Evaluating the ability of community‐protected forests in Cambodia to prevent deforestation and degradation using temporal remote sensing data

Abstract Community forests are known to play an important role in preserving forests in Cambodia, a country that has seen rapid deforestation in recent decades. The detailed evaluation of the ability of community‐protected forests to retain forest cover and prevent degradation in Cambodia will help to guide future conservation management. In this study, a combination of remotely sensing data was used to compare the temporal variation in forest structure for six different community forests located in the Phnom Kulen National Park (PKNP) in Cambodia and to assess how these dynamics vary between community‐protected forests and a wider study area. Medium‐resolution Landsat, ALOS PALSAR data, and high‐resolution LiDAR data were used to study the spatial distribution of forest degradation patterns and their impacts on above‐ground biomass (AGB) changes. Analysis of the remotely sensing data acquired at different spatial resolutions revealed that between 2012 and 2015, the community forests had higher forest cover persistence and lower rates of forest cover loss compared to the entire study area. Furthermore, they faced lower encroachment from cashew plantations compared to the wider landscape. Four of the six community forests showed a recovery in canopy gap fractions and subsequently, an increase in the AGB stock. The levels of degradation decreased in forests that had an increase in AGB values. However, all community forests experienced an increase in understory damage as a result of selective tree removal, and the community forests with the sharpest increase in understory damage experienced AGB losses. This is the first time multitemporal high‐resolution LiDAR data have been used to analyze the impact of human‐induced forest degradation on forest structure and AGB. The findings of this work indicate that while community‐protected forests can improve conservation outcomes to some extent, more interventions are needed to curb the illegal selective logging of valuable timber trees.


| INTRODUC TI ON
Protected areas (PAs) are widely regarded as an important bulwark against deforestation and biodiversity loss (Klein et al., 2015).
However, PAs are a partial and imperfect conservation solution for the tropical forests of the world. Analysis of the Global Forest Cover Change dataset (Hansen et al., 2013) revealed that globally, protected areas lost 3% of their forest cover, intact forest landscapes have lost 2.5%, while protected intact forest landscapes have lost 1.5% of their forest cover (Heino et al., 2015). An analysis of 60 tropical forest protected areas revealed that half of these have faced significant biodiversity erosion as a result of forest resource extraction and wildlife hunting. Furthermore, overharvesting, deforestation, and degradation outside the reserve boundaries can have a detrimental effect on biodiversity persistence within the reserve boundaries (Laurance et al., 2012).
International Union for Conservation of Nature (IUCN) categorizes protected areas into six broad management categories, ranging from strict nature reserves that limit human activity to reserves that allow for sustainable resource extraction (Hayes et al., 2013). Categories V and VI are more amenable to human resource extraction compared to categories I and II which focus more on preserving natural features and curtailing human activities within the boundaries of the PA (IUCN, 2013). In addition to enforcement, PA efficacy also depends on the benefits and compensation accrued by local communities (Liu et al., 2001). Strict PA categorizations which do not provide benefits to the local community can also cause forest loss to worsen. A temporal analysis of land-cover change in the Wolong PA, an IUCN Category I PA in China, revealed that the area had undergone considerable degradation subsequent to its designation as a PA (Liu et al., 2001). On the other hand, cooperation of the local communities can increase the chances of PAs being able to avoid forest loss. A Latin America-wide study carried out by Porter-Bolland et al. (2012), which compared annual forest loss within 40 protected areas and 33 community-managed forests, discovered that the latter had a lower rate of annual forest loss. The authors suggest that accounting for local tenure rights and the socio-economic welfare of the local inhabitants can yield better conservation outcomes. Community forestry was found to better protect forests from anthropogenic disturbances and logging in the Prey Long district of Cambodia where a substantial proportion of people depend on forests for sustenance (Lambrick, Brown, Lawrence, & Bebber, 2014). However, satellite land cover change analysis from 2003 to 2013 indicated that protected areas in Paraguay's Atlantic forest helped slow deforestation (Da Ponte et al., 2017a). A meta-analysis of African and Latin-American PAs revealed that while strict PAs delivered fire-prevention benefits, multiuse community PAs were more effective in fire prevention and could contribute to both biodiversity conservation and AGB stock retention (Nelson & Chomitz, 2011). Community forests can facilitate long-term forest protection in certain situations and deliver benefits to the local community (Bray et al., 2008). On the basis of the existing literature, it may be inferred that both community forests and protected areas deliver different outcomes across different regions. Evaluating the ability of different protection schemes, to counter forest cover change (in the form of deforestation and degradation) is important (Da Ponte, Roch, Leinenkugel, Dech, & Kuenzer, 2017b).

Different magnitudes of forest degradation and regeneration
impact forest structure parameters such as AGB storage and canopy structure-related variables such as gap fraction differently. In fact, even low logging volumes can lead to a decline in the carbon stocks of tropical forests (Bryan, Shearman, Ash, & Kirkpatrick, 2010). However, forests regenerating after shifting cultivation are vital AGB sinks and their ability to store biomass increases with the length of abandonment (Mukul, Herbohn, & Firn, 2016). Forest regeneration and associated increases in forest cover facilitate a rapid increase in carbon stocks storage (Lohbeck, 2016). Even degraded forests can act as valuable carbon sinks under certain conditions (Alamgir et al., 2016).
In addition to the AGB storage, other forest structure parameters such as canopy cover and tree height also vary across a degradation gradient (Mehta, Sullivan, Walter, Krishnaswamy, & DeGloria, 2008;Pfeifer et al., 2016). Gap fractions in the forest canopy (open gaps in forest canopy not covered by foliage) vary considerably between primary forests compared and forests that have been logged (Pinagé, Matricardi, Osako, & Gomes, 2014). In the Brazilian Amazon, it was discovered that forest canopy gaps undergo rapid regeneration and that within a three-year period, and no detectable difference remained between the canopy gaps of undisturbed and logged forests (Espirito-Santo, Keller, Braswell, & Palace, 2006). Canopy gaps resulting from conventional logging had lower rates of recovery compared to those caused by reduced-impact-logging 3.5 years after logging in the Brazilian Amazon (Asner, Keller, Pereira, Zweede, & Silva, 2004). Different types of remote sensing (RS) data have been used (either alone or in conjunction with each other) to study the variation in forest structure, greenness and degradation in time and space for tropical forests. Optical data such as those derived from Landsat have been extensively used for mapping temporal changes in forest cover and land use types in the tropics (Potapov et al., 2014).
A freely available software system CLASlite developed by the Carnegie Institute of Science has employed Landsat data to detect temporal forest cover change in Madagascar (Allnutt, Asner, Golden, & Powell, 2013) and distinguish plantations from natural forests in Borneo (Bryan et al., 2013). In addition to Landsat data, ALOS PALSAR radar data have been employed for studying the patterns of forest degradation and recovery in Cambodia, Laos, and Vietnam (Mermoz & Le Toan, 2016) and mapping varying levels of forest degradation in Laos (Singh, Tokola, Hou, & Notarnicola, 2017). LiDAR data, which have a higher spatial resolution than optical and radar data, have also been extensively used for mapping the variation in AGB stocks and other forest structure parameters in degraded forests of tropical Asia (Kronseder, Ballhorn, Böhm, & Siegert, 2012;Singh et al., 2016). The ability of LiDAR to capture canopy height at a fine scale makes it a useful tropical forest mapping tool with other datasets, notably Landsat (Leinenkugel, Wolters, Oppelt, & Kuenzer, 2015;Peou, Natarajan, Tianhua, & Philippe, 2016). LiDAR data offer the distinct advantage of being able to identify individual trees and measures of their biophysical parameters such as height, crown volume, and area. These measurements in turn can be used to model AGB variation at a landscape scale (DeFries, Rudel, Uriarte, & Hansen, 2010;Motzke, Wanger, Zanre, Tscharntke, & Barkmann, 2012). Temporal LiDAR data have been used for monitoring the impact of selective logging on AGB stocks in the Brazilian Amazon.
Using these data, locations that had lost their tallest trees were identified, along with changes in the proportion of logging trails, landings, and gaps. Furthermore, the role of large tree removal in influencing AGB stocks from 2010 to 2011 was examined (Asner, Knapp, Balaji, & Páez-Acosta, 2009).
RS data can play a vital role in monitoring the efficacy of conservation management schemes and PAs. Landsat data have been extensively used for examining the ability of protected areas to retain forest cover at both local and global scales (Allnutt et al., 2013;Heino et al., 2015). While ALOS-PALSAR and airborne LiDAR data have not been extensively used for mapping and monitoring the efficacy of protected areas and conservation management schemes, we hypothesize that use of these different RS data can help to monitor different aspects of tropical forest cover change dynamics (their impacts on forest structure dynamics such as AGB) and improve our understanding of the ability of community-protected forests to retain forest cover and prevent degradation in Cambodia.
The main objective of this research was to compare the temporal variation in forest cover (including the expansion of cashew plantations) and structure of six different community forests located in a National Park in Cambodia and assess how these dynamics vary between this community-protected forests and the wider study area.
The specific aims of this study are as follows: 1. to quantify the variation in Landsat-derived forested areas in the study area and across the different community-protected forests; 2. to use a combination of LiDAR and Landsat data to map and monitor the changes in forest cover, cashew plantation, and bare soil In addition to these, the impact of selective logging from 2012 to 2015 will be estimated by identifying tree height classes that have faced losses (at the individual tree scale).
It is expected that the findings of our research will help us to quantify the efficacy of community-protected forests in preventing deforestation and facilitating regeneration (compared to the wider landscape) and their ability to curb the selective illegal logging of individual tree species. The countries of Greater Mekong region have high rates of deforestation resulting from factors ranging from plantation agriculture to selective logging, with the latter being more difficult to detect (Leinenkugel et al., 2015). Quantifying the ability of community forests to facilitate forest regeneration and curb selective logging can help to inform conservation management strategies.

| Study area
Phnom Kulen National Park (PKNP) is located 48km from Siem Reap in northwestern Cambodia (Figure 1). It is an important archaeological site, a critical area for biodiversity, and a significant component of the regional watershed which includes the World Heritage listed Angkor Archaeological Park. In terms of composition, PKNP is mainly dominated by semi-evergreen forests (with isolated patches of dry dipterocarp forests). However, in terms of land use forests that have undergone varying levels of degradation as a result of activities such as selective logging and swidden agriculture and land use types such as cashew plantations now dominate PKNP. Notably, PKNP is home to several IUCN-listed species of international concern, including the Pileated Gibbon, Indochinese Silver Langur, Bengal Slow Loris, and Binturong (Hayes et al., 2013;Peou et al., 2016).
F I G U R E 2 Location of study area and community forests in PKNP Over recent decades, Cambodia has experienced some of the highest rates of deforestation globally (Hansen et al., 2013). Despite its protected status, PKNP has experienced high deforestation and degradation rates and, as with other PAs in Cambodia, faces significant threats from local resource extraction activities (Motzke et al., 2012). Additionally, several villages are located within or on the boundary of PKNP. Many of these villages have experienced significant population growth due to people relocating in search of cheap farmland (DeFries et al., 2010). Furthermore, the villages located within the boundary of PKNP have high rates of poverty, low educational levels, and depend heavily on forest resources for their sustenance.

| Field data collection
Field survey was conducted in March 2016 and during this the geo-locations of the different land cover types, including, forests, cashew plantations and bare earth were collected. The standard FAO definitions of forests (which is common for all the countries in the world) was used; "land of at least 0.5 ha covered by trees higher than 5 m and with a canopy cover of more than 10%, or by trees able to reach these thresholds, and predominantly under forest land use" (Hansen et al., 2013). The survey revealed that the community forests were comprised mostly of regenerating/secondary forests while areas outside these were comprised of severely degraded forests, agricultural/bare areas and cashew plantations. Bearing in mind the criticism of the bespoke standard definition and for the purpose of this research, cashew plantation monocultures were given their own category as opposed to categorizing them as forests. The field-collected geo-locations were cross-verified using high-resolution Google Earth imagery.

| Spaceborne optical and radar data
Spaceborne optical and radar data in the form of Landsat TM and ALOS PALSAR, respectively, were used in this research. Landsat TM data (path 127, row 51 with spatial resolution 30m) for March 2011-2015 were downloaded from Earth Explorer. The month of Landsat data acquisition was selected to match the season of LiDAR data acquisition. Raw Landsat data were converted to surface reflectance by applying both radiometric and atmospheric correction to these data through the freely available software CLASlite (Asner, Lactayo, Tupayachi, & Luna, 2013;Asner et al., 2009). In addition to atmospheric corrections, masking of clouds and haze was carried out by the software. An Automated Monte Carlo Un-mixing algorithm that uses a probabilistic subpixel analysis approach was used to decompose the surface reflectance data into fractional cover (Asner et al., 2009). Under the subpixel analysis approach, each pixel of the data is decomposed into a fraction of photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and bare substrate (BS).
The recommended threshold values of PV > 80% and BS < 20%were used to decompose the fractional cover image into binary maps of forest and nonforest cover (Asner et al., 2009;Bryan et al., 2013).
The binary maps of forest-nonforest areas have been extensively The backscatter values of HH and HV are strongly associated with the forest structural components, orientation, and canopy cover (Mitchard et al., 2011). RFDI values are obtained on a scale of (1) RFDI = (HH − HV)∕(HH + HV) 0 to 1. When the canopy opens up (as a consequence of logging and deforestation), RFDI values go up. Completely cleared areas have RFDI value of 1, while undisturbed forests have RFDI values ranging from 0.3 to 0.4 (Saatchi, Houghton, Dos Santos Alvala, Soares, & Yu, 2007). Lower values of RFDI indicate higher levels of forest canopy cover and intactness (Singh et al., 2017).

| Airborne LiDAR
LiDAR data were acquired over the study area in March 2012 and April 2015 (Evans, 2016;Evans et al., 2013). For both data acquisitions, a Leica ALS60 laser system and a 40 megapixel Leica RCD105 medium-format camera within an external pod were used by mounting to the left skid of a Eurocopter AS350 B2 helicopter (Evans, 2016).
For our study area, the 2012 data were derived from discretized full waveform data acquired in both N-S and E-W strips, while the 2015 data represent a combination of discrete-return data (NW-SE strips) and discretized points from full waveform data acquired in NW-SE strips. The point density of the 2012 LiDAR data was 12 points/m 2 (Singh et al., 2016) and the LiDAR data collected in 2015 were>15 points/m 2 (Evans, 2016). In order to achieve this level of accuracy and point density, was achieved by flying at altitudes of 800-1000 m above-ground level at a speed of 80 knots, with the ALS70 configured to Multipulse in Air (MPiA). The pulse rate was 500 kHz with a scan angle of 45° from nadir and a swath side-lap of 50% (i.e., almost all terrain was scanned twice from different angles). Aircraft attitude was measured by a Honeywell CUS6 IMU at a rate of 200 kHz and positional data was logged at 2 Hz using a survey-grade L1/L2 GNSS receiver mounted in the tail rotor assembly (Evans, 2016). These data were classified into ground and nonground points using the LiForest software (LiForest, 2016). It has been suggested that LiDAR data with point density >0.5 pulses/m 2 produce reliable estimates of the forest canopy (Hansen et al., 2013) and LiDAR data with pulse density greater than 1 pulse/m 2 have limited impact on estimating forest structure variables (Andersen, Reutebuch, McGaughey, d'Oliveira, & Keller, 2014). Hence, no thinning of the 2015 LiDAR dataset was carried out.
Ground returns were used to derive a Digital Elevation Model Points with elevation values greater than the height break of 2m were considered to be tree points and the ratio of LiDAR returns less than the height break to the total number of returns was computed to produce estimates of gap fractions at a 15m resolution (LiForest, 2016). The resulting point densities for the 2012 data within our study area are 12 points/m 2 for the 2015 data, point densities are 16 points/m 2 (Evans, 2016). The gap fraction values range from 0% to 100% where 0 means a closed canopy and 100% means an open canopy [19]. Canopy cover was also extracted for the entire study area (Chen et al., 2014) This equation was used to produce AGB estimates for both 2012 and 2015 using the aerial imagery-derived canopy cover as previously done in (Singh, Evans, Friess, Tan, & Nin, 2015;Singh et al., 2016). Similar log-log-based models have been used for landscape scale biomass mapping with LiDAR-derived variables in other tropical forest ecosystems as well .
In addition to these, relative density models (RDMs) were computed from the LiDAR data of 2012 and 2015. This metric helps to map the impact of logging roads, skid trails, and landings. It is a raster layer indicating the percentage of LiDAR returns within a userspecified above-ground height category (Andersen et al., 2014). This was derived using LAStools by using the return data both 1m above the ground and from 1m to 10m above the ground as done previously

| Data analysis
Wilcoxon paired sample tests were applied to examine whether the LiDAR-derived forest structure variables had changed significantly between 2012 and 2015. This is a nonparametric test that does not need the assumption of normally distributed data (Gaveau et al., 2009;Grandin, 2011). The temporal changes in  (Breiman, 2001). It is an ensemble-based modelling technique where individual classifiers are built and later combined to improve predictive performance (Devaney, Barrett, Barrett, Redmond, & John, 2015). This technique can work with high-dimensional data and correlated predictors and has thus been extensively used for land use classification (Gislason, Benediktsson, & Sveinsson, 2006).
For classification purposes, an ensemble of individual decision-tree classifiers is created and these are combined using a majority voting scheme. The individual trees are constructed using a bootstrap sample of the training data, whereby the training is performed on twothirds of the data samples and the remaining one-third of the data samples are omitted. The latter is used for testing the robustness of the developed model (Devaney et al., 2015). In this research, the RF classification was carried out by using 70% of the data for training and 30% for testing in the R programming language.

| Overall forest cover change patterns
The binary Landsat forest/nonforest cover maps derived for 2011 and 2015 were examined for the temporal changes in forest cover using IDRISI (Figure 3). It was discovered that over a 4-year period, the forest cover in the study area had declined by more than 20%.
The forest cover persistence and gain in the entire study area from 2011 to 2015 was 53% and 16%, respectively (Figure 4).
Compared to the overall study area, the persistence of forest cover was much higher in the community forests (Table 1).
A visual inspection of the forest cover loss, gain, and persistence map ( Figure 3) also reveals that the community-protected forests are dominated by persistent forest cover. Analysis of these two random forest-based maps also indicated high levels of forest cover retention within the community forests.
Further analysis of these data also revealed that overall forest cover declined by 20.4% across the entire study area. This is consistent with the findings of Figure 3, which also indicate a similar decline in forest cover values (with the Landsat-based forest-nonforest maps).
Analysis of the random forest-derived Landsat-LiDAR maps also revealed that cashew plantations increased by 7.5% and the bare ground increased by 70.8%. In all, 4 km 2 of forests in the area were converted to cashew plantations from 2012 to 2015 ( Figure 5). Figure 5 shows the areas converted to cashew plantations by 2015.
A visual examination of Figure 5 indicates that cashew plantations have penetrated the community forests marginally and that most of the forest-cashew plantation conversions have occurred outside the community forests. In addition to examining the changes in forest cover and cashew plantation expansion, we have examined the spatial distribution of canopy height changes using LiDAR. Furthermore, the changes in forest structural and spectral properties within the community forests from 2012 to 2015 were also examined.

| Changes in forest structure from 2012 to 2015
Wilcoxon paired sample test discovered that tree heights for all com-

| Changes in forest cover and structure of the community forests
Analysis of the Landsat-based binary forest-nonforest map revealed that all the community forests have much higher level persistence of forest cover persistence (72%-99%) compared to the wider landscape, where more than 20% of forest cover was lost in these three years. However, binary forest cover maps are unable to distinguish between different forest types, including plantation monocultures (Tropek et al., 2014). The 3 class Landsat-LiDAR map also indicates a forest cover decline of 20% in the time period. These findings are consistent with the research done by Davies, Murphy, and Bruce (2016) in PKNP, which indicated an increase in the proportion of PKNP undergoing forest decline.
Unlike the binary forest cover class produced by ClaSLite, the 3 class LiDAR-Landsat map helped spatially map forest areas converted to cashew plantations and indicated limited encroachment of these plantations into community forests. We note that the area under cashew plantations increased by 15% from 7.4 to 13.3 km 2 .
Field research indicates that one of the community forests, APA-NT, has high levels of cashew plantations, which had mainly been established before the APA was formally designated. Hence the cashew plantations observed within the community forest predate the period of analysis.
All community forests have also seen a significant increase in NDVI values which may be attributed to an increase in forest cover (Song, Huang, Sexton, Channan, & Townshend, 2014). NDVI is a robust indicator for mapping forest degradation, regeneration, and successional patterns and has been used for quantifying these in the tropical forests of Mexico and Congo (Hartter, Lucas, Gaughan, & Aranda, 2008;Njomo, 2008). The forest cover gain in the different community forests ranges from 0.2% to 24.6%. However, NDVI increases do not always correspond to forest cover increase which is why RFDI, a measure of forest degradation change was computed as well. It is remarked that in cases where an increase in NDVI corresponds to a decrease in RFDI (this being an indicator of decreasing forest degradation), it may be argued that forest regeneration may be underway. A combination of RFDI and Landsat-based greenness measures was previously used to quantify the varying levels of degradation in a human-modified tropical forest ecosystem in Lao PDR (Singh et al., 2017).
The analysis of LiDAR-derived canopy heights and crown areas indicates that in all cases except one, these have increased in all the  (Lasco, Visco, & Pulhin, 2001;Mukul et al., 2016). Forest regeneration and associated increases in forest cover facilitate the rapid increase in carbon stocks (Lohbeck, 2016). The decline in canopy gap fractions (and consequently an increase in canopy cover) is a sign of regeneration in tropical forests (Espirito-Santo et al., 2006;Filer, Keenan, Allen, & Mcalpine, 2009 (Pfeifer et al., 2016).
RFDI was previously used by Mitchard et al. (2012) to help distinguish between the different forest classes (Mitchard et al., 2011) and mapping the temporal variation in forest degradation (Joshi et al., 2015) in African forests. RFDI was also used for mapping forest degradation in the different forest types of human-modified ecosystems of Laos (Singh et al., 2017). While developing formal relations between AGB and RFDI is not the focus of this research, the findings suggest that RFDI mapping can be undertaken as a way of identifying areas that have undergone high levels of degradation and are susceptible to losing their AGB stocks. This can be especially beneficial for monitoring degradation (and its impacts) in areas where LiDAR and other high-resolution data sources are not available for fine-scale AGB mapping.
Forest degradation is a spatially diverse phenomenon which unlike deforestation can also occur in forest ecosystems that have high or even near-intact canopy coverage (Joshi et al., 2015). Our research backs up these findings; even though the community forests have retained >70% forest cover and see an increase in NDVI, the CPAs continue to suffer from small levels of degradation which is reflected in the increase in canopy gap fraction and decline in AGB stocks.
Selective logging for valuable tree species is a leading cause of forest degradation in the countries of Southeast Asia, including Cambodia (Miettinen, Stibig, & Achard, 2014). This has an adverse impact on forest structure parameters such as stand-scale tree  21.8 ± 5.9 6.2 ± 3.22 6.4 ± 3.22

| Monitoring selective logging
An examination of the prominent roads/logging trails presents within the study area indicates that all the community forests within the study area can be accessed using them and that many of the community forests are located in or near areas of high road density (see Supporting information Figure S1). It should be stressed that about two-thirds of over 4,500 people that are living on the plateau across 10 villages are farmers who practice slash and burn clearing and cashew nut cultivation. When interviewed, park rangers stated that ongoing resource extraction from the community forests remains an ongoing concern. These interviews also confirmed the ongoing problem of selective luxury hardwood removal in PKNP, and that a pervasive network of roads/logging trails contributes substantially to that problem.
Interviewees also indicated that individual tree removal was being carried out on an ad hoc, opportunistic basis rather than a planned, systematic program of timber extraction. Future research will benefit from a detailed analysis of both the temporal changes in road density and its impact on individual tree removal and forest cover loss.
An analysis of relative density measure (RDM) also revealed that all community forests had faced an increase in understory damage caused by skidding and haulage, which are the hallmarks of selective logging for specific tree species (the RDM had decreased for all the community forests; Andersen et al., 2014;d'Oliveira, Reutebuch, McGaughey, & Andersen, 2012). However, the decline in RDM was especially large for the CPAs. This is arguably linked to an increase in forest degradation in these from 2012 to 2015 (measures using RFDI) and a consequent increase in the canopy gap fraction and decline in the AGB stocks. The RDM metric was previously used by Andersen et al. to quantify the increase in the area impacted by the hallmarks of selective logging-skidding trails, haulage, and roads from 2010 to 2011 in the Brazilian Amazon (Andersen et al., 2014).
It was also discovered that areas impacted by these activities related to selective logging had a higher rate of AGB loss as compared to the nonimpacted areas (Andersen et al., 2014). This research also establishes that areas with a steep decline in RDM values lost AGB from 2012 to 2015. Additionally, the findings would indicate that the impact of selective logging in terms of increased skidding and understory damage is reflected in the RFDI metric, which is essentially based on the radar-measured changes in the forest canopy (Mitchard et al., 2011;Saatchi et al., 2007). ALOS data are sensitive to patterns of disturbance and regrowth and were used to characterize these patterns from 2007 to 2010 for the Greater Mekong countries, including Cambodia (Chheng, Mizoue, Khorn, Kao, & Sasaki, 2015).
A previous study carried out in the semi-evergreen forests of Cambodia revealed that during selective logging, felling of larger trees caused severe damage to the surrounding forest (Chheng et al., 2015). Meta-scale analysis by Martin et al. revealed different logging techniques influence tree damage, AGB storage, and tree species dynamics differently (Martin, Newton, Pfeifer, Khoo, & Bullock, 2015).
While we do not seek to establish any causality between RDM and radar-measured degradation, this research has demonstrated the utility of different sources in evaluating forest loss and degradation. We suggest that temporal monitoring of forest cover change, especially in areas impacted by selective logging will benefit from evaluating the impact of different logging regimes and methods of felling different sized trees on the overall forest canopy. Use of multiscale remote sensing techniques (together with field data) can help to quantify forest degradation and its impact on both biodiversity and carbon (Bustamante et al., 2016).

| Can community forests deliver conservation outcomes?
Our study area covers only a small proportion of PKNP. However, analysis of the study area does suggest that community-protected forests are an effective bulwark against large-scale deforestation/ slash-burn clearance and plantation establishment. Indeed, within our study area, the community forests had higher rates of forest cover retention as compared to areas outside the community forests. Furthermore, higher levels of forest-to-cashew plantation conversions were observed outside the community forests than inside. While our study area is relatively small and the changes were examined at a relatively narrow temporal scale, on the basis of these findings, it is suggested that analysis at larger spatial scales and longer temporal scales could be undertaken to better help to understand the spatiotemporal dynamics of forest cover change.
Specifically, future research proposes to scale up the analysis to the scale of the whole of PKNP (and include other Cambodian PAs) to conduct a detailed analysis of forest cover retention and biodiversity conservation benefits provided by different protection and management schemes, including community forests and strict nature reserves.
A meta-scale study of the community forests in South and South-East Asia revealed that community forests have had a positive impact on improving tree species biodiversity and forest biomass production (Ravindranath, Murali, & Sudha, 2006). Previous research has discovered that community forests established in consultation with local people had higher AGB storage, reduced canopy openness and lower anthropogenic disturbance in the district of Prey Long in Cambodia (Lambrick et al., 2014). Policy research further suggests including local rulemaking autonomy may aid national scale REDD+ programs (Hayes & Persha, 2010), and that programs managed by local organizations garner more support from local people (Clements et al., 2010). Virachey National Park, an IUCN category II park located in northeastern Cambodia, partially encompasses the ancestral home of an ethnic minority group. It was discovered that the resource tenure regimes developed locally had a positive impact on biodiversity and local livelihood outcomes (Baird & Dearden, 2003

| CON CLUS IONS
The temporal patterns of forest cover change, degradation, and recovery in community forests located within an IUCN Category II park have been examined using remotely sensed data acquired at different spatial resolutions. An examination of Landsat-derived forest cover revealed that the community forests had higher forest cover persistence and lower rates of forest cover loss compared to the overall study area. The role of community forests in facilitating forest cover retention and regeneration is well established. The analysis of high-resolution aerial LiDAR data also confirms these findings and except for the two CPAs, the remaining community forests have to the anonymous reviewers whose input considerably improved the final manuscript.

CO N FLI C T O F I NTE R E S T
The authors declare no conflict of interest.