification plugin (SCP); (ii) simulation of future prediction using Modules of Land Use Change Evaluation (MOLUSCE) prediction model; (iii) assessment of effects of land use change on air quality using GIOVANNI-NASA product; and (iv) monitoring, change detection, and result interpretation. According to the research findings, there was an increase in metropolitan areas and a decrease in vegetation, barren land, and water bodies for both historical and future projections. The findings also indicated that from 2000 to 2020, about 27.41% of the urban area expanded, with a decline of 42.13% in vegetation, 2.3% in the barren land, and 6.51% in water bodies, respectively. Between 2020 and 2040, the urban area is predicted to increase by 23.15%, while vegetation, barren land, and water bodies are expected to decrease by 28.05%, 1.8%, and 12.31%, respectively. Also, the atmospheric pollutants have been increased including NO2 (1.60%), SO2 (1.02%), CO2 (0.71%), CO (1.56%), O3 (0.15%), and CH4 (0.20%), respectively. And it is projected that by 2040, the average annual atmospheric concentration of NO2, SO2, CO2, CO, O3, and CH4 will be increased by 28.80%, 18.36%, 12.78%, 28.08%, 2.70%, and 3.60%, respectively. In addition, it was also observed that the majority of the city's urban area expansion was found in the city's eastern and southern regions. Therefore, government should focus on natural resource conservation especially vegetation cover to reduce air pollutants concentration and the LULC effect.

Prediction of soil water contents and erodibility indices based on artificial neural networks: using topography and remote sensing.

This study aimed to predict some soil water contents and soil erodibility indices with a multilayer perceptron (MLP) artificial neural network (ANN) using remote sensing data (Landsat 8 OLI TIRS) and topographic variables from a digital elevation model (DEM) in a semi-arid ecosystem. In models, the input variables were derived from remote sensing imaging and DEM. The output variables were field capacity, wilting point, aggregate stability index, structural stability index, dispersion ratio, and clay flocculation index. This study was realized in the watersheds of the Koruluk dam, the Kizlarkalesi, and the Telme ponds built for agricultural irrigation in Gümüshane-Siran. The soil samples were obtained from two depths (0-10 cm and 10-20 cm) from 59 soil profiles. Besides field capacity, wilting point, and aggregate stability analysis, undispersed/dispersed sand, silt, clay contents, and organic matter analysis were performed due to their strong effect on soil moisture, soil water content, and erodibility indices. The correlation analysis results showed significant relationships between soil characteristics and soil water contents/soil erodibility indices. The remote sensing variables were derived from three Landsat images of 2015 (June, July, and September). The performance results of MLP ANN models predicted for soil water contents and erodibility indices ranged from 0.75 to 0.90 for R2, 0.046-4.115 for root mean square error (RMSE), 4.46-6.54 for normalized root mean square error (NRMSE), and 0.042-0.186 for mean absolute error (MAE). Topography was a more significant group of variables that affected soil water contents and soil erodibility indices and the feature importance of topography in the prediction was over 55%. The results showed that the use of topographic variables together with remote sensing variables in MLP ANN modeling increased the performance of the models.

The composite dataset of the present-day Infralittoral Prograding Wedges (IPWs) in the inner continental shelf of the Campania region (Central-Eastern Tyrrhenian Sea).

This article reports on the dataset gathered following the census of 83 present-day Infralittoral Prograding Wedges (IPWs), surveyed on the inner continental shelf of the Central-Eastern Tyrrhenian Sea. The purpose of the census was to explore their bathymetric range and assess the observational laws governing this variability. The ensued dataset (Campania Region IPW Dataset, CRID) includes geographic, topographic and morpho-bathymetric indices, descriptive of each IPW and more, the exposure of each IPW to wave forcing (Geographical fetch, Effective fetch and extreme significant wave height, H S ). In this work, histograms contribute to describe all the variables and highlight the dominant features of each IPW. Location maps univocally links the geographic position of each IPW to the appropriate attribute record in the dataset. Further, thematic maps illustrate eight wave fields obtained by offshore-to-nearshore transformation by as many sea states scenarios with 200-year return period. Such wave fields are used as sources for significant wave height representing wave conditions over each IPW. This dataset could be implemented with new measures at a broader scale, by following analogue procedures for measurements, to enlarge the observational scale on IPWs and improve the numerical models which might eventually derive by the analysis of this dataset.

Measuring habitat complexity and spatial heterogeneity in ecology.

Habitat complexity has been considered a key driver of biodiversity and other ecological phenomena for nearly a century. However, there is still no consensus over the definition of complexity or how to measure it. Up-to-date and clear guidance on measuring complexity is urgently needed, particularly given the rise of remote sensing and advent of technologies that allow environments to be scanned at unprecedented spatial extents and resolutions. Here we review how complexity is measured in ecology. We provide a framework for metrics of habitat complexity, and for the related concept of spatial heterogeneity. We focus on the two most commonly used complexity metrics in ecology: fractal dimension and rugosity. We discuss the pros and cons of these metrics using practical examples from our own empirical data and from simulations. Fractal dimension is particularly widely used, and we provide a critical examination of it drawing on research from other scientific fields. We also discuss informational metrics of complexity and their potential benefits. We chart a path forward for research on measuring habitat complexity by presenting, as a guide, sets of essential and desirable criteria that a metric of complexity should possess. Lastly, we discuss the applied significance of our review.