Clément Bourgoin
email: bourgoin.clement2@gmail.com

Julie Betbeder

Pierre Couteron

Lilian Blanc

Hélène Dessard

Johan Oszwald

Renan Le Roux

Louis Reymondin

Lucas Mazzei

Plinio Sist

Guillaume Cornu

Peter Läderach

Valéry Gond

UAV-based canopy textures assess changes in forest structure from long-term degradation

Keywords: Canopy structure, Forest degradation, Remote Sensing, Texture, Tropical forest, Unmanned Aerial Vehicle

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Introduction

Forest degradation is a threat [START_REF] Potapov | The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013[END_REF] to the provision of ecosystem services by tropical forests. Degradation causes loss of biodiversity through habitat disturbance and fragmentation [START_REF] Barlow | Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation[END_REF][START_REF] Broadbent | Forest fragmentation and edge effects from deforestation and selective logging in the Brazilian Amazon[END_REF], erosion of hydrological and soil properties, the reduction of non-timber forest resources [START_REF] Lewis | Increasing human dominance of tropical forests[END_REF][START_REF] Thompson | Forest resilience, biodiversity, and climate change: a synthesis of the biodiversity, resilience, stabiblity relationship in forest ecosystems[END_REF], and currently accounts for 68.9% of overall carbon losses from tropical forests [START_REF] Baccini | Tropical forests are a net carbon source based on aboveground measurements of gain and loss[END_REF].

The accumulation of forest disturbances such as selective logging and understory fires affects the states of the forest by destroying the canopy and the internal structure without triggering any changes in land use [START_REF] Ghazoul | Degradation and Recovery in Changing Forest Landscapes: A Multiscale Conceptual Framework[END_REF][START_REF] Putz | The Importance of Defining 'Forest': Tropical Forest Degradation, Deforestation, Long-term Phase Shifts, and Further Transitions: Importance of Defining 'Forest[END_REF]. Degraded forests are therefore the consequence of complex degradation and recovery processes, which creates a gradient of varying structures within the forest landscape [START_REF] Chazdon | When is a forest a forest? Forest concepts and definitions in the era of forest and landscape restoration[END_REF][START_REF] Malhi | Tropical Forests in the Anthropocene[END_REF].

Measuring the current forest structure and its degree of degradation are crucial for effective but sustainable management of degraded forests to guarantee the conservation, management and betterment of their ecological values [START_REF] Goldstein | The Afterlives of Degraded Tropical Forests: New Value for Conservation and Development[END_REF].

However, the identification, characterization and measurement of forest degradation remains a scientific challenge, in particular in the remote sensing community [START_REF] Frolking | Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure[END_REF][START_REF] Herold | Options for monitoring and estimating historical carbon emissions from forest degradation in the context of REDD+[END_REF][START_REF] Hirschmugl | Methods for mapping forest disturbance and degradation from optical earth observation data: A review[END_REF][START_REF] Mitchell | Current remote sensing approaches to monitoring forest degradation in support of countries measurement, reporting and verification (MRV) systems for REDD+[END_REF]. Among the wide range of remote sensing approaches, optical time series of medium resolution Landsat images have been used to derive forest states indicators and to reconstruct forest degradation history through the detection and quantification of disturbances within the canopy [START_REF] Asner | Automated mapping of tropical deforestation and forest degradation: CLASlite[END_REF][START_REF] Bullock | Monitoring tropical forest degradation using spectral unmixing and Landsat time series analysis[END_REF][START_REF] Devries | Tracking disturbance-regrowth dynamics in tropical forests using structural change detection and Landsat time series[END_REF][START_REF] Souza | Ten-Year Landsat Classification of Deforestation and Forest Degradation in the Brazilian Amazon[END_REF]. These approaches are steps towards degradation monitoring and informing Reducing Emissions from Deforestation and Degradation (REDD+) systems [START_REF] Goetz | Measurement and Monitoring for REDD+: The Needs, Current Technological Capabilities, and Future Potential[END_REF] but do not provide quantitative information on the forest structure which is directly related to carbon stocks. Airborne Light Detection and Ranging (A-LiDAR) is the most successful technique to retrieve three-dimensional forest structural parameters and estimate aboveground biomass (AGB) stocks [START_REF] Asner | A universal airborne LiDAR approach for tropical forest carbon mapping[END_REF][START_REF] Longo | Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon: AMAZON INTACT AND DEGRADED FOREST BIOMASS[END_REF][START_REF] Rappaport | Quantifying long-term changes in carbon stocks and forest structure from Amazon forest degradation[END_REF] but the data are often costly to acquire and to replicate both in space and over time [START_REF] Silva | Impacts of Airborne Lidar Pulse Density on Estimating Biomass Stocks and Changes in a Selectively Logged Tropical Forest[END_REF].

In addition to the spectral properties of optical remote sensing, Very High Spatial Resolution (VHSR) sensors (images with less than 5m/pixel) also acquire information on the distribution of dominant tree crowns that define the forest canopy and also canopy gaps, thereby providing important indirect indicators of forest three-dimensional structure [START_REF] Meyer | Canopy area of large trees explains aboveground biomass variations across neotropical forest landscapes[END_REF]. The spatial distribution of trees, the shapes and dimensions of their crowns and the characteristics of the inter-crown gaps interact to define the forest canopy grain and can be assessed through canopy texture analysis (Couteron et al., 2005). Several studies have demonstrated the potential of texture methods to characterize VHSR canopy images (Couteron et al., 2005;[START_REF] Frazer | Simulation and quantification of the finescale spatial pattern and heterogeneity of forest canopy structure: A lacunarity-based method designed for analysis of continuous canopy heights[END_REF]. Among them, the FOurier-based Textural Ordination (FOTO) method has been used in a variety of tropical forests to characterize gradients of canopy grain, heterogeneity and crown size distribution [START_REF] Barbier | The variation of apparent crown size and canopy heterogeneity across lowland Amazonian forests: Amazon forest canopy properties[END_REF][START_REF] Bastin | Aboveground biomass mapping of African forest mosaics using canopy texture analysis: toward a regional approach[END_REF]Couteron et al., 2005;[START_REF] Ploton | Assessing aboveground tropical forest biomass using Google Earth canopy images[END_REF][START_REF] Singh | Biomass estimation of mixed forest landscape using a Fourier transform texture-based approach on very-high-resolution optical satellite imagery[END_REF]. Case studies have shown that FOTO indices can correlate with forest structural parameters along gradients of natural variation (Couteron et al. 2005) of degradation [START_REF] Ploton | Assessing aboveground tropical forest biomass using Google Earth canopy images[END_REF][START_REF] Singh | Biomass estimation of mixed forest landscape using a Fourier transform texture-based approach on very-high-resolution optical satellite imagery[END_REF] or in landscapes mixing both [START_REF] Bastin | Aboveground biomass mapping of African forest mosaics using canopy texture analysis: toward a regional approach[END_REF][START_REF] Ploton | Toward a general tropical forest biomass prediction model from very high resolution optical satellite images[END_REF]. Lacunarity analysis, another textural approach, also captures spatial heterogeneity of forest canopies and additionally provides a quantitative measure of canopy 'gapiness' that correlates with canopy cover and gap fraction [START_REF] Frazer | Simulation and quantification of the finescale spatial pattern and heterogeneity of forest canopy structure: A lacunarity-based method designed for analysis of continuous canopy heights[END_REF][START_REF] Malhi | Analysis of lacunarity and scales of spatial homogeneity in IKONOS images of Amazonian tropical forest canopies[END_REF][START_REF] Ploton | Toward a general tropical forest biomass prediction model from very high resolution optical satellite images[END_REF]. However, the possible links between canopy texture and forest structure parameters are context dependent [START_REF] Ploton | Toward a general tropical forest biomass prediction model from very high resolution optical satellite images[END_REF], and relationships have to be verified and calibrated using reference data from either field plots or airborne canopy altimetry, and such data are not available in many tropical landscapes or regions. Moreover, one cannot expect the variety of stand structures generated by degradation processes to display unequivocal relationships with canopy texture variables [START_REF] Rappaport | Quantifying long-term changes in carbon stocks and forest structure from Amazon forest degradation[END_REF]. For instance, severe degradation may result in coarse texture (e.g. because of big gaps) as well as fine-grained aspects owing to small crowns in regenerating patches. In this sense, there is a lack in understanding and quantifying the consequences of forest degradation on canopy texture. Using unmanned aerial vehicles (UAV), the aim of this paper is to demonstrate that texture information can efficiently characterize degraded forest types. Unmanned aerial vehicles are thus a new promising tool to acquire altimetry data and very high resolution images of the canopy [START_REF] Ketchen | The Application Of Cluster Analysis In Strategic Management Research: An Analysis And Critique[END_REF][START_REF] Zhang | Seeing the forest from drones: Testing the potential of lightweight drones as a tool for long-term forest monitoring[END_REF].

Here, we used very high resolution UAV images to sample a broad range of degraded and intact forests conditions in an old deforestation pioneer front of the Brazilian Amazon. For each forest site, we combined degradation history from Landsat time series with UAV data including canopy elevation and grey-level images. Our large-area and diverse UAV coverage addressed two questions: (1) How do canopy textures correlate with forest structure parameters within a large range of degraded forest types? (2) How do disturbance type and frequency contribute to variability in texture metrics through heterogeneity, coarseness and openness canopy gradients?

In so doing, our study aims to pave the way for interpreting canopy texture in VHSR satellite images from agile UAV-based ground truthing and consequently help decision makers improve the management of degraded forests.

Materials and Methods

Study area

The study was carried out in the municipality of Paragominas, located in the northeastern part of the State of Pará, Brazil, and covered an area of 19,342 km² (Fig. 1). The municipality experienced different colonization processes since its foundation in 1965, which led to significant deforestation with conversion of land to pasture for cattle ranching and forest degradation through overexploitation of timber. Deforestation was accentuated by the grain agro-industry in the 2000s, dominated by intensive soybean and maize cultivation mainly in the center of the municipality (Piketty et al., 2015).

We demonstrated in previous studies that the history and processes of colonization spatially differ within Paragominas [START_REF] Laurent | Le tournant environnemental en Amazonie : ampleur et limites du découplage entre production et déforestation[END_REF]. This led to a mosaic of forests in very different cover states within heterogeneous landscape mosaics dominated by different land uses [START_REF] Bourgoin | The Potential of Multisource Remote Sensing for Mapping the Biomass of a Degraded Amazonian Forest 21[END_REF][START_REF] Mercier | Evaluation of Sentinel-1 and 2 Time Series for Land Cover Classification of Forest-Agriculture Mosaics in Temperate and Tropical Landscapes 20[END_REF]. In this region, forest management plans with selective logging have rarely been adopted except in CIKEL Brasil Verde Madeiras Ltda forestry company [START_REF] Mazzei | Above-ground biomass dynamics after reduced-impact logging in the Eastern Amazon[END_REF]. Forest suffered from two major anthropogenic disturbances. Unplanned logging with over-logging intensity is marked by repeated frequencies over time. Fire alters deeply the understory and generate high mortality rates for canopy trees (Fig. 1) [START_REF] Hasan | Cumulative disturbances to assess forest degradation using spectral unmixing in the northeastern Amazon[END_REF][START_REF] Tritsch | Multiple Patterns of Forest Disturbance and Logging Shape Forest Landscapes in Paragominas, Brazil[END_REF]. Forty forest sites were selected in various forested landscapes to cover a large variation of disturbance types (Fig. 1). Using visual interpretation from Google Earth® VHSR images validated by in-situ UAV observations, we distinguished between sites that experienced disturbances such as logging and fire and intact sites with no human-induced disturbance. We also used the management plan of the Cikel forestry company (Fig. 1) that provides spatial information on undisturbed and selectively logged forests at different dates over the last 20 years.

We used a DJI mavic pro UAV carrying its original RGB camera of 12.71 megapixel resolution (DJI, Shenzhen, China). The acquisition plan was designed with Pix4D Capture software (Pix4D, Lausanne, Switzerland). We used a single grid with 80% of front and side overlap between images and a constant flight altitude of 300 meters above ground level. The objective was to maximize the overlap between each image and the total surface area mapped. As a result, the average surface mapped in each forest plot was 24 ha (~600 by 400 meters) at 10 centimeters spatial resolution (Appendix C) for a total of 739 ha. In order to generate a high quality canopy height model, each flight was constrained by several conditions: i) Flat terrain was selected with imaged areas that overlapped with roads or agricultural fields to allow us to retrieve the ground elevation during the preprocessing step;

ii) Acquisition in the morning (9 to 11 am) and afternoon (3 to 5 pm) was preferred to avoid zenithal effects (halo and low image contrast); iii) Either cloud free or totally cloudy sky conditions were necessary to avoid cloud shadows; iv) Absence of wind to low wind conditions were needed to generate crisp images of the forest canopy.

Raw image data were processed to the highest density point cloud using structure from motion (SfM) followed by densification using multi-view stereo algorithms in the Pix4D software [START_REF] Alonzo | Quantifying Boreal Forest Structure and Composition Using UAV Structure from Motion[END_REF][START_REF] Westoby | Structure-from-Motion' photogrammetry: A low-cost, effective tool for geoscience applications[END_REF]. Final point cloud densities were ~27 pts.m-3 depending on the availability of viable tie points, and some other acquisition parameters (Table 1). Using the georectified point clouds, we corrected the raw images to generate RGB mosaics, which were then converted into single-band panchromatic grey level mosaics. Digital Surface Models (DSM) of the canopy (i.e. the top of the forest's surface) at 0.10 m resolution were directly computed from the point cloud. We extracted the average ground elevation data in non-forest areas (e.g. roads, agricultural fields or canopy gaps) from the DSMs and derived canopy height models for each forest plot.

Landsat time series to detect forest disturbances and reconstruct degradation history

We acquired Landsat data from 1984 to 2017 (Appendix A) to detect forest disturbances along time and reconstruct degradation history for each forest site. The images at Level 1 (Tier 1 product) were pre-processed to surface reflectance by the algorithm developed by the NASA Goddard Space Flight Center (http://earthexplorer.usgs.gov/). We computed the Normalized Difference Moisture Index (NDMI) from the Short-Wave InfraRed (SWIR) and Near InfraRed (NIR) bands as follows [START_REF] Gao | NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space[END_REF]:

𝑁𝐷𝑀𝐼 = 𝑁𝐼𝑅 -𝑆𝑊𝐼𝑅 𝑁𝐼𝑅 + 𝑆𝑊𝐼𝑅 ⁄
This index previously used to monitor forest degradation [START_REF] Devries | Tracking disturbance-regrowth dynamics in tropical forests using structural change detection and Landsat time series[END_REF] allowed us to identify disturbance type and frequency (selective logging, over-logging and fire) at forest plot scale, using photointerpretation (Appendix B). The disturbance type was identified based on its spatial extent and shape and on the low NDMI values. Selective logging is marked by regular and spaced logging roads (Fig. 1A), over-logging is marked by irregular logging roads (Fig. 1B) and fire presents open canopy structure and low values of NDMI [START_REF] Silva | Dynamics of forest fires in the southwestern Amazon[END_REF][START_REF] Tritsch | Multiple Patterns of Forest Disturbance and Logging Shape Forest Landscapes in Paragominas, Brazil[END_REF]. We also recorded the date of the most recent disturbance (Appendix C) which has a significant influence on the current forest structure [START_REF] Rappaport | Quantifying long-term changes in carbon stocks and forest structure from Amazon forest degradation[END_REF]. Figure 2 shows the diversity of forest degradation history of our sampling such as selectively logged forests, over-logged forests and over-logged and burned forests.

Methods

The data analysis was based on two steps: (1) use canopy texture metrics derived from grey-level UAV images to retrieve canopy structure metrics (based on canopy height models) derived from UAV Selective logging events Over-logging events Fires events Last disturbance date structure from motion within a large range of degraded forest types at 1 ha scale and (2) potential of canopy texture metrics to discriminate degradation history and the resulting changes in forest structures at the forest plot scale (Fig. 3).

Figure 3: Workflow of the method used to evaluate the potential of canopy texture metrics to retrieve the canopy structure along the gradient of forest degradation and their relation with forest degradation history.

Computation of forest canopy texture metrics from grey level UAV images at 1 ha scale

We performed texture analysis of grey level canopy images using FOTO [START_REF] Couteron | Quantifying change in patterned semi-arid vegetation by Fourier analysis of digitized aerial photographs[END_REF] and lacunarity [START_REF] Frazer | Simulation and quantification of the finescale spatial pattern and heterogeneity of forest canopy structure: A lacunarity-based method designed for analysis of continuous canopy heights[END_REF] algorithms. We also used basic descriptors of statistical grey level distributions such as skewness and kurtosis. Each of the UAV canopy images was divided into canopy 100*100 m windows (fixed grid) for texture analysis. This size was shown in previous studies to be appropriate to capture several repetitions of the largest tree crowns (in our case 45 meters of maximum tree crown diameter) in forest stands [START_REF] Ploton | Toward a general tropical forest biomass prediction model from very high resolution optical satellite images[END_REF].

The FOTO method is extensively described elsewhere [START_REF] Couteron | Quantifying change in patterned semi-arid vegetation by Fourier analysis of digitized aerial photographs[END_REF]Couteron et al., 2005;[START_REF] Ploton | Toward a general tropical forest biomass prediction model from very high resolution optical satellite images[END_REF], hence we only give here a brief outline of the procedure. When applying FOTO, each of the windows originating from the UAV images is subjected to a two-dimensional Fourier transform to enable computation of the two-dimensional periodogram. 'Radial-' or 'r-spectra' are extracted from the periodogram to provide simplified, azimuthally-averaged textural characterization. Spectra are systematically compared using the two first axes of a principal component analysis (FOTO_PCA1, FOTO_PCA2), providing an ordination along a limited number of coarseness vs. fineness gradients. In this process, windows are treated as statistical observations that are characterized and compared on the basis of their spectral profile, i.e., the way in which window grey scale variance is broken down in relation to Fourier harmonic spatial frequencies (ranging from 50 to 240 cycles/km for this study). PCA captures gradients of variation between windows spectra opposing those concentrating most variance in low frequencies (i.e. coarse textures) and those in which high frequencies retain a substantial share of variance (i.e. fine textures).

Lacunarity was defined following [START_REF] Frazer | Simulation and quantification of the finescale spatial pattern and heterogeneity of forest canopy structure: A lacunarity-based method designed for analysis of continuous canopy heights[END_REF] and Malhi and Roman-Cuesta (2008). For each 100*100 m window, a moving square box of size 's' was glided by one pixel at a time and the sum of all pixel spectral radiance, called the mass, was computed at each gliding position. The frequency distribution of the mass divided by the number of boxes' positions is computed, and Lacunarity at box size 's' is the squared ratio of the first and second moment of this distribution. This process was repeated for 100 box sizes ranging from 1 to 99 m and the resulting lacunarity spectrum was normalized by lacunarity at size 1. Finally, the spectra were compared using the two first axes of a PCA (Lacu_PCA1 and Lacu_PCA2 respectively), to provide an ordination of windows along inter-crown canopy openness gradients. The ability of CTM to predict forest canopy structures was tested using regression models for each CSM based on Random Forest machine learning (RF) [START_REF] Breiman | Random forests[END_REF].

The learning set is randomly partitioned into k equal size sub-samples with k=10. Each regression process is then applied where k-1 sub samples are used as training data and the remaining ones for validation. This process is repeated by changing the training/validation sub-samples in such a way that all learning samples are used for validation. Cross-validation is a common and sound procedure in machine learning processes [START_REF] Arlot | A survey of cross-validation procedures for model selection[END_REF][START_REF] Kohavi | A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection 7[END_REF]. The R-squared, average Root Mean Square Error (RMSE) and relative RMSE were utilized to evaluate the performance of the model.

The number of trees and the number of variables used for tree nodes splitting were randomly determined using the tune function implemented in the R randomForest package, version 4.6-14 [START_REF] Liaw | Classification and regression by randomForest[END_REF]. The number of tree was set to 500 to reduce computation times without notable loss in accuracy.

Potential of canopy texture metrics to discriminate forest degradation histories at the plot scale

The forest plot scale was used to combine canopy texture and canopy structure metrics with forest degradation history. We first classified forest plots according to their canopy structures (mean CSM calculated at the plot scale) using PCA and hierarchical clustering (Ward's criterion). The number of clusters was optimized by calculating the inter-cluster variance [START_REF] Ketchen | The Application Of Cluster Analysis In Strategic Management Research: An Analysis And Critique[END_REF]. Each cluster of forest canopy structure was then related to forest degradation history by calculating the average disturbance frequency of over-logging, selective logging and fire events. We then used Linear Discriminant Analysis (LDA) to predict membership of forest structure clusters from averaged value of CTM at the plot scale. LDA algorithm tries to find a linear combination within the canopy textural metrics averaged at plot scale that maximizes separation between the barycenters of the clusters while minimizing the variation within each group of the dataset [START_REF] Hamsici | Bayes Optimality in Linear Discriminant Analysis[END_REF][START_REF] Kuhn | Applied predictive modeling[END_REF]. We used MANOVA with Pillai's Trace tests to evaluate the significance of the multivariate inter-cluster difference computed from the 6 CTM. All processes were computed using the R packages FactoMineR [START_REF] Husson | Principal component methods -hierarchical clustering -partitional clustering: why would we need to choose for visualizing data? 17[END_REF][START_REF] Lê | FactoMineR : An R Package for Multivariate Analysis[END_REF] and the MASS package [START_REF] Venables | Modern Applied Statistics with S 504[END_REF].

Results

Forest canopy texture metrics from grey level UAV images at 1 ha scale

The two first factorial axes of the PCA accounted for 51.6% of the total variability of the r-spectra observed (Fig. 4b). FOTO_PCA1 expresses a gradient between coarse and fine texture corresponding to spatial frequencies of less than 90 cycles/km and more than 120 cycles/km, respectively (Fig. 4c).

FOTO_PCA2 expresses a gradient leading from heterogeneous textures with the coexistence of low and high frequencies (negative scores) toward homogeneous intermediate frequencies in the range 90-120 cycles/km (high scores). Fine textures correspond to homogeneous distribution of small tree crowns reflecting ongoing regeneration after probable over-logging. Intermediate textures along axis 1 associate large and smaller tree crowns that characterize preserved forest with the natural distribution of high emergent trees and lower canopy trees. The left part of the scatter plot groups coarse textures corresponding to large gaps in the canopy related to logging activities. Finally, the two examples at the bottom of the plot show mixed coarse (remaining trees) and fine (low understory or shrub stratum) textures that characterize over-logged and recently burned forests. The first factorial axe of the PCA on the lacunarity spectra account for more than 70% of total variability (Fig. 5b). Lacu_PCA1 expresses a gradient of gapiness with large gaps appearing in the extreme left part of the scatter plot (Fig. 5a) and closed canopy forest with no gaps in the extreme right part. Large gaps are tree shadows projected over large canopy gaps. Homogeneous canopies, i.e.

smooth grain and a closed canopy characterizing low degradation forests were found on the positive side of the second axis (Lacu_PCA2)(11% of total variability) and vice versa for heterogeneous canopies. These are highly degraded forests (over-logged at different ages and recently burned forests) with destroyed canopies and patches of small crowns linked to the understory or to regeneration. Other axes did not reveal other structures. Substantial analogy can be observed between the main texture gradients provided by FOTO and by the lacunarity analyses.

Relationships between canopy textures and forest structure parameters at 1 ha scale

The standard deviation and variance of canopy height were the CSM best explained by texture with a R² of 0.58 and 0.54 respectively (Table 1). These metrics pointing to the variability of canopy structure directly reflect the different processes of degradation and the associated gradients of canopy grain texture. Maximum and mean canopy height and coefficient of variation show lower relationship (resp.

R² of 0.43, 0.38 and 0.31). The minimum height showed a low R² of 0.13 with CTM. The clustering method allowed identifying six clusters of canopy structures (Fig. 6). Cluster 1 groups wide open and low canopy forests with a significantly lower average canopy height (9.9 m) than the other clusters and high Standard Deviation (SD) values (6.19 m). It groups forest plots that have mainly experienced over-logging (~1.4 events) and recent fire events identified between 2015 and 2017 (1.6 in average). Cluster 2 groups 23-year-old secondary forests characterized by a homogeneous and low canopy (average height of 13.86m and SD of 2.28). Cluster 3 groups forest plots with homogeneous (SD of 5.10), low average canopy height (14.13m) mainly marked by over-logging (~1.5 events). Cluster 4 has a heterogeneous canopy structure characterized by high standard deviation (SD of 7.03) which is explained by recent logging events detected in 2017 (~1.2 events) and other previous disturbances such as fire (~1 event). Clusters 5 and 6 have similar canopy height (~22 m) but variable canopy roughness (SD ranging from 6.05 to 7.59). Their degradation histories differ as cluster 5 groups recently selectively logged forest (~0.6 events) or over-logged forests (~1 event) while cluster 6 mostly groups undisturbed forest and old selectively logged forests (more than 10 years ago). However both clusters are marked by very low (~0.1 events for cluster 5) to none fire disturbances detected. Further explanation on the different steps of the method and on the statistical results can be found in Appendix D and E.

Linear discriminant analysis at the forest plot scale

The two first discriminant components (LD1 and LD2) account for 93.6% of the total proportion of the trace i.e. the proportion of inter-cluster discrimination of the LDA based on texture (MANOVA test with p-value < 0.05)(Fig. 7a). The prominent first discriminant component is mainly correlated with FOTO_PCA1 (r=0.55 in LD1), FOTO_PCA2 (0.49 in LD1) and LACU_PCA2 (-0.79 in LD1) (Fig. 7b). The second discriminant component is mainly correlated with LACU_PCA2.

In the LD1-LD2 plane, clusters 2 and 3 are mainly separated from the rest of clusters thanks to axis LDA1. Cluster 1, 4 and 5 are discriminated along LD2. Cluster 6 has less discriminating capacity, especially compared with cluster 5.

LDA results include misclassification errors corresponding to disagreement between texture-based and structural classifications of the plots (Fig. 7a). The confusion matrix shows an overall accuracy of 55% and kappa index at 0.44 (Fig 7c). The LDA classification performed well for all clusters except cluster 6 which has the highest misclassification rate with a high rate of confusion with cluster 5. Based on CTM, clusters 5 and 6 appeared to be similar because they mainly differ in their minimum height, which logically is difficult to predict from canopy texture metrics on 2D images.

Discussion

A better characterization of forest structure is crucial to tailoring forest management plans [START_REF] Goldstein | The Afterlives of Degraded Tropical Forests: New Value for Conservation and Development[END_REF]. In this paper, we show that canopy texture metrics extracted from very-high spatial resolution optical images acquired by unmanned aerial vehicle are clearly related to forest canopy height models and can reveal different and complex degradation history. The canopy texture metrics provide complementary information on degraded forest states compared to other remotely sensed indicators based on vegetation photosynthesis activity [START_REF] Asner | Automated mapping of tropical deforestation and forest degradation: CLASlite[END_REF][START_REF] Bullock | Monitoring tropical forest degradation using spectral unmixing and Landsat time series analysis[END_REF][START_REF] Mitchell | Current remote sensing approaches to monitoring forest degradation in support of countries measurement, reporting and verification (MRV) systems for REDD+[END_REF]. The canopy texture metrics can also be used in multidisciplinary approaches such as for the assessment of forest ecosystem services that require detailed information on forest structure [START_REF] Barlow | Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation[END_REF][START_REF] Berenguer | A large-scale field assessment of carbon stocks in human-modified tropical forests[END_REF].

Potential of canopy texture metrics to assess degraded canopy structures

We showed that CTM can assess forest structure variability that reflects both horizontal and vertical heterogeneity induced by degradation. Through the expression of canopy grain and heterogeneity gradients, CTM provides reliable estimations of canopy roughness (standard deviation of canopy height) at 1 ha-scale. At the forest plot scale, we demonstrated the complementary of FOTO and lacunarity metrics in distinguishing between the different clusters. While FOTO and lacunarity express similar gradients of canopy texture (Figs. 4 and5), the measure of canopy openness in lacunarity provide useful additional information to distinguish large canopy gaps from large crowns, as underlined by [START_REF] Ploton | Toward a general tropical forest biomass prediction model from very high resolution optical satellite images[END_REF]. However, the first axis of lacunarity reveals a gradient of canopy textures that could be influenced by sun-angle conditions during the UAV data acquisition. Early morning and late afternoon data acquisitions generate higher projected shadow, which drives the distribution of the data towards the negative values of the first axis. Data acquisition parameters (sun and sensor angles, clouds etc.) are known to be able to disturb grey level values and textures [START_REF] Barbier | Attenuating the bidirectional texture variation of satellite images of tropical forest canopies[END_REF]. One advantage of using UAV is that they allow better control of acquisition conditions than do satellites.

Long-term forest degradation consequences on structure explained using current canopy textures

In this study, we demonstrate the potential of single shot UAV-based canopy altimetry and texture to correlate with current structure that can reveal both past disturbances and recovery processes (Herold et al. 2011, Ghazoul and[START_REF] Ghazoul | Degradation and Recovery in Changing Forest Landscapes: A Multiscale Conceptual Framework[END_REF]. At the forest plot scale (~24 ha), the average CSM and the variability of CSM enabled the identification of six forest clusters with specific degradation history. The link between long term degradation history and canopy structure has already been identified in previous studies that quantified carbon densities of Amazonian forests following successive logging and/or fire events [START_REF] Berenguer | A large-scale field assessment of carbon stocks in human-modified tropical forests[END_REF][START_REF] Longo | Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon: AMAZON INTACT AND DEGRADED FOREST BIOMASS[END_REF][START_REF] Rappaport | Quantifying long-term changes in carbon stocks and forest structure from Amazon forest degradation[END_REF]. CTM proved to be able to differentiate between five types of degradation, although with some confusion between the less degraded forest types.

Undisturbed forests have a high closed canopy, and are homogeneous in texture with an intermediate grain associating large and medium-sized tree crowns (cluster 6). Any logging event will disrupt the canopy and create a coarse texture with greater variation in canopy height (cluster 4). Under a management plan, logging intensity is moderate (< 5 to 8 trees/ha) and the recovery time (35 years in Brazil) is expected. In that case, the coarse texture will recover and will turn back into intermediate grain (cluster 5 or 6). We showed that after seven years, the canopy texture of a logged forest resembles that of undisturbed forest (Fig. 6 and Appendix C). For unmanaged forests, subject to higher uptake, the time for the forest to recover a canopy texture of intermediate grain will be longer (e.g. plots 23 and 24). In the case of additional impacts (progressive disappearance of large crowns), the coarse canopy texture will also be maintained longer. Repetitive and intense logging have therefore triggered the complete harvesting of emergent trees, thereby weakening the capacity of forests to cope with further disturbances [START_REF] Asner | Remote sensing of selective logging in Amazonia: Assessing limitations based on detailed field observations, Landsat ETM+, and textural analysis[END_REF]. During the recovery process, canopy is of low height and its texture is dominated by a fine and heterogeneous grain (cluster 3).

Additionally, we found that recent fire (single or multiple events) has a major impact on the damaged forest structure. The resulting highly degraded forests are characterized by coarse textures corresponding to large gaps and/or homogeneous regeneration stratum and highly damaged canopy (cluster 1). Moreover, most fires were detected in 2015, which correlated with the El Nino drought event [START_REF] Berenguer | Tree growth and stem carbon accumulation in human-modified Amazonian forests following drought and fire 8[END_REF]. These findings underline the importance of the synergetic effects of logging and fire on forest structure [START_REF] Dwomoh | Forest degradation promotes fire during drought in moist tropical forests of Ghana[END_REF][START_REF] Morton | Understorey fire frequency and the fate of burned forests in southern Amazonia[END_REF]. We also found that fire was never detected (i.e. did not occur) in closed canopy and relatively low degradation forests.

This confirms that heavily logged forests are more vulnerable to fire due to the presence of dead and dry vegetation resulting from logging. At a larger scale, this vulnerability is linked with the fragmentation of forest patches, which facilitates access to forest resources, accentuates dry edge effects and increases potential pressure caused by agricultural expansion [START_REF] Briant | Habitat fragmentation and the desiccation of forest canopies: A case study from eastern Amazonia[END_REF][START_REF] Broadbent | Forest fragmentation and edge effects from deforestation and selective logging in the Brazilian Amazon[END_REF][START_REF] Silva Junior | Deforestation-Induced Fragmentation Increases Forest Fire Occurrence in Central Brazilian Amazonia[END_REF]. The mitigation of fire occurrence through improved landscape management is therefore a priority in order to prevent further degradation and forest carbon losses [START_REF] Berenguer | A large-scale field assessment of carbon stocks in human-modified tropical forests[END_REF].

The results of the present study reveal possible ways to address questions concerning the future management of primary degraded forests by analyzing the structure and texture of their canopy in order to better differentiate forest states and the associated degradation history. This study also opens the way for further analysis of secondary forests in abandoned lands which will certainly play a crucial role in future scenarios of landscape restoration [START_REF] Chazdon | When is a forest a forest? Forest concepts and definitions in the era of forest and landscape restoration[END_REF].

UAV technology: from data acquisition to limitations and perspectives

This paper reports the first large-scale application of low cost UAV to retrieve quantitative information on closed canopy forest structures. The UAV used is inexpensive (<2,000 US$) and a highly efficient cost/time ratio was found for data acquisition in the field. In a 20 minutes flight, around 25 hectares of forest was mapped at a resolution of 10 centimeters. Other studies using UAV produced results that are comparable with LiDAR in terms of point cloud densities [START_REF] Chung | Comparison of forest canopy height profiles in a mountainous region of Taiwan derived from airborne lidar and unmanned aerial vehicle imagery[END_REF][START_REF] Dandois | Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure[END_REF] and estimations of forest structural parameters [START_REF] Alonzo | Quantifying Boreal Forest Structure and Composition Using UAV Structure from Motion[END_REF]. Finally, the computation of CTM using open-source Matlab® routines is automatic and only requires the window size used for texture analysis as the user input. Window size can be adapted to forest canopy crown size and distribution, although limited variations do not alter the results much. Consequently, the workflow described here has great promise for future monitoring of tropical forest at low cost, which is interesting when airborne LiDAR is not affordable or available.

However, UAV remain limited by their inability to cover regions as large as those covered by satellite images, and climatic conditions (wind, cloud shadows) are likely to disturb the consistency of image texture and thus the automatic mapping process. Finally, regulations strongly limit the use of UAV in certain countries. Nonetheless, UAVs appears to be an efficient tool at the interface between field inventory and satellite characterization of forest structure [START_REF] Ketchen | The Application Of Cluster Analysis In Strategic Management Research: An Analysis And Critique[END_REF]. Unmanned aerial vehicle acquired reference data could be the basis of upscaling chains that would allow the use of spaceborne data of decreasing resolution yet increasing swath and affordability so as to reach broad scale, wall to wall mapping of forest state indicators of known accuracy.

Figure 1 :

 1 Figure 1: Location of the study site, Paragominas municipality, in Pará state in the Brazilian Amazon. Distribution of the 40 forest plots covered using UAV. Illustrations of selective logging (A), over-logging (B) and fire (C) from Google Earth® 2017

Figure 2 :

 2 Figure 2: Forest degradation history of the 40 forest plots based on the frequency of selective logging,

Figure 4 :

 4 Figure 4: Canopy texture ordination based on the FOTO method applied to UAV-acquired grey level images. (a) Scatter plots of PCA scores along F1 and F2 and windows selected as illustrations. (b) Histogram of eigenvalues expressed as % of total variance. (c) Correlation circles with frequencies ranging from 50 to 240 (cycles/km).

Figure 5 :

 5 Figure 5: Canopy texture ordination based on the lacunarity method. (a) Scatter plots of PCA scores along F1 and F2 and windows selected as illustrations. (b) Histogram of eigenvalues expressed as % of total variance. (c) Correlation circles with sub-window sizes ranging from 2 to 102 pixels.

Figure 6 :

 6 Figure 6: Three-dimensional plots of canopy height models of the 100 x 100 m windows selected to illustrate the six forest structure clusters. Radar chart shows the average frequency of over-logging, selective logging and fire disturbances detected in all forest plots within a given cluster.

Figure 7 :

 7 Figure 7: (a) Scatter plot showing the distribution of the 40 forest plots with color based on the color of canopy clusters on the linear discriminant plane (LD1-2). Inset: proportions of LDA trace (b) Correlation circle of CTM with respect to the two main components (axes) of the LDA (c) Confusion matrix between observed and predicted clusters for the 40 plots (LDA classifications)

Figure 8 :

 8 Figure 8: Mean and SD values of CTM calculated within the 6 predicted clusters using LDA.

  Routines for both FOTO (http://doi.org/10.5281/zenodo.1216005) and lacunarity methods were developed in the MatLab® environment (The MathWorks, Inc., Natick, Massachusetts, USA). 2.3.2. Computation of forest canopy structure metrics from canopy height models at 1 ha scale From the canopy height model, six Canopy Structure Metrics (CSM) were computed in the same 100*100m window grid previously described: mean elevation (mean), minimum (min), maximum (max), variance (var), Standard Deviation (SD) and Coefficient of Variation (CV) defined as the ratio between standard deviation and mean elevation. We then compiled the six Canopy Texture Metrics, noted CTM, (FOTO_PCA1, FOTO_PCA2, Lacu_PCA1, Lacu_PCA2, Skewness, Kurtosis) and the 6 CSM. 2.3.3. Canopy texture -structure relations within a large range of degraded forest types at 1 ha scale

Table 1 :

 1 Random forest regression models for the prediction of canopy structure metrics (CSM) from canopy texture metrics (CTM) on grey level images.

	CSM	R²	RMSE	Relative RMSE
	Minimum (m)	0.13	3.49	0.94
	Maximum (m)	0.43	5.66	0.76
	Mean (m)	0.38	4.88	0.79
	Variance	0.54	13.03	0.68

Potential of canopy texture metrics to discriminate forest degradation histories at the plot scale

	3.3.1. Clusters of canopy structures and related degradation history

Acknowledgements

This work was supported by 1) the European Union through the H2020-MSCA- RISE-2015 ODYSSEA project (Project Reference: 691053), by 2) the CNES (France) through the TOSCA CASTAFIOR project (ID 4310), by 3) the EIT through the Climate-KIC ForLand Restoration project and by 4) the CGIAR Research Program on Forest Trees and Agroforestry (FTA) and on Climate Change, Agriculture and Food Security (CCAFS), the latter with support from CGIAR Fund Donors and through bilaterial funding agreements.

Authors' contributions

 CB, JB, LB, JO, LR, PL and VG conceived the ideas and designed methodology; CB, JB, LB and VB collectedthe data; CB, JB, PC, LB, HD, LR and VG analyzed the data; CB, PC, VG, LB and PS led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.

For details, please visit https://ccafs.cgiar.org/donors. The views expressed in this document cannot be taken to reflect the official opinions of these organizations

Conflicts of Interest:

The authors declare no conflict of interest.


Elodie Conrath

Patrice Berthod

Properties of a HfC-Reinforced Nickel-Based Superalloy in Creep and Oxidation at 1100°C

Keywords: Ni alloy, HfC carbides, creep resistance, high temperature oxidation

come

In front of the demand of superalloys for applications at increasing temperatures the microstructure solutions begin lacking to offer a complete set of good properties at high temperature (H.T.): toughness, mechanical strength, good behavior in hot oxidation and corrosion, and furthermore workability with complex shapes [START_REF]The Superalloys[END_REF]. Obtaining coarse grains for high temperature strength by keeping complex shape feasibility leads to casting process. The obtained microstructures are necessarily polycrystalline. The presence of grain boundaries and, consequently of intergranular reinforcing particles (e.g. carbides), limit the fusion start temperatures. This does not allow applying optimized heat treatments to achieve high mechanical strength at very high temperature such as the high volume fractions of Ni 3 Al precipitates in Nibased γ/γ'single-crystals.

MC carbides are among the most efficient particles for strengthening grain boundaries. The most common MC carbides are the tantalum carbides. The script-like shape of TaC in cast Co-based alloys are very useful for resisting creep at H.T. Unfortunately they are not so stable in Ni-based alloys [START_REF] Berthod | Experimental and thermodynamic study of the microstructural state at high temperature of nickel base alloys containing tantalum // Calphad[END_REF]. Hafnium carbides are other possible MC which may precipitate in Co-based alloys with the same morphology as TaC [START_REF] Berthod | High temperature properties of several chromium-containing Co-based alloys reinforced by different types of MC carbides (M = Ta, Nb, Hf and/or Zr)[END_REF]. This second type of MC carbide is also script-like and very stable at H.T. in Co-based [START_REF] Berthod | Microstructure evolution in the bulk and surface states of chromium-rich nickel-based cast alloys reinforced by hafnium carbides after exposure to high temperature in air // Materials at High Temperature[END_REF].

Combining a Ni-based matrix rich in Cr and HfC carbides in a cast alloy may is an interesting solution for constituting complex-shaped pieces resisting both chemical aggressiveness from gases and melts (intrinsic property of a Ni-Cr base) and mechanical solicitations (austenitic network of Ni matrix and interdendritic reinforcement by HfC), at H.T. This is what was attempted in this work with a Ni-25Cr-0.25C-3.7Hf alloy.

Method of investigation

40g of this alloy, named "NCCHf", was cast from pure elements (Alfa Aesar, > 99.9 wt.%), in the water-cooled copper crucible of a High Frequency Induction furnace (CELES), under 300 mbars of pure Ar. A Ni(bal.)-25Cr-0.25C alloy, named "NCC", was added to the study in order to specify the effect of Hf on the oxidation behavior. This second alloy was elaborated in the same conditions as NCCHf.

Three types of samples were prepared. A first compact one was kept for the metallographic study of the as-cast microstructure of NCCHf. A second sample, an elongated parallelepiped, was devoted to creep bending tests. A third one, a square-based parallelepiped, was destined to oxidation test in thermobalance. A sample for oxidation test was prepared from NCC (reference), as base of comparison.

The metallographic sample was embedded then ground with 250 to 1200-grit SiC papers. Thereafter it was polished with 1µm hard particles for obtaining a mirror-like state. The creep sample (1mm × 2mm × 15mm), was cut then polished with papers up to 1200-grit with final strips in the length direction. This parallelepiped, with homogeneous thickness and width (tolerance: ±0.01mm), was thoroughly examined by microscopy to be sure of the absence of any defects able to weaken it. The dimensions of the samples for H.T. oxidation tests were 8mm × 8mm × 3mm. They were polished with 1200-grit SiC papers with smoothing of edges and corners.

The metallographic samples were observed using a Scanning Electron Microscope (SEM, JEOL JSM6010LA). Observations were done under 20kV in the Back Scattered Electrons (BSE) mode, with the ×125, 250, 500 and 1000 magnifications. The obtained chemical composition was measured using the Energy Dispersive Spectrometry (EDS) device attached to the SEM. The different phases were specified by EDS spot analyses. Indentations were carried out for the two alloys using a Testwell Wolpert apparatus, to measure their Vickers hardness under a 30kg load. In each case three indentations were performed; average value and standard deviation values were calculated.

NCCHf was submitted to bending tests at 1100°C under constant load, according to the symmetrical three point flexural method, using a dilatometer (SETARAM TMA92-16.18) modified to allow 3-points flexural testing. The space between the two bottom supports was 12mm and the maximal possible deformation was about 1.4 mm (height of these supports). The load was applied to the sample by the upper support, inducing a 20MPa tensile stress in the middle of the bottom side of the sample. Its value was calculated for each sample by taking into account the exact values of their thickness and width. The load was progressively applied at room temperature, until the vised value. The heating was then done, at a constant rate of 20°C min -1 . The isothermal stage was more or less long, depending on the behavior of the tested sample. The downward vertical displacement of the upper support was recorded every 60 seconds. At the end of the experiment a cooling was applied at a rate of -20°C min -1 .

NCCHf and NCC were both tested in oxidation at 1100°C during 46h, using a thermo-balance (SETARAM TG-92), in dry synthetic air. The heating was achieved at 20°C min -1 , and the post-isothermal stage cooling was done at -5°C min -1 . X-ray diffraction (XRD) was carried out to characterize the oxides scales. The oxidized samples were thereafter covered by a thin gold coating then by a thick electrolytic nickel coating, to protect the oxide scales during cutting. The coated oxidized samples were cut, embedded and polished. They were examined using the SEM in BSE mode. The sub-surfaces and the oxide scales were subjected to spot EDS analysis.

Results and discussion

EDS analyses showed that the vised chemical compositions were well obtained. The NCCHf microstructure is illustrated in Fig. 1 (left: general view; right: details). HfC is obviously the single carbide phase present. This demonstrates that Hf is a much more carbide-former element than Cr in nickel alloys while this was the contrary for Ta in the same base (Ta less carbide-former than Cr in nickel alloys [START_REF] Berthod | Experimental and thermodynamic study of the microstructural state at high temperature of nickel base alloys containing tantalum // Calphad[END_REF]). All the Hf atoms are present as HfC since no Hf was detected in the matrix by EDS. Thanks to the atomic ratio Hf/C equal to 1 obtained by choosing 0.25 wt.%C and 3.7wt.% Hf, all the C atoms were also involved in HfC. HfC are script-like shaped and located in the interdendritic areas where they form a eutectic with the matrix, which let think to high mechanical properties at H.T. The Vickers indentations led to 194±9 Hv 30kg for NCCHf, i.e. higher than NCC (163±3 Hv 30kg ). This hardness close to 200 let expecting respectable strength at high temperature.

M HfC

Fig. 1. As-cast microstructure of NCCHf (SEM/BSE mode); visualization of matrix ("M") and the HfC carbides ("HfC")

The deformation curve (displacement of the upper support) obtained for NCCHf during the flexural 3-point creep test is shown in Fig. 2 together with a scanned view of the deformed sample. The deformation rate in the secondary creep stage is of only 2 µm h -1 which is very low for a nickel alloy at this temperature for a resulting tensile stress of 20 MPa. The thermogravimetry test carried out at 1100°C in synthetic dry air led to a parabolic mass gain kinetic (Fig. 3). Compared with the mass gain curve obtained for the reference NCC alloy -the good behavior of which is well-known (among the best chromia-forming alloys in this field) -this result is very interesting. Indeed, NCCHf did not present any mass gain jump (better adherence of the oxide scale on the substrate, due to Hf) and its mass gain rate is lower (Table 1). The transitory linear constant K l (slope of the mass gain curve at the beginning of the isothermal stage) and the parabolic constant Kp classically determined (slope of the mass gain curve when plotted versus the square root of time) are effectively a little lower than for NCC (Table 1, 2 nd and 3 rd columns): 14 against 18 ×10 -8 g cm -2 s -1 , and 4 against 6 × 10 -12 g 2 cm -4 s -1 , respectively. The comparison was also done by taking into account the mass loss by chromia volatilization (small loss of Cr 2 O 3 by re-oxidation in volatile CrO 3 ). For that the mass gain files were plotted according to the {m×(dm/dt)=f(-m)}-method [START_REF] Berthod | Kinetics of high temperature oxidation and chromia volatilization for a binary Ni-Cr alloy[END_REF] which allows the determination of the volatilization constant Kv (Kv is the slope of the obtained straight line) and of the real parabolic constant Kp (ordinate at the origin of this same straight line) since corrected from the minimization of mass gain induced by the volatilization of chromia. The volatilization-corrected Kp is again slightly lower for NCCHf than for NCC (12 against 14 × 10 -12 g 2 cm -4 s -1 ). In contrast the volatilization rate seems a little higher for NCCHf (87 against 82 ×10 -10 g cm -2 s -1 ). Fig. 3. A: comparison of the mass gain curves of NCCHf ("H") to the reference NCC ("N"); B: test of the determined kinetic constants by comparison between the experimental mass gain curve ("E") and the mathematical curves plotted from the Kp values either classically determined ("C") or determined according to the Kp-Kv method ("V") [START_REF] Berthod | Kinetics of high temperature oxidation and chromia volatilization for a binary Ni-Cr alloy[END_REF] Table 1. Values of the oxidation kinetic constants Constants K l (× 10 -8 g/cm 2 /s) K p (× 10 -12 g 2 cm -4 s -1 ) K v (× 10 -10 g/cm 2 /s) Before cross-section preparation the two oxidized samples were photographed and subjected to Xray diffraction. The two types of results are given together in Fig. 4. NCC obviously suffered scale spallation during the -5°C min -1 cooling after the isothermal oxidation, while NCCHf has obviously kept all its external scale. The X-ray diffractograms show that, even if Cr 2 O 3 is the main oxide present in both cases, the scales formed over NCC contain significant parts of spinel NiCr 2 O 4 and even NiO. In addition the substrate (Ni-Cr austenitic alloy) is also more detected in its case than for NCCHf, consequently to its more extended oxide spallation. The external oxide scale formed around NCCHf is richer in chromia than NCC, but it contains a little spinel NiCr 2 O 4 and also the hafnium oxide.

Alloys slope at stage start classic m × dm/dt = f(-m) m × dm/dt = f(-m) NCCHf 14
The cross-sectional examinations (Fig. 5, NCCHf surface and subsurface with two magnifications) confirm these observations. Additionally one can see that no carbide-free zone developed from the oxidealloy interface, in contrast with what generally occurs for cast Ni alloys with chromium carbides or TaC carbides. Indeed, the HfC carbides seem being still present in alloy close to the external oxide. X-ray diffraction and cross-sectional EDS spot analysis show that the carbides close to the oxide/alloy interface are oxidized in situ: HfC are replaced by HfO 2 at the same location.

Conclusion

The studied alloy demonstrated very interesting properties: rather high level of hardness but still compatible with a sufficient machinability, good creep resistance at 1100°C for a resulting tensile stress of 20 MPa and very good oxidation behavior. Concerning the two first points the high hardness of the HfC carbides (harder than Cr 7 C 3 and Cr 23 C 6 ) and their stability at H.T. bring important advantages by comparison with other more classical carbides. Concerning the last point this is essentially the Ni-base matrix which brings its intrinsic good high temperature behavior (by comparison with Co-based alloys for example). But the second beneficial role of Hf here, which is usually the main one in many cases, was also observed: the improvement in oxide scale adherence and thus resistance against spallation. Workable by foundry and offering so high levels of mechanical and chemical properties at H.T., this alloy ought to meet needs in structural metallic alloys for many high temperature applications.

Fig. 2 .

 2 Fig. 2. Flexural creep deformation of the NCCHf alloy at 1100°C under 20 MPa ("σ max ")

Fig. 4 . 2 Fig. 5 .

 425 Fig. 4. NCC (A) and NCCHf (B) oxidized at 1100°C for 46h: the X-ray diffractograms obtained on the oxidized surfaces ("c": Cr 2 O 3 , "cn: NiCr 2 O 4 , "n": NiO, "N": FCC Ni-based matrix); visualization of the oxide spallation (photographs in the top-right corners)

Acknowledgments

The authors wish to thank Th. Schweitzer and P. Villeger for their help.