Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area

This paper presents landslide-susceptibility mapping using an adaptive neuro-fuzzy inference system (ANFIS) using a geographic information system (GIS) environment. In the first stage, landslide locations from the study area were identified by interpreting aerial photographs and supported by an extensive field survey. In the second stage, landslide-related conditioning factors such as altitude, slope angle, plan curvature, distance to drainage, distance to road, soil texture and stream power index (SPI) were extracted from the topographic and soil maps. Then, landslide-susceptible areas were analyzed by the ANFIS approach and mapped using landslide-conditioning factors. In particular, various membership functions (MFs) were applied for the landslide-susceptibility mapping and their results were compared with the field-verified landslide locations. Additionally, the receiver operating characteristics (ROC) curve for all landslide susceptibility maps were drawn and the areas under curve values were calculated. The ROC curve technique is based on the plotting of model sensitivity — true positive fraction values calculated for different threshold values, versus model specificity — true negative fraction values, on a graph. Landslide test locations that were not used during the ANFIS modeling purpose were used to validate the landslide susceptibility maps. The validation results revealed that the susceptibility maps constructed by the ANFIS predictive models using triangular, trapezoidal, generalized bell and polynomial MFs produced reasonable results (84.39%), which can be used for preliminary land-use planning. Finally, the authors concluded that ANFIS is a very useful and an effective tool in regional landslide susceptibility assessment.     

Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley,Malaysia

The purpose of the present paper is to manifest the results of the neuro-fuzzy model using remote sensing data and GIS for landslide susceptibility analysis in a part of the Klang Valley areas i Malaysia. Landslide locations in the study area were identified by interpreting aerial photographs and satellite images, supported by extensive field surveys. SPOT 5 satellite imagery was used to map vegetation index. Maps of topography, lineaments, NDVI and land cover were constructed from the spatial datasets. Seven landslide conditioning factors such as altitude, slope angle, plan curvature, distance from drainage, soil type, distance from faults and NDVI were extracted from the spatial database. These factors were analyzed using a neuro-fuzzy model (adaptive neuro-fuzzy inference system, ANFIS) to construct the landslide susceptibility maps. During the model development works, total 5 landslide susceptibility models were obtained by using ANFIS results. For verification, the results of the analyses were then compared with the field-verified landslide locations. Additionally, the ROC curves for all landslide susceptibility models were drawn and the area under curve values was calculated. Landslide locations were used to validate results of the landslide susceptibility map and the verification results showed 98% accuracy for the model 5 employing all parameters produced in the present study as the landslide conditioning factors. The validation results showed sufficient agreement between the obtained susceptibility map and the existing data on landslide areas. Qualitatively, the model yields reasonable results which can be used for preliminary landuse planning purposes. As a conclusion, the ANFIS is a very useful tool for regional landslide susceptibility assessments.     

A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison

This article uses an integrated methodology based on a chi-squared automatic interaction detection (CHAID) model combined with analytic hierarchy process (AHP) for pair-wise comparison to assess medium-scale landslide susceptibility in a catchment in the Inje region of South Korea. An inventory of 3596 landslide locations was collected using remote sensing, and a random sample comprising 30% of these was used to validate the model. The remaining portion (70%) was processed by the nearest-neighbour index (NNI) technique and used for extracting the cluster patterns at each location. These data were used for model training purposes. Ten landslide-conditioning factors (independent variables) representing four main domains, namely (1) topology, (2) geology, (3) hydrology, and (4) land cover, were used to produce two landslide-susceptibility maps. The first landslide-susceptibility map (LSM1) was produced by overlaying the terminal nodes of the CHAID result tree. The second landslide-susceptibility map (LSM2) was produced using the overlay result of AHP pair-wise comparisons of CHAID terminal nodes. The prediction rate curve results were better with LSM2 (area under the prediction curve (AUC) = 0.80) than with LSM1 (AUC = 0.76). The results confirmed that the integrated hybrid model has superior prediction performance and reliability, and it is recommended for future use in medium-scale landslide-susceptibility mapping.     

Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling

Data collection for landslide susceptibility modeling is often an inhibitive activity. This is one reason why for quite some time landslides have been described and modelled on the basis of spatially distributed values of landslide-related attributes. This paper presents landslide susceptibility analysis in the Klang Valley area, Malaysia, using back-propagation artificial neural network model. A landslide inventory map with a total of 398 landslide locations was constructed using the data from various sources. Out of 398 landslide locations, 318 (80%) of the data taken before the year 2004 was used for training the neural network model and the remaining 80 (20%) locations (post-2004 events) were used for the accuracy assessment purpose. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Eleven landslide occurrence related factors were selected as: slope angle, slope aspect, curvature, altitude, distance to roads, distance to rivers, lithology, distance to faults, soil type, landcover and the normalized difference vegetation index value. For calculating the weight of the relative importance of each factor to the landslide occurrence, an artificial neural network method was developed. Each thematic layer's weight was determined by the back-propagation training method and landslide susceptibility indices (LSI) were calculated using the trained back-propagation weights. To assess the factor effects, the weights were calculated three times, using all 11 factors in the first case, then recalculating after removal of those 4 factors that had the smallest weights, and thirdly after removal of the remaining 3 least influential factors. The effect of weights in landslide susceptibility was verified using the landslide location data. It is revealed that all factors have relatively positive effects on the landslide susceptibility maps in the study. The validation results showed sufficient agreement between the computed susceptibility maps and the existing data on landslide areas. The distribution of landslide susceptibility zones derived from ANN shows similar trends as those obtained by applying in GIS-based susceptibility procedures by the same authors (using the frequency ratio and logistic regression method) and indicates that ANN results are better than the earlier method. Among the three cases, the best accuracy (94%) was obtained in the case of the 7 factors weight, whereas 11 factors based weight showed the worst accuracy (91%).     

Quantifying the spatial distribution of soil mass wasting processes after the 2008 earthquake in Wenchuan, China: A case study of the Longmenshan area

The beautiful Longmenshan area is one of the main tourist attractions in Sichuan Province, China. The epicenter of a catastrophic earthquake measured at 8.0 Ms (China Seismological Bureau), occurred within this area at Wenchuan (31°01′16″N, 103°22′01″E) at 14:28 May 12, 2008 (Beijing time). The earthquake triggered numerous types of landslide transport and hazards, including soil and debris avalanches, rockfalls, slumps, debris flows, creation of barrier lakes and slope flattenings. This paper examines the landslide hazards in the Longmenshan area caused by the earthquake using remotely sensed images, mainly Beijing-1 Microsatellite data before and after the earthquake, compared to digital elevation maps and slope gradient maps, land use and vegetation cover maps. Areas of erosion and loss of vegetation were compared from pre- and post-earthquake data, from which were calculated changes in vegetated areas, bare slopes, and mass movement during the earthquake. These events occurred over altitudes from 1000 to 4000 m and on slope angles between 25 and 55°. The results show that the total area of erosion and land movement due to the earthquake increased by 86.3 km² (19.2% of the study area). Compared with pre-earthquake, the areas of very low intensity soil erosion and moderate intensity soil erosion were respectively reduced by 3.6 km², 24.3 km² and 30.9 km². On the other hand, the areas of severe and very severe intensity soil erosion were substantially increased by 45.8 km² and 99.2 km². In the post-earthquake stage, the bare areas (vegetation cover < 15%) have increased by 65.8 km². Without vegetation, the denuded earthquake damaged slopes and other high risk sites have become severe erosion problems. Thus, it is essential to continue long-term monitoring of mass wasting in the denuded areas and evaluate potential risk sites for future landslides and debris flows. We anticipate that these results will be helpful in decision making and policy planning for recovery and reconstruction in the earthquake-affected area.     

Application of binary logistic regression analysis and its validation for landslide susceptibility mapping in part of Garhwal Himalaya,India

Landslides cause heavy damage to property and infrastructure, in addition to being responsible for the loss of human lives, in many parts of the Himalaya. It is possible to take appropriate management measures to reduce the risk from potential landslide hazard with the help of landslide hazard zonation (LHZ) maps. The present work is an attempt to utilize binary logistic regression analysis for the preparation of a landslide susceptibility map for a part of Garhwal Himalaya, India, which is highly prone to landslides, by taking the geological, geomorphological and topographical parameters into consideration. Remote sensing and the geographic information system (GIS) were found to be very useful in the input database preparation, data integration and analysis stages. The coefficients of the predictor variables are estimated using binary logistic regression analysis and are used to calculate the landslide susceptibility for the entire study area within a GIS environment. The receiver operator characteristic curve analysis gives 88.7% accuracy for the developed model.     

An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley,Himalayas

Landslides are natural hazards that cause havoc to both property and life every year, especially in the Himalayas. Landslide hazard zonation (LHZ) of areas affected by landslides therefore is essential for future developmental planning and organization of various disaster mitigation programmes. The conventional Geographical Information System (GIS)-based approaches for LHZ suffer from the subjective weight rating system where weights are assigned to different causative factors responsible for triggering a landslide. Alternatively, artificial neural networks (ANNs) may be applied. These are considered to be independent of any strict assumptions or bias, and they determine the weights objectively in an iterative fashion. In this study, an ANN has been applied to generate an LHZ map of an area in the Bhagirathi Valley, Himalayas, using spatial data prepared from IRS-1B satellite sensor data and maps from other sources. The accuracy of the LHZ map produced by the ANN is around 80% with a very small training dataset. The distribution of landslide hazard zones derived from ANN shows similar trends as that observed with the existing landslides locations in the field. A comparison of the results with an earlier produced GIS-based LHZ map of the same area by the authors (using the ordinal weight rating method) indicates that ANN results are better than the earlier method.     

Study of tectonics in relation to the seismic activity of the Dalvat area,Nasik District,Maharashtra, India using remote sensing and GIS techniques

Well‐documented geological data (from both field and satellite) in the Deccan Volcanic Provinces (DVP) in and around the Dalvat region, Nasik District, India has been analysed by Geographic Information System (GIS) techniques and reported in this paper so as to relate the geology and structures with recent seismicity. It has been the belief among earth scientists that the Deccan Traps in Maharashtra, India is tectonically stable as the region attained solidity long ago. However, recent activity in the study area altered this concept and it is now accepted that seismic activity is still continuing on a mild scale. As such, the need has arisen to take into consideration historical as well as recent geological data to study in detail the tectonic setup in the Deccan Traps.

Using the well‐known relationship between the shear zone, lineaments, and geomorphology, and incorporating these with tectonic events, an attempt has been made to explore the geology and structures in and around the Dalvat region. Field observations and signatures on remote sensing data show that there is evidence of fault traces in the form of shear zones and slickensides in the Deccan Traps near the Kosurde, Dhanoli, Chikhli, and Manchandar villages of the Nasik District. The study has further been incorporated with seismic density data. Magnitudes of 3.9 were recorded as the maximum micro‐epicentres, and they fell on the shear fractures detected in the area of study.

In order to identify seismically vulnerable areas, seismic hazard zonation (SHZ) mapping has been carried out. Different data layers, including structural, lithological, geomorphological, drainage, and soil have been visually interpreted, scanned, and rectified. A rose diagram of the lineaments shows trends in the NNE–SSW to NS falling on major seismic zones of the study area, showing weaker zones beneath the surface. Raster images were digitized for conversion to a vector coverage using ERDAS 8.6. and ArcGIS 8.3. The ordinal scale (qualitative) relative weighting rating technique was used to give a seismic hazard index (SHI) value to delineate various seismic hazard zones; namely very low, low, moderate, high, and very high.     

Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model

This paper presents the application of remote sensing techniques, digital image analysis and Geographic Information System tools to delineate the degree of landslide hazard and risk areas in the Balik Pulau area in Penang Island, Malaysia. Its causes were analysed through various thematic attribute data layers for the study area. Firstly, landslide locations were identified in the study area from the interpretation of aerial photographs, satellite imageries, field surveys, reports and previous landslide inventories. Topographic, geologic, soil and satellite images were collected and processed using Geographic Information System and image processing tools. There are 12 landslide-inducing parameters considered for the landslide hazard analyses. These parameters are: topographic slope, topographic aspect, plan curvature, distance to drainage and distance to roads, all derived from the topographic database; geology and distance to faults, derived from the geological database; landuse/landcover, derived from Landsat satellite images; soil, derived from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value, derived from SPOT satellite images. In addition, hazard analyses were performed using landslide-occurrence factors with the aid of a statistically based frequency ratio model. Further, landslide risk analysis was carried out using hazard map and socio-economic factors using a geospatial model. This landslide risk map could be used to estimate the risk to population, property and existing infrastructure like transportation networks. Finally, to check the accuracy of the success-rate prediction, the hazard map was validated using the area under curve method. The prediction accuracy of the hazard map was 89%. Based on these results the authors conclude that frequency ratio models can be used to mitigate hazards related to landslides and can aid in land-use planning.     

Application to GF-2 Satellite for Road Landslides Monitoring

Global warming has profoundly changed extreme weather events,and remote sensing technology is gradually being applied to the monitoring of ecological and environmental disasters.China is one of the countries with most serious natural disasters in the world.With the development of human society and the improvement of people’s awareness of disaster risk,geological disaster monitoring and risk management has attracted more and more attention.Taking the Adjacent Area of Changsha \| Xiangtan(CXAA),which is the main part of Xiangtan jiuhua economic development zone,as the research area,the road slope and the ecological environment in the area were monitored by using the fusion image of high spatial resolution remote sensing of GF\|2 made in China on March 27,2016.Taking normalized difference vegetation index(NDVI),terrain slope index,soil index and other parameters as inputs,the comprehensive evaluation of ecological environment in CXAA was obtained by simulating the comprehensive factors of ecological environment with the comprehensive index method.We found that the ecological environment of 78.75 % of the study area was good,which indicated that the natural ecological environment on both sides of the road and the surrounding areas was basically not damaged.The road surface,water area(such as Xiangjiang river),construction land(such as Xiangtan high\|speed railway north station) and industrial areas(such as Geely automobile,Tidfore enterprise group,etc.) and other regions of the ecological environment comprehensive index is poor,on both sides of the road in some slope sections still exist the potential risk of landslide.Therefore,we suggest that during the rainy season,there is a need for more time\|phase high spatial resolution of GF-2 remote sensing satellite and other domestic GF satellite continuous monitoring,in order to more fully understand the risk of road slope landslide and provide early warning.     

Landslide susceptibility mapping using downscaled AMSR-E soil moisture: A case study from Cleveland Corral, California, US

As soil moisture increases, slope stability decreases. Remotely sensed soil moisture data can provide routine updates of slope conditions necessary for landslide predictions. For regional scale landslide investigations, only remote-sensing methods have the spatial and temporal resolution required to map hazard increases. Here, a dynamic physically-based slope stability model that requires soil moisture is applied using remote-sensing products from multiple Earth observing platforms. The resulting landslide susceptibility maps using the advanced microwave scanning radiometer (AMSR-E) surface soil moisture are compared to those created using variable infiltration capacity (VIC-3L) modeled soil moisture at Cleveland Corral landslide area in California, US. Despite snow cover influences on AMSR-E surface soil moisture estimates, a good relationship between the downscaled AMSR-E's surface soil moisture and the VIC-3L modeled soil moisture is evident. The AMSR-E soil moisture mean (0.17 cm³/cm³) and standard deviation (0.02 cm³/cm³) are very close to the mean (0.21 cm³/cm³) and standard deviation (0.09 cm³/cm³) estimated by VIC-3L model. Qualitative results show that the location and extent of landslide prone regions are quite similar. Under the maximum saturation scenario, 0.42% and 0.49% of the study area were highly susceptible using AMSR-E and VIC-3L model soil moisture, respectively.     

Landslide mapping in the coastal area between the Strymonic Gulf and Kavala (Macedonia,Greece) with the aid of remote sensing and geographical information systems

The main aim of this study is to photointerpret land cover change along hill slopes in order to detect existing landslides with the aid of methodologies such as false colour composites (FCCs), principal component analysis (PCA) and the normalized difference vegetation index (NDVI). Then, by combining geological data (lithology, vegetation cover), and geomorphologic factors (slope, aspect, distance from the rivers), landslide susceptibility maps were produced. The region where the study took place was the coastal area between the Strymonic Gulf and southwest Kavala prefecture, which was selected because of its intense landslide activity. The identification of locations where landslides occurred was achieved with the use and processing of TERRA/ASTER satellite images, while the data, which were mainly collected from the digitization of contours from topographical maps at 1 : 50 000 scale, were used to construct the final landslide susceptibility maps. The resulting FCC images provided satisfactory information about the locations of landslide sites and the landslide susceptibility maps indicated areas that were more prone to produce such phenomena.     

Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data

The aim of this study is to evaluate the hazard of landslides at Penang, Malaysia, using a Geographical Information System (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and from field surveys. Topographical and geological data and satellite images were collected, processed and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land use from Thematic Mapper (TM) satellite images; and the vegetation index value from Système Probatoire de l'Observation de la Terre (SPOT) satellite images. Landslide hazardous areas were analysed and mapped using the landslide‐occurrence factors by logistic regression model. The results of the analysis were verified using the landslide location data and compared with probabilistic model. The validation results showed that the logistic regression model is better in prediction than probabilistic model.     

ALOS/PALSAR full polarimetric observations of the Iwate–Miyagi Nairiku earthquake of 2008

We analysed Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data on the areas damaged by the Iwate–Miyagi Nairiku earthquake that struck Japan in 2008. The observations before and after the earthquake have been carried out in the full polarimetric mode. We observed the dominance of surface scattering of the three-component scattering model in the landslide areas and identified 11 of the 13 landslide areas. However, we also detected vacant pieces of land, pastures and other land bodies. The possible landslide areas are estimated for 102 patch areas, of which 36 correspond to the actual landslide areas. This method is useful to detect the landslide area when the land classification map or optical image taken before a disaster is available. We propose the use of σ⁰ _VH information to distinguish the landslide areas from the other areas. Since σ⁰ _VH is sensitive to the surface roughness of an area, vacant pieces of land and pastures, which have a relatively low surface roughness, can be distinguished from the landslide areas, which have a high surface roughness. By combining the surface scattering and the σ⁰ _VH filter, the number of possible landslide areas is reduced from 102 to 54, which include the actual landslide areas except for some small patch areas.     

Application of remote sensing and GIS for the demarcation of groundwater potential zones of a river basin in Kerala,southwest coast of India

An integrated hydrogeological investigation has been made to delineate the groundwater‐potential zones of the Muvattupuzha river basin, Kerala, along the southwest coast of India. The basin is characterized by charnockites and gneisses of Archean age covering more than 80% of the area and the remaining by Pleistocene laterites and Miocene formation. The basin receives high rainfall, measuring 3100 mm/year. However, acute water shortage occurs during the premonsoon season and hence, a number of dug wells are made to tap the groundwater. Seasonal rainfall during NE and SW monsoons is the major source of groundwater recharge. Further, hydrogeomorphology, geology, fracture systems and the slope of the terrain also play a significant role on the movement and behaviour of the groundwater of this basin. The integration of conventional and remote sensing data has been made through geographic information system (GIS) and it is found that about 50% of the area can be identified as very good or good potential zones, whereas the remaining area falls under moderate and poor categories. Most of the Muvattupuzha sub‐basin and the western part of the Kothamangalam and Kaliyar sub‐basins are classified as good groundwater‐potential zones, although the eastern upstream part of the basin has poor groundwater potential.     

Development of time-variant landslide-prediction software considering three-dimensional subsurface unsaturated flow

An accurate landslide-susceptibility assessment is fundamental for preventing landslides and minimizing damage. In this study, a new time-variant slope-stability (TiVaSS) model for landslide prediction is developed. A three-dimensional (3D) subsurface flow model is coupled with the infinite slope-stability model to consider the effect of horizontal water movement in the subsurface. A 3D Richards' equation is solved numerically for the subsurface flow. To overcome the massive computational requirements of the 3D subsurface flow module, partially implicit temporal discretization and the simplification of first-order spatial discretization are proposed and applied in TiVaSS. A graphical user interface and two-dimensional data visualization are supported in TiVaSS. The model is applied to a 2011 Mt. Umyeon landslide in the Republic of Korea, and its overall performance is satisfactory.     

基于数字高程模型的古滑坡区提取

针对古滑坡的滑前影像无法获得,植被、纹理信息都已恢复,无法通过对比分析滑坡滑动前后的植被、纹理等信息的变化来提取滑坡区域的问题,提出了一种新的基于数字高程模型的滑坡区域范围提取方法。该方法基于简化的滑坡体模型及特征分析,对滑坡区进行水流方向、坡度、山脊山谷线提取,通过流域分析获取滑坡区域范围;利用坡度图实现滑坡壁与滑坡体提取。实验利用全球30m分辨率ASTER GDEM数据,提取了四川理县3个古滑坡体区域范围,验证了该方法的有效性。     

Object-oriented Detection of Landslides based on the Spectral，Spatial and Morphometric Properties of Landslides

Earthquakes in mountain area often induce hundreds of thousands of landslides resulting in destructive casualties and economic damage.It is urgent needed to rapidly detect the extent areas of the landslides.With the advent of very high resolution satellite remote sensing,the application of the object\|oriented classification method in this area have significant advantage comparing to those of visual interpretation and pixel\|based methods.However,the study of object\|oriented landslide detection is relatively few,and the study usually has a small study area.The method of object\|oriented rapid identification of landslides based on the spectral,spatial and morphometric properties of landslides and a 2.5m SPOT5 multi\|spectral image is proposed in this paper and is applied in a relatively large study area.The normalized difference vegetation index (NDVI) threshold was set to remove vegetation objects and obtain landslide candidates.Then,the spectral characteristics,texture,terrain features and context of the image were used to build indicators to gradually separate the landslide from false positives.The small scale chessboard segmentation was conducted to further eliminate vegetation objects and get the landslide objects.The object\|oriented detection results show that the adopted method can recognize about 95% of the landslides in the study area.When considering the landslide excessive detection and omissions,the landslide detection quality percentage of the proposed method is 74.04%.Hence,the method proposed in the article can help to rapid assess landslide disasters caused by earthquakes or heavy rainfalls,providing a reference for post\|disaster emergency relief and reconstruction work.     

基于迁移学习的低空摄影测量滑坡方量估算方法

针对现有方法难以准确地估算山体滑坡体积的问题,引入人工智能算法,提出耦合迁移学习与微分算法的低空摄影测量山体滑坡方量估算方法。首先,利用SfM与SGM密集匹配等算法从低空无人机立体影像中解算出高精度三维密集点云,结合可见光植被指数和双边滤波算法从密集点云中剥离出目标区地面点云;然后,构建深度神经网络插值模型来表征二维坐标与高程之间的非线性映射关系,并基于参数共享的迁移学习来自适应优化深度神经网络以实现滑坡目标区高程值预测,进而重构滑坡区域的数字地表模型;最后,基于目标区滑坡前后数字地表模型高程差值和微分算法实现山体滑坡方量估算。实验结果表明,该方法平均相对误差为2.7%,相比常用的方法,显著提高了滑坡方量估计精度,并能适应不同地形条件下滑坡方量估算。     

Estimating structural properties of riparian forests with airborne lidar data

Riparian forest zones adjacent to surface water such as streams, lakes, reservoirs and wetlands maintain significant forest ecosystem functions including nutrient cycling, vegetative communities, water quality, fish and wildlife habitat and landscape aesthetics. In order to support the sustainable management of riparian forests, riparian zones should first be carefully delineated and then structural properties of riparian vegetation, especially forest trees, should be accurately measured. Geographical information system (GIS) techniques have been previously implemented to determine riparian zones quickly and reliably. However, basic measurements of forest structures in riparian areas have relied heavily on field-based surveys, which can be extremely time consuming in large areas. In this study, riparian forest zones were initially located using GIS techniques and then airborne lidar (light detection and ranging) data were used to determine and analyse structural properties (i.e. tree height, crown diameter, canopy closure and vegetation density) of a sample riparian forest. Lidar-derived tree height and crown diameter measurements of sample trees were compared with field-based measurements. Results indicated that 77.92% of the riparian area in the study area was covered by forest. Based on lidar-derived data, the average tree height, total crown width, canopy closure (above 3 m) and vegetation density (3–15 m) were found to be 74.72 m, 16.82 m, 71.15% and 26.05%, respectively. Although we found differences between measurement methods, lidar-derived riparian tree measurements were highly correlated with field measurements for tree height (R ²?=?88%) and crown width (R ²?=?92%). Differences between measurement methods were likely a result of difficulties associated with field measurements in the dense vegetation that is often associated with forested riparian areas.