基于无人3D摄影技术的雪松(Cedrus deodara)群落高度测定

植物群落高度是反映植物群落特征的重要指标,植物群落高度的测定能给植物群落多样性分析、生物量估算、功能形状研究提供重要的数据基础。传统的森林调查主要由生态调查工作者通过目测或者利用激光测高仪对每个个体进行逐一测定,因此劳动强度大,耗时费力,并且难以进行大面积的植物群落高度调查。近年来,随着无人机(Unmanned Aerial Vehicle,UAV)技术的飞速发展,催生了无人机低空摄影测量和遥感技术,该技术已被应用于农作物植株高度测定和生物量估测等。然而针对植被类型多样、树木种类繁多且地形复杂的山区如何精确的获取植物群落高度仍然是一个较大挑战。以缓坡地形的云南大学呈贡校区为研究区,选取校区内人工种植的雪松(Cedrus deodara(Roxb.)G.Don)林为研究对象,利用无人机搭载可见光相机平台获取研究区近地面航空影像,利用高分辨的影像匹配加密获得的点云数据生成数字表面模型(Digital Surface Model,DSM)。依据点云分类提取非植物类点,消除少数因植被与非植被相接的边缘模糊而错分类的部分,内插后生成数字地面模型(Digital Terrain Model,DTM)。将DSM和DTM叠加相减得到树木高度变化模型(Canopy Height Model,CHM),即获得研究区各个雪松的高度。然后利用激光测距仪测定研究样地内100棵雪松的高度,将此测定的树高与无人机航测技术生成的CHM模型测定的树高值进行精度检验。结果表明无人机测定的树高值与激光测距仪测定的树高值线性拟合度较高,r2值在0.904以上。此方法基于无人机影像生成空间模型,提取树高,受外界环境因素影响较小,且成本较传统测树方法低廉,可广泛运用于各种植物群落的调查研究当中,有极好的应用前景。     

集成高分辨率UAV影像与激光雷达点云的亚热带森林林分特征反演

该研究集成高分辨率无人机(UAV)影像和激光雷达(LiDAR)点云数据估算亚热带天然次生林林分基本特征变量。首先, 基于LiDAR点云和反距离加权插值法构建林下高精度数字高程模型(DEM); 然后利用UAV影像对序列构建植被冠层上层三维点云, 并借助DEM进行高度信息归一化, 提取高度和冠层点云密度相关的特征变量; 最后, 构建预测模型并估算Lorey’s高、林分密度、胸高断面积、蓄积量。结果表明: 联合提取的特征变量与Lorey’s高的敏感性最高, 蓄积量次之, 林分密度和胸高断面积最低; 利用UAV灵活快速的手段获取森林冠层信息, 辅以高精度LiDAR数据获取的地形信息, 两者互补实现一种可重复的快速、廉价和灵活的林分特征的反演方式。     

Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system

Canopy height (H_canopy) and aboveground biomass (AGB) of crops are two basic agro-ecological indicators that can provide important indications on the growth, light use efficiency, and carbon stocks in agro-ecosystems. In this study, hundreds of stereo images with very high resolution were collected to estimate H_canopy and AGB of maize using a low-cost unmanned aerial vehicle (UAV) system. Millions of point clouds that are related to the structure from motion (SfM) were produced from the UAV stereo images through a photogrammetric workflow. Metrics that are commonly used in airborne laser scanning (ALS) were calculated from the SfM point clouds and were tested in the estimation of maize parameters for the first time. In addition, the commonly used spectral vegetation indices calculated from the UAV orthorectified image were also tested. Estimation models were established based on the UAV variables and field measurements with cross validation, during which the performance of the UAV variables was quantified. Finally, the following results were achieved: (1) the spatial patterns of maize H_canopy and AGB were predicted by a multiple stepwise linear (SWL) regression model (R² = 0.88, rRMSE = 6.40%) and a random forest regression (RF) model (R² = 0.78, rRMSE = 16.66%), respectively. (2) The UAV-estimated maize parameters were proved to be comparable to the field measurements with a mean error (ME) of 0.11 m for H_canopy, and 0.05 kg/m² for AGB. (3) The SfM point metrics, especially the mean point height (H_mean) greatly contributed to the estimation model of maize H_canopy and AGB, which can be promising indicators in the detection of maize biophysical parameters. To conclude, the variations in spectral and structural attributes for maize canopy should be simultaneously considered when only simple RGB images are available for estimating maize AGB. This study provides some suggestions on how to make full use of the low-cost and high-resolution UAV stereo images in precision agro-ecological applications and management.     

A new synergistic approach for Sentinel-1 and PALSAR-2 in a machine learning framework to predict aboveground biomass of a dense mangrove forest

Mangroves are well-recognized for their very high carbon sequestration potential. However, studies on their role in global carbon cycling and climate change are hindered due to lack of operational methodologies to model and map their biophysical properties. This study establishes a robust methodological protocol for aboveground biomass (AGB) estimation using i) field measurements, ii) a generic (in the absence of species-specific) allometric equation, iii) multi-sensor/polarization SAR data and derived variables thereof, and iv) machine learning models; that demonstrated high prediction accuracy (R² = 0.93) and low uncertainty (mean ≤ 3% and median ≤ 1.5%) for a mangrove forest. Following stratified random sampling and on-field accessibility criteria, we laid out 314 elementary sampling points of 0.04 ha each at Bhitarkanika wildlife sanctuary (BWS), India and measured circumference at breast height (CBH) and canopy tree height for 18 species. The estimated AGB range of a generic allometric equation was 9–474 Mg/ha for BWS, with a major representation of 9–347 Mg/ha. We utilized Sentinel-1 and ALOS-2/PALSAR-2 and derived their variables for AGB prediction. Compared to single sensor-based model, we observed higher prediction accuracy for combined sensor data (R² = 0.63, 0.87, 0.93; RMSE = 66.75, 39.95, 28.35 Mg/ha; MAE = 52.63, 24.21, 19.55 Mg/ha; and Bias = 3.42, 0.22, 2.15 Mg/ha for C, L and C & L bands respectively using a Generalized Additive Model (GAM) over Random Forest (RF), Gradient Boosting Machines (GBM) and Support Vector Regression (SVR). The higher uncertainty pixels represented seasonal grassland and scrubs in the swampy areas and along the fringes of the creeks that experience diurnal tidal fluctuations. This robust methodology can be replicated for AGB estimates in other mangrove ecosystems to meet the operational carbon accounting requirements of various entities.     

Landscape‐scale lidar analysis of aboveground biomass distribution in secondary Brazilian Atlantic Forest

Secondary forests account for more than half of tropical forests and represent a growing carbon sink, but rates of biomass accumulation vary by a factor of two or more even among plots in the same landscape. To better understand the drivers of this variability, we used airborne lidar to measure forest canopy height and estimate biomass over 4529 ha at Serra do Conduru Park in Southern Bahia, Brazil. We measured trees in 30 georeferenced field plots (0.25‐ha each) to estimate biomass using allometry. Then we estimated aboveground biomass density (ABD) across the lidar study area using a statistical model developed from our field plots. This model related the 95th percentile of the distribution of lidar return heights to ABD. We overlaid this map of ABD on a Landsat‐derived forest age map to determine rates of biomass accumulation. We found rapid initial biomass regeneration (~6 Mg/ha yr), which slowed as forests aged. We also observed high variability in both height and biomass across the landscape within forests of similar age. Nevertheless, a regression model that accounted for spatial autocorrelation and included forest age, slope, and distance to roads or open areas explained 62 and 77 percent of the landscape variation in ABD and canopy height, respectively. Thus, while there is high spatial heterogeneity in forest recovery, and the drivers of this heterogeneity warrant further investigation, we suggest that a relatively simple set of predictor variables is sufficient to explain the majority of variance in both height and ABD in this landscape.     

油菜光合器官面积与导数光谱特征的相关关系

 运用导数光谱分析技术,研究了不同氮肥水平下不同品种油菜(Brassica napus)的 叶面积指数（Leaf area index, LAI）及角果皮面积指数 （Pod area index,PAI ）与冠层导数光谱及其衍生参数的定量关系。结果表明,油菜导数光谱与花前LAI和花后PAI均有良好的相关关系,在 750 nm附近相关关系最好,相关系数达到0.9左右。三边参数与油菜LAI和PAI的相关性顺序为：红边＞黄边＞蓝边,面积参数＞振幅参数＞位置 参数。油菜红边导数光谱的双峰现象降低了红边位置对油菜LAI和PAI的敏感程度,利用线性外推法拟合红边位置能 提高其对油菜LAI和PAI的敏 感程度。在三边参数及其衍生参数中,红边面积及其与蓝边面积的差与LAI及PAI的相关性最好,且适用于该研究中使用的不同品种。因此,750 nm 处的一阶导数光谱、红边面积及其与蓝边面积的差可用于有效地监测油菜的光合器官面积。     

Chronosequence predictions are robust in a Neotropical secondary forest,but plots miss the mark

Tropical secondary forests (TSF) are a global carbon sink of 1.6 Pg C/year. However, TSF carbon uptake is estimated using chronosequence studies that assume differently aged forests can be used to predict change in aboveground biomass density (AGBD) over time. We tested this assumption using two airborne lidar datasets separated by 11.5 years over a Neotropical landscape. Using data from 1998, we predicted canopy height and AGBD within 1.1 and 10.3% of observations in 2009, with higher accuracy for forest height than AGBD and for older TSFs in comparison to younger ones. This result indicates that the space‐for‐time assumption is robust at the landscape‐scale. However, since lidar measurements of secondary tropical forest are rare, we used the 1998 lidar dataset to test how well plot‐based studies quantify the mean TSF height and biomass in a landscape. We found that the sample area required to produce estimates of height or AGBD close to the landscape mean is larger than the typical area sampled in secondary forest chronosequence studies. For example, estimating AGBD within 10% of the landscape mean requires more than thirty 0.1 ha plots per age class, and more total area for larger plots. We conclude that under‐sampling in ground‐based studies may introduce error into estimations of the TSF carbon sink, and that this error can be reduced by more extensive use of lidar measurements.     

Response of an old‐growth tropical rainforest to transient high temperature and drought

Tropical rainforests have experienced episodes of severe heat and drought in recent decades, and climate models project a warmer and potentially drier tropical climate over this century. However, likely responses of tropical rainforests are poorly understood due to a lack of frequent long‐term measurements of forest structure and dynamics. We analyzed a 12‐year record (1999–2010) of 47 817 annual measurements of canopy height to characterize the response of an old‐growth Neotropical rainforest to the severe heat and drought associated with the 1997–1998 El Niño. Well‐drained soils on slopes and plateaus experienced a threefold increase in the fraction of the landscape in gaps (≤2 m) and a reduction in the fraction in high canopy (>15 m) causing distributions of canopy height to depart from equilibrium for a period of 2–3 years. In contrast, forests on low‐lying alluvial terraces remained in equilibrium and were nearly half as likely to experience upper canopy (>15 m) disturbance over the 12 years of observation. Variation in forest response across topographic positions suggests that tropical rainforests are more sensitive to moisture deficits than high temperature and that topography likely structures landscape‐level variation in the severity of drought impacts.     

Mapping and characterization of mangrove plant communities in Hong Kong

Ecological surveys were carried out to investigate the distribution and characterization of remaining mangrove stands in Hong Kong. The field studies indicate that 43 mangrove stands, excluding Mai Po Nature Reserve, still remained along the coastline of Hong Kong despite tremendous reclamation and development which occurred in the past 40 years. Most mangrove stands were found in Deep Bay (western part)and Sai Kung District (eastern coasts). The total areas occupied by these mangrove stands were 178 ha,varying from a very small stand (with 1–2 mangrove shrubs) to fairly extensive mangroves in Deep Bay (> 10 ha). It appeared that mangrove stands located in Deep Bay area were larger than those in the eastern coasts. Twenty plant species were identified from these stands, with 13 being exclusive or associate mangrove species. The major constituent species were Kandelia candel, Aegiceras corniculatum, Excoecaria agallocha and Avicennia marina. Rare species such as Heritiera littoralis were only found in a few mangrove stands. Out of the 43remaining mangrove stands, 23 were more worthwhile for conservation and their plant community structures were further investigated by transect and quadrat analyses. The importance values (sum of relative abundance,frequency and dominance) show that K. candel was the most dominant species. Species richness and Simpson's indices together with tree height, tree density and canopy area fluctuated significantly between mangrove stands. These values were used to prioritize the conservation potential of the remaining mangrove stands in Hong Kong. This revised version was published online in August 2006 with corrections to the Cover Date.     

Correction of phenology-induced effects in forest canopy height models based on airborne laser scanning data. Insights from the deciduous mountain forests in Picos de Europa National Park in Spain

The comparison of ALS time-series is an important element of modern landscape conservation planning, especially to monitor forest ecosystems. Modellers must evaluate phenology when comparing ALS-based maps of ground elevation or canopy height between years. We showcase the scenario using a National Park in the North-West of Spain where bi-temporal ALS has been used to map deciduous mountain forests. We compare the 2010 and 2021 surveys using the same algorithms to interpret ALS data and to generate actionable products for managers, mainly digital terrain models and canopy height models. We implemented a hybrid approach to correct the discrepancies between the surveys, showing the problems arising from differences in phenology or the selection of the scaling. We quantified around 5% of the area suffered from >1 m difference in DTM and higher impact on CHM values. With the hybrid method, modellers can highly reduce the uncertainty when comparing two ALS surveys and derivable products. We provided a solid graphical and analytical diagnosis of these emerging problems in the context of multi-temporal ALS surveys testing the hybrid approach at two resolutions: 1 and 2 m, fine-grained scales. The assessment of phenology-induced effects is important under the context of nationwide ALS survey programmes currently in operation and on high-demand. Finally, we discuss and frame the hybrid-approach as a well-suited vector of canopy gap detection methods to support conservation planning and enforce species-specific habitat improvements.     

Mangrove forests in a peri-urban setting: the case of Mombasa (Kenya)

The structure and regeneration patterns of the peri-urban mangrove vegetation of Mombasa at Tudor creek were studied along belt transects at two forest sites of Kombeni and Tsalu. Based on the species importance values, the dominant mangrove species were Rhizophora mucronata Lam. (Rhizophoraceae) and Avicennia marina (Forssk.) Vierh. (Avicenniaceae). Lumnitzera racemosa Willd., reported in an earlier floristic survey, was not encountered. Tree density varied from 1,264 trees ha^–1 at Kombeni to 1,301 trees ha^–1 at Tsalu and mean tree height was higher at the former site compared to the latter. The size-class structure at both localities showed the numerical dominance of small trees over larger trees. The spatial distribution pattern of adults and juveniles varied greatly between sites and showed a close to uniform pattern (Morisita’s Index I _δ ≪ 1) for adult trees, but a tendency to clustered distribution (I _δ ≫ 1) for juveniles. The present paper shows that unmanaged but exploited peri-urban mangroves are structurally stressed, having enlarged canopy gaps that are characterised by spatial and temporal site heterogeneity that influences regeneration, implying longer periods for canopy closure. Diversifying uses of mangrove products and establishing reserves as no cut zones with regulated harvesting will minimise canopy gap sizes, and promote conservation practices. The proposed management strategy shall boost the ecosystem resilience to both anthropogenic and natural stressors expected in the peri-urban setting in the long run.     

Canopy structure in a temperate old-growth evergreen forest analyzed by using aerial photographs

We used aerial photographs to create a digital elevation model of the canopy surface of a 10-ha study area in a temperate old-growth evergreen forest. A topographic map of the ground surface in a 4-ha permanent plot within the study area was also drawn from ground measurements. The difference between the two elevation values (i.e., canopy surface – ground surface) at each point in a 5-m grid was considered to be the canopy height, and a canopy height profile was constructed from these data. The canopy structure in the 4-ha plot that was estimated in this way was compared with that obtained by two ground observation methods, i.e., the canopy (vegetation) height profile method and the canopy coverage census method. Large gaps were adequately detected by the aerial photograph method, but small gaps were less often detected. Gap size distribution obtained by the aerial photograph method was similar to that observed on the ground, and was a function of gap depth. This study indicates that if a detailed topographic map can be made, the canopy height profile derived from aerial photography can be effective in analyzing the canopy structure of evergreen forests, such as tropical rain forests, over large areas.     

Monitoring landscape fragmentation and aboveground biomass estimation in Can Gio Mangrove Biosphere Reserve over the past 20 years

Over the past 20 years, the mangrove landscape of Can Gio Mangrove Biosphere Reserve (MBR) has undergone drastic changes in space and time. However, we know very little about changes in mangrove landscape model characteristics from analysis of different aspects based on landscape fragmentation. In the present study, the temporal and spatial changes of landscape pattern of land use/land cover (LULC) over the past 20 years in Can Gio Mangrove Biosphere Reserve (MBR), southern Vietnam were analyzed based on remote sensing data, with high classification accuracy (overall accuracy >85%, Kappa >0.8). The present study selected representative landscape indexes and built an integrated landscape index to examine the spatial-temporal changes of landscape patterns. Overall, over the past 20 years, the degree of fragmentation has gradually increased, mainly occurring in the transition zone of MBR. These changes are intended to reflect the significant temporal variation of the MBR, where the ecosystem is strongly disturbed by the intensity of human activities. We then investigate the effectiveness of principal component analysis (PCA)-based machine learning techniques in estimating the mangrove AGB, and applying landscape indices to assess impacts in Can Gio MBR. It reveals that the ANN model obtained the highest prediction accuracy (R²_train = 0.785), followed by GPR (R²_train = 0.703), and SVM (R²_train = 0.671). As a result of applying the ANN model, the predicted mangrove AGB in 2000 and 2020 in the study site ranged from 6.531 to 368.163 Mg ha⁻¹, and 13.749 to 320.295 Mg ha⁻¹, respectively. These results support the application of the model as a tool to support LULC management and protection in the study site, and to contribute insights into the future mangrove research in other regions of the world.     

Diurnal space use and nocturnal roost‐site selection by male Cerulean Warblers during the breeding season

Detailed information about space use during the breeding season is limited for most Nearctic‐Neotropical migratory species of songbirds because of their small size and often cryptic behaviors. We monitored male Cerulean Warblers (Setophaga cerulea), a species of conservation concern, using radio‐telemetry during the 2006–2008 breeding seasons in northern Alabama to better understand their space use and habitat selection. We estimated diurnal home range and core areas using information theoretic criteria, located nocturnal roost sites, and related day and evening locations to surrounding landscape habitat, including features representative of canopy disturbances. Mean home range size was 6.7 ha (N = 10), and home ranges included an average of at least 2 core areas encompassing 0.7 ha. We located 53 nocturnal roost sites that were an average 159.0 m from the center of the nearest core area. More than one‐third (36.6%) of roost sites were located outside the diurnal home ranges of male Cerulean Warblers; only 13.6% were located in core areas. Males in our study moved much farther than reported in previous studies, with some singing in areas > 300 m from previously used song perches, a behavior suggesting pursuit of extra‐pair copulations. Cerulean Warblers in our study preferentially selected a heavily forested landscape composed of mesic, floodplain bottomlands with little man‐made disturbance. Within their home ranges, diurnal locations of males in core areas were located significantly closer to a creek than locations outside of core areas. Our results suggest that male Cerulean Warblers require much larger areas than previously reported and underscore the importance of a predominately forested landscape in their habitat selection process. Although edge habitats appeared to influence space use by male Cerulean Warblers in our study, the extent to which this is an essential requirement is unclear. Our results and those of previous studies suggest that specific habitat requirements of this species can vary at the local scale throughout its breeding range.     

Early post-hurricane stand development in Fringe mangrove forests of contrasting productivity

We examined the immediate effects of a hurricane (Hurricane Andrew, August 1992) in a coastal landscape in sub-tropical Florida, and then monitored stand recovery in Fringe mangrove sites of different productive capacity for 9 years after the disturbance. Structural impacts of the hurricane were confined almost entirely to forests within 200–300 m of the coast. Mortality and damage were concentrated on canopy individuals. Following the hurricane, rapid canopy recovery and the early onset of competition among Fringe forest stems, as evidenced by relatively high mortality of smaller individuals, magnified the initial dominance of hurricane survivors and early-established seedlings over later cohorts, and limited recruitment to the brief period prior to canopy closure. Changes in the relative abundance of the two dominant mangrove species following disturbance varied strongly along the productivity gradient. The shade-tolerant Rhizophora mangle L. generally became the overwhelming canopy dominant in the competitive environment of the recovering Coastal Fringe forest following hurricane, but the shade-intolerant Laguncularia racemosa (L.) C.F. Gaertn was better represented in less productive Interior Fringe sites, where canopy closure was delayed. Site productivity is an important determinant of the success of mangrove species during post-hurricane stand development, and consequently of the zonation of communities in the coastal landscape.     

Extraction of soybean planting area based on feature fusion technology of multi-source low altitude unmanned aerial vehicle images

Soybean is an important food and oil crop in the world. It is of great significance to statics the planting scale accurately for optimizing the crop planting structure and world food security. The technology of accurately extracting the area of soybean planting areas at the field scale using UAV images combined with deep learning algorithms is important for the application. In this study, firstly, RGB images and multispectral images (RGN) were acquired simultaneously by the quad-rotor UAV DJ-Phantom4 Pro at a flying height of 200 m. And the features were extracted from the RGB and RGN images. Further, the fusion image of RGB + VIs and the fusion image of RGN + VIs were obtained by concatenating the band reflectivity of the original image with the calculated Vegetation Index (VI). Then, the soybean planting area was segmented from the feature fusion images by U-Net. And the accuracy of the two sensors was compared. The results showed that the Kappa coefficients obtained based on RGB image, RGN image, CME(the combination of CIVE, MExG, and ExGR), ODR(the combination of OSAVI, DVI, and RDVI), RGB + CME(the combination of RGB and CME), and RGN + ODR(the combination of RGN and ODR) were 0.8806, 0.9327, 0.8437, 0.9330, 0.9420, and 0.9238, respectively. The Kappa coefficient of the combination of the original image and the vegetation index was higher than the original image, indicating that the vegetation index calculation was beneficial to improving the soybean recognition accuracy of the U-Net model. Among them, the precision of the soybean planting area extracted from RGB + CME was the highest, and the Kappa coefficient was 0.9420. Finally, the soybean recognition accuracy of U-Net was compared with the results of DeepLabv3+, Random Forest, and Support Vector Machine. The accuracy of U-Net was the best. It can be concluded that this research proposed the method that was using U-Net trained the fusion image of the original image and vegetation index feature fusion image obtained by the UAV platform, which can effectively segment soybean planting areas. The conclusion of this work provided important technical support for farm level, family cooperatives, and other business entities to manage finely soybean planting and production at low cost.     

Water availability predicts forest canopy height at the global scale

The tendency of trees to grow taller with increasing water availability is common knowledge. Yet a robust, universal relationship between the spatial distribution of water availability and forest canopy height (H) is lacking. Here, we created a global water availability map by calculating an annual budget as the difference between precipitation (P) and potential evapotranspiration (PET) at a 1‐km spatial resolution, and in turn correlated it with a global H map of the same resolution. Across forested areas over the globe, H_mean increased with P‐PET, roughly: H_mean (m) = 19.3 + 0.077*(P‐PET). Maximum forest canopy height also increased gradually from ~ 5 to ~ 50 m, saturating at ~ 45 m for P‐PET > 500 mm. Forests were far from their maximum height potential in cold, boreal regions and in disturbed areas. The strong association between forest height and P‐PET provides a useful tool when studying future forest dynamics under climate change, and in quantifying anthropogenic forest disturbance.     

Wetland changes and mangrove restoration planning in Shenzhen Bay, Southern China

Mangrove forests and associated gei wai (excavated ponds used for shrimp and fish farming) provide important ecosystem services in Shenzhen Bay. Much of the mangrove and gei wai wetlands, however, have been lost because of intensified human activities in the past 30 years. Using five-phase remote-sensing images, we describe the recent history of the spatial–temporal dynamics for the wetlands in the bay. From 1986 to 2007, mangrove area increased from 1.8 to 4.8 km², while the area of gei wai decreased from 36.6 to 17.2 km². Reclamation of gei wai mainly occurred in western and northern Shenzhen Bay, and changed the tidal water environment. The bay has five typical mangrove communities: Avicennia marina + Kandelia candel + Aegiceras corniculatum, Kandelia candel + Aegiceras corniculatum + Acanthus ilicifolius, Bruguiera gymnorrhiza + Excoecaria agallocha, Aegiceras corniculatum, and Sonneratia apetala + Sonneratia caseolaris. The distribution of these communities and their dominant species in the bay exhibit a spatial pattern and temporal (successional) sequence. We describe a mangrove restoration program based on the mangrove successional sequence and the interaction of mangrove and gei wai in the bay. We have planned six mangrove protection and restoration projects in closed areas, semiclosed areas, and open areas to reconstruct the ecological integrity of the entire Shenzhen Bay.     

1964-2015年海南省八门湾红树林湿地及其周边土地景观动态分析

以海南省文昌市八门港红树林湿地及其周边土地为研究对象,采用6期遥感影像为主要的数据来源,建立起研究区域内1964、1972、1988、2000、2009、2015年50年的景观数据库,利用土地转移矩阵和表征景观破碎化过程的景观指数系统阐述了八门湾红树林湿地及其周边土地的土地利用/覆被变化以及景观破碎化的过程,探讨八门湾红树林湿地及其周边土地土地利用/覆被变化与景观破碎化过程之间的相互关系。结果表明：（1）1964-2015年间,研究区域内建筑用地、养殖水面面积持续增长面积比重分别上升了7.72%、12.55%,耕地、林地、红树林面积所占比重分别下降了7.01%、9.16%、9.74%。（2）1964-2015年间,研究区域内斑块数量增加了685个,平均斑块面积缩减了39.12%,聚合度下降了3.5%,最大斑块面积缩减了28.38%,蔓延度下降了9.26%,斑块平均形状破碎化指数和面积加权平均形状破碎化指数分别上升了0.0148、0.0207,斑块密度从1964年的0.0653个/hm²上升到2015年的0.1073个/hm²。（3）八门湾红树林湿地及其周边土地的土地利用/覆被变化与景观破碎化过程的相关关系主要体现在养殖水面、建筑用地面积变化对研究区域景观破碎化指数的影响上。养殖水面、建筑用地面积变化对研究区域景观破碎化过程的影响主要体现在聚合度、斑块数量、蔓延度的作用上。其中,养殖水面面积变化对研究区域景观破碎化过程的影响主要体现在其对红树林面积的侵占,使得红树林面积占研究区域总面积的比例由15%下降到5.25%,红树林由大面积连续集中分布趋向于小面积孤立分散分布。建筑用地面积变化对研究区域景观破碎化过程的影响主要体现在城市的快速发展、交通设施大量的建设。50年来,八门湾红树林湿地各地类之间的转化主要表现为红树林面积转化成养殖水面,林地和耕地面积转化为建筑用地,由此可见,人类活动能力的增强以及影响范围的不断扩大是引发八门湾红树林湿地景观破碎化的主要原因。     

Influence of the landscape matrix on the abundance of arboreal primates in fragmented landscapes

Composition of the landscape matrix of surrounding forest fragments is thought to be critically important to the survival of arboreal primates because it offers structures that help the animals move between fragments and other foraging sites. However, little is known about the composition of the matrix used by these animals. The aim of this study was to quantitatively assess the importance of the landscape matrix and its effects on primate abundance, using black howler monkeys (Alouatta pigra) living in a landscape fragmented by the expansion of agriculture and pastures for livestock in southeastern Mexico. In 2008, a complete census of the monkeys was carried out across the 2000-ha landscape matrix, and for every site where we observed monkeys, we recorded canopy height, tree basal area, food-source abundance, and distance to the nearest fragment. A total of 244 howler monkeys, distributed among 48 groups (including six solitary males) were counted in the matrix. Mean troop size was 5.6 ± 2.8 individuals, and the mode was three individuals. The highest number of troops and greatest howler monkey abundance were recorded in the isolated trees, the eucalyptus plantation, and orchards. A generalized linear model revealed that monkey abundance tended to be higher in matrix elements with higher canopy height, greater food availability, and closest to rainforest fragments. These results suggest that it is necessary to take into account the many elements of the landscape when drawing up conservation and habitat management plans, particularly in order to establish connectivity among the fragments and elements of the matrix with native trees.