Prediction of tree crown width in natural mixed forests using deep learning algorithm

Crown width(CW)is one of the most important tree metrics,but obtaining CW data is laborious and timeconsuming,particularly in natural forests.The Deep Learning(DL)algorithm has been proposed as an alternative to traditional regression,but its performance in predicting CW in natural mixed forests is unclear.The aims of this study were to develop DL models for predicting tree CW of natural spruce-fir-broadleaf mixed forests in northeastern China,to analyse the contribution of tree size,tree species,site quality,stand structure,and competition to tree CW prediction,and to compare DL models with nonlinear mixed effects(NLME)models for their reliability.An amount of total 10,086 individual trees in 192 subplots were employed in this study.The results indicated that all deep neural network(DNN)models were free of overfitting and statistically stable within 10-fold cross-validation,and the best DNN model could explain 69%of the CW variation with no significant heteroskedasticity.In addition to diameter at breast height,stand structure,tree species,and competition showed significant effects on CW.The NLME model(R^(2)=0.63)outperformed the DNN model(R^(2)=0.54)in predicting CW when the six input variables were consistent,but the results were the opposite when the DNN model(R^(2)=0.69)included all 22 input variables.These results demonstrated the great potential of DL in tree CW prediction.

Dominant woody plant species recognition with a hierarchical model based on multimodal geospatial data for subtropical forests

Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully exploited.To extract dominant woody plant species,GEE combined Sen-tinel-1(S1)and Sentinel-2(S2)data with the addition of the National Forest Resources Inventory(NFRI)and topographic data,resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast China.Spectral and texture features,red-edge bands,and vegetation indices of S1 and S2 data were computed.A hierarchical model obtained information on forest distribution and area and the dominant woody plant species.The results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source independently.Similarly,for dominant woody species recognition,using S1 winter and S2 data across all four seasons was accurate.Including terrain factors and removing spatial correlation from NFRI sample points further improved the recognition accuracy.The optimal forest extraction achieved an overall accuracy(OA)of 97.4%and a maplevel image classification efficacy(MICE)of 96.7%.OA and MICE were 83.6%and 80.7%for dominant species extraction,respectively.The high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant species.Visualizing the results using the GEE application allows for an intuitive display of forest and species distribution,offering significant convenience for forest resource monitoring.

Using machine learning algorithms to estimate stand volume growth of Larix and Quercus forests based on national-scale Forest Inventory data in China

Estimating the volume growth of forest ecosystems accurately is important for understanding carbon sequestration and achieving carbon neutrality goals.However,the key environmental factors affecting volume growth differ across various scales and plant functional types.This study was,therefore,conducted to estimate the volume growth of Larix and Quercus forests based on national-scale forestry inventory data in China and its influencing factors using random forest algorithms.The results showed that the model performances of volume growth in natural forests(R^(2)=0.65 for Larix and 0.66 for Quercus,respectively)were better than those in planted forests(R^(2)=0.44 for Larix and 0.40 for Quercus,respectively).In both natural and planted forests,the stand age showed a strong relative importance for volume growth(8.6%–66.2%),while the edaphic and climatic variables had a limited relative importance(<6.0%).The relationship between stand age and volume growth was unimodal in natural forests and linear increase in planted Quercus forests.And the specific locations(i.e.,altitude and aspect)of sampling plots exhibited high relative importance for volume growth in planted forests(4.1%–18.2%).Altitude positively affected volume growth in planted Larix forests but controlled volume growth negatively in planted Quercus forests.Similarly,the effects of other environmental factors on volume growth also differed in both stand origins(planted versus natural)and plant functional types(Larix versus Quercus).These results highlighted that the stand age was the most important predictor for volume growth and there were diverse effects of environmental factors on volume growth among stand origins and plant functional types.Our findings will provide a good framework for site-specific recommendations regarding the management practices necessary to maintain the volume growth in China's forest ecosystems.

Impacts of climate change on the potential forest productivity based on a climate-driven biophysical model in northeastern China

Climate warming is expected to influence forest growth,composition and distribution.However,accurately estimating and predicting forest biomass,potential productivity or forest growth is still a challenge for forest managers dealing with land-use at the stand to regional levels.In the present study,we predicted the potential productivity(PP)of forest under current and future climate scenarios(RCP2.6,RCP4.5,RCP6.0 and RCP8.5)in Jilin province,northeastern China by using Paterson’s Climate Vegetation and Productivity(CVP)index model.The PP was validated by comparing it with the mean and maximum net primary production calculated from light energy utilization(GLM_PEM).Our results indicated that using the CVP index model is partially valid for predicting the potential forest productivity in northeastern China.PP exhibited obvious spatial heterogeneity varying from 4.6 to 8.9 m3 ha-1 year-1 with an increasing tendency from northwest to southeast driven by the precipitation across the region.The number of vegetation-active months,precipitation and insolation coefficient were identified as the primary factors affecting PP,but no significant relationship was found for warmest temperature or temperature fluctuation.Under future climate scenarios,PP across the Jilin Province is expected to increase from 1.38%(RCP2.6 in 2050)to 15.30%(RCP8.5 in 2070),especially in the eastern Songnen Plain(SE)for the RCP8.5 scenarios.

Cost-effectiveness analysis of subsidy schemes for industrial timber development and carbon sequestration in Japanese forest plantations

This study uses simulations to investigate the effects of implementing two different Japanese forestry subsidy systems on timber production and carbon stock, and examines the consequences for harvesting strategies. An existing Local Yield Table Construction System （LYCS）, a wood conversion algorithm, and a harvesting cost model were used in the simulations to test the applicability of different subsidies to the thinning of stands. Using forest inventory data collected by local government staff, simulation output was used to calculate forestry profits, carbon stocks, subsidies, the amount of labor required, and the cost effectiveness of investing in subsidies. By comparing the output of simulations based on two scenarios, we found that both the clear-cutting area and the amount of harvested timber were larger under Scenario 2, in which the rules governing subsidy allocations are more relaxed, than under Scenario 1, in which the rules are more restrictive. Because the harvested timber under Scenario 1 was mainly produced by clear-cutting, the forestry profits and the subsidy predicted in the early period of the simulation, were larger under Scenario 1 than under Scenario 2. In contrast, the carbon stock was larger under Scenario 2 than under Scenario 1. The simulation model is likely to be useful for improving Plan-Do-Check-Act cyclesimplemented in Japanese forest management systems.

Nature Conservation versus Scenic Quality:A GIS Approach towards Optimized Tourist Tracks in a Protected Area of Northwest Yunnan,China

Development of appropriate tourism infrastructure is important for protected areas that allow public access for tourism use.This is meant to avoid or minimize unfavourable impacts on natural resources through guiding tourists for proper use.In this paper,a GIS-based method,the least-cost path(LCP) modelling,is explored for planning tourist tracks in a World Heritage site in Northwest Yunnan(China),where tourism is increasing rapidly while appropriate infrastructure is almost absent.The modelling process contains three steps:1) selection of evaluation criteria(physical,biological and landscape scenic) that are relevant to track decision; 2) translation of evluation criteria into spatially explicit cost surfaces with GIS,and 3) use of Dijkstra's algorithm to determine the least-cost tracks.Four tracks that link main entrances and scenic spots of the study area are proposed after optimizing all evaluation criteria.These tracks feature lowenvironmental impacts and high landscape qualities,which represent a reasonable solution to balance tourist use and nature conservation in the study area.In addtion,the study proves that the LCP modelling can not only offer a structured framwork for track planning but also allow for different stakeholders to participate in the planning process.It therefore enhances the effectivenss of tourism planning and managemnt in protected areas.

Mapping the potential distribution suitability of 16 tree species under climate change in northeastern China using Maxent modelling

Knowledge on the potential suitability of tree species to the site is very important for forest management planning.Natural forest distribution provides a good reference for afforestation and forest restoration.In this study,we developed species distribution model(SDM)for 16 major tree species with 2,825 permanent sample plots with natural origin from Chinese National Forest Inventory data collected in Jilin Province using the Maxent model.Three types of environmental factors including bioclimate,soil and topography with a total of 33 variables were tested as the input.The values of area under the curve(AUC,one of the receiver operating characteristics of the Maxent model)in the training and test datasets were between 0.784 and 0.968,indicating that the prediction results were quite reliable.The environmental factors affecting the distribution of species were ranked in terms of their importance to the species distribution.Generally,the climatic factors had the greatest contribution,which included mean diurnal range,annual mean temperature,temperature annual range,and iosthermality.But the main environmental factors varied with tree species.Distribution suitability maps under current(1950-2000)and future climate scenarios(CCSM4-RCP 2.6 and RCP 6.0 during 2050)were produced for 16 major tree species in Jilin Province using the model developed.The predicted current and future ranges of habitat suitability of the 16 tree species are likely to be positively and negatively affected by future climate.Seven tree species were found to benefit from future climate including B etula costata,Fraxinus mandshurica,Juglans mandshurica,Phellodendron amurense,Populus ussuriensis,Quercus mongolica and Ulmus pumila;five tree species will experience decline in their suitable habitat including B.platyphylla,Tilia mongolica,Picea asperata,Pinus sylvestris,Pinus koraiensis;and four(Salix koreensis,Abies fabri,Pinus densiflora and Larix olgensis)showed the inconsistency under RCP 2.6 and RCP 6.0 scenarios.The maps of the habitat suitability can be used as a basis for afforestation and forest restoration in northeastern China.The SDMs could be a potential tool for forest management planning.

Variability in the climate-radial growth correlation of Pinus massoniana of different diameter classes

The physiological characteristics of trees change with age,suggesting that growth-related climate signals vary over time.This study aimed to clarify the impacts of different diameter classes on the chronological characteristics of Pinus massoniana Lamb.and its response to climatic factors.Chronologies of P inus massoniana were established in small diameter(14.1 cm),middle diameter(27.3 cm),and large diameter(34.6 cm)trees according to dendrochronology.The results show that:(1)radial growth of different diameter classes had varied characteristics and climate sensitivities;(2)radial growth of small diameter trees was affected by climatic factors of the previous and the current year,while large diameter trees were mainly affected by climatic factors of the current year;and(3)precipitation and temperature were key factors that restricted the radial growth of small and large diameter trees,respectively.

Generalized or general mixed-effect modelling of tree morality of Larix gmelinii subsp.principis-rupprechtii in Northern China

Tree mortality models play an important role in predicting tree growth and yield,but existing mortality models for Larix gmelinii subsp.principis-rupprechtii,an important species used for regeneration and afforestation in northern China,have overlooked potential regional influences on tree mortality.This study used data acquired from 102 temporary sample plots(TSPs)in natural stands of Prince Rupprecht larch in the state-owned Guandi Mountain Forest(n=67)and state-owned Boqiang Forest(n=35)in northern China.To model stand-level tree mortality,we compared seven model forms of county data.Three continuous(dominant height,plot mean diameter,and basal area per hectare)and one dummy variable with two levels(region)were used as fixed effects variables.Tree morality variations caused by forest blocks were accounted for using forest blocks as a random effect in selected models.Results showed that tree mortality significantly positively correlated with stand basal area and dominant height,but negatively correlated with stand mean diameter.Incorporating both the dummy variables and random effects into the tree mortality models significantly increased the fitting improvements,and Hurdle Poisson mixed-effects model showed the most attractive fit statistics(largest R^(2)and smallest RMSE)when employing leave-one-out cross-validation.These mixed-effects dummy variable models will be useful for accurately predicting Larix tree mortality in different regions.

Individual tree segmentation and biomass estimation based on UAV Digital aerial photograph

Digital aerial photograph(DAP)data is processed based on Structure from Motion(Sf M)algorithm and regional net adjustment method to generate digital surface discrete point clouds similar to Light Detection and Ranging(LiDAR)and digital orthophoto mosaic(DOM)similar to optical remote sensing image.In this study,we obtained highresolution images of mature forests of Chinese fir by unmanned aerial vehicle(UAV)flying through crossroute flight,and then reconstructed the threedimensional point clouds in the UAV aerial area by SfM technique.The point cloud segmentation(PCS)algorithm was used for the individual tree segmentation,and the F-score of the three sample plots were 0.91,0.94,and 0.94,respectively.Individual tree biomass modeling was conducted using 155 mature Chinese fir forests which were correctly segmented.The relative root mean squared error(rRMSE)values of random forest(RF),bagged tree(BT)and support vector regression(SVR)were 34.48%,35.74%and 40.93%,respectively.Our study demonstrated that DAP point clouds had great potential to extract forest vertical parameters and could be applied successfully in individual tree segmentation and individual tree biomass modeling.

Peak extraction and classification from digital elevation models based on the relationship between morphological characteristics and spatial position

A peak is an important topographic feature crucial in quantitative geomorphic feature analysis,digital geomorphological mapping,and other fields.Most peak extraction methods are based on the maximum elevation in a local area but ignore the morphological characteristics of the peak area.This paper proposes three indices based on the morphological characteristics of peaks and their spatial relationship with ridge lines:convexity mean index(CM-index),convexity standard deviation(CSD-index),and convexity imbalance index(CIBindex).We develop computation methods to extract peaks from digital elevation model(DEM).Subsequently,the initial peaks extracted by neighborhood statistics are classified using the proposed indices.The method is evaluated in the Qinghai Tibet Plateau and the Loess Plateau in China.An ASTER Global DEM(ASTGTM2 DEM)with a grid size of 30 m is chosen to assess the suitability of the proposed mountain peak extraction and classification method in different geomorphic regions.DEM data with grid sizes of 30 m and 5 m are used for the Loess Plateau.The mountain peak extraction and classification results obtained from the different resolution DEM are compared.The experimental results show that:(1)The CM-index and the CSDindex accurately reflect the concave or convex morphology of the surface and can be used as supplements to existing surface morphological indices.(2)The three indices can identify pseudo mountain peaks and classify the remaining peaks into single ridge peak(SR-Peak)and multiple ridge intersection peak(MRI-Peak).The visual inspection results show that the classification accuracy in the different study areas exceeds 75%.(3)The number of peaks is significantly higher for the 5 m DEM than for the 30 m DEM because more peaks can be detected at a finer resolution.

Object-Based Burned Area Mapping with Extreme Gradient Boosting Using Sentinel-2 Imagery

The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. This paper proposes an automated methodology for mapping burn scars using pairs of Sentinel-2 imagery, exploiting the state-of-the-art eXtreme Gradient Boosting (XGB) machine learning framework. A large database of 64 reference wildfire perimeters in Greece from 2016 to 2019 is used to train the classifier. An empirical methodology for appropriately sampling the training patterns from this database is formulated, which guarantees the effectiveness of the approach and its computational efficiency. A difference (pre-fire minus post-fire) spectral index is used for this purpose, upon which we appropriately identify the clear and fuzzy value ranges. To reduce the data volume, a super-pixel segmentation of the images is also employed, implemented via the QuickShift algorithm. The cross-validation results showcase the effectiveness of the proposed algorithm, with the average commission and omission errors being 9% and 2%, respectively, and the average Matthews correlation coefficient (MCC) equal to 0.93.

Automated Burned Scar Mapping Using Sentinel-2 Imagery

The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. However, the high detail and volume of information provided actually encumbers the automation of the mapping process, at least for the level of automation required to map systematically wildfires on a national level. This paper proposes a fully automated methodology for mapping burn scars using Sentinel-2 data. Information extracted from a pair of Sentinel-2 images, one pre-fire and one post-fire, is jointly used to automatically label a set of training patterns via two empirical rules. An initial pixel-based classification is derived using this training set by means of a Support Vector Machine (SVM) classifier. The latter is subsequently smoothed following a multiple spectral-spatial classification (MSSC) approach, which increases the mapping accuracy and thematic consistency of the final burned area delineation. The proposed methodology was tested on six recent wildfire events in Greece, selected to cover representative cases of the Greek ecosystems and to present challenges in burned area mapping. The lowest classification accuracy achieved was 92%, whereas Matthews correlation coefficient (MCC) was greater or equal to 0.85.

Database and primer selections affect nematode community composition under different vegetations of Changbai Mountain

High-throughput sequencing technology is increasingly used in the study of nematode biodiversity.However,the annotation difference of commonly used primers and reference databases on nematode community is still unclear.We compared two pairs of primers(3NDf/C_1132rmod,NF1F/18Sr2bR)and three databases(NT_V20200604,SILVA138/18s Eukaryota and PR2_v4.5 databases)on the determination of nematode community from four different vegetation types in Changbai Mountain,including mixed broadleaf-conifer forest,dark coniferous forest,betula ermanii Cham and alpine tundra.Our results showed that the selection of different primers and databases influenced the annotation of nematode taxa,but the diversity of nematode community showed consistent pattern among different vegetation types.Our findings emphasize that it is necessary to select appropriate primer and database according to the target taxonomic level.The difference in primers will affect the result of nematode taxa at different classification levels,so sequencing analysis cannot be used for comparison with studies using different primers.In terms of annotation effect in this study,3NDf/C_1132rmod primers with NT_v20200604 database could provide more information than other combinations at the genus or species levels.

Effects of altitude and slope on the climate–radial growth relationships of Larix olgensis A.Henry in the southern Lesser Khingan Mountains,Northeast China

Background:The relationship between climate and radial growth of trees exhibits spatial variation due to environ-mental changes.Therefore,elucidation of how the growth–climate responses of trees vary in space is essential for understanding forest growth dynamics to facilitate scientific management with the ongoing global climate warming.To explore the altitudinal and slope variations of these interactions,tree-ring width chronologies of Larix olgensis A.Henry were analyzed in the southern Lesser Khingan Mountains,Northeast China.Results:The radial growth of L.olgensis exhibited significant 5-to 10-year periodic changes at three altitudes and two slopes,and the frequency change occurred mainly during the early growth stage and after 2000.The radial growth of L.olgensis was significantly negatively correlated with September precipitation only at low altitudes,but also with the mean temperature in July–August and the mean maximum temperature in June–August at high altitudes.The radial growth of L.olgensis at low and middle altitudes as well as on the sunny slope led to a higher demand for moisture,while temperature was the key limiting factor at high altitudes and on the shady slope.Conclusions:The climate–radial growth relationship of L.olgensis exhibits altitudinal and slope variability.This study quantitatively describes the spatially varying growth–climate responses of L.olgensis in the southern Lesser Khingan Mountains,which provides basic data for the management of L.olgensis forests and the prediction of future climate impacts on forest ecosystems.

Comparing independent climate-sensitive models of aboveground biomass and diame ter grow th with their compatible simultaneous model system for three larch species in China

Accurate estimate of tree biomass is essential for forest management.In recent years,several climate-sensitive allometric biomass models with diameter at breast height(D)as a predictor have been proposed for various tree species and climate zones to estimate tree aboveground biomass(AGB).But the allometric models only account for the potential effects of climate on tree biomass and do not simultaneously explain the influence of climate on D growth.In this study,based on the AGB data from 256 destructively sampled trees of three larch species randomly distributed across the five secondary climate zones in northeastern and northern China,we first developed a climate-sensitive AGB base model and a climate-sensitive D growth base model using a nonlinear least square regression separately.A compatible simultaneous model system was then developed with the climate-sensitive AGB and D growth models using a nonlinear seemingly unrelated regression.The potential effects of several temperature and precipitation variables on AGB and D growth were evaluated.The fitting results of climatic sensitive base models were compared against those of their compatible simultaneous model system.It was found that a decreased isothermality([mean of monthly(maximum temperatureminimum temperature)]/(Maximum temperature of the warmest month-Minimum temperature of the coldest month))and total growing season precipitation,and increased annual precipitation significantly increased the values of AGB;an increase of temperature seasonality(a standard deviation of the mean monthly temperature)and precipitation seasonality(a standard deviation of the mean monthly precipitation)could lead to the increase of D.The differences of the model fitting results between the compatible simultaneous system with the consideration of climate effects on both AGB and D growth and its corresponding climate-sensitive AGB and D growth base models were very small and insignificant(p>0.05).Compared to the base models,the inhere nt correlation of AGB with D was taken into account effectively by the proposed compatible model system developed with the climate-sensitive AGB and D grow th models.In addition,the compatible properties of the estimated AGB and D were also addressed substantially in the proposed model system.

A commentary of “Using satellites to map trees”: 10 remarkable discoveries from 2020 in Nature

Brandt et al.reported the results of their analysis from high-resolution satellite images,covering more than 1.3 million square kilometers of the Western Sahara and Sahel in West Africa.They mapped the locations and sizes of approximately 1.8 billion trees.Prior to this,scientists had never made such a detailed map of trees in such a large area.Commercial satellites have begun to collect data and can detect small ground objects that are 1 square meter or less in size.Therefore,the field of terrestrial remote sensing may have a significant advance from mainly a comprehensive landscape-scale measurement to mapping the position and canopy size of each tree at a regional or even global scale.This progress will revolutionize how we think,monitor,simulate,and manage the global terrestrial ecosystem.

利用人工智能神经网络和DEM数据进行植被变化探测

适时精确的探测土地利用和覆盖变化是研究其起因、过程和效应的基础.本研究利用人工智能神经网络对两景不同时期的影像(2003和2004年)进行一次性变化探测.并且输入层中增加了DEM和坡度两个额外"波段".在选取训练样区的过程中,共选取了82个亚类型,其中36个为植被变化亚类型,46个为无变化植被亚类型.NDVI差值法被用于探测变化和无变化的区域,该方法为获取精确的和足够的植被变化类型训练样区提供有效的参考.研究结果显示利用人工智能神经网络探测变化技术所产生的植被变化图,其精度明显高于分类后比较法所产生的植被变化图精度.此外将DEM和坡度作为额外波段结合两景影像得的8个主成分作为输入层,能够有效的提高人工智能神经网络进行山区植被变化探测精度.同时在选取训练样区的过程中,将各不同的变化和无变化的植被类型根据其不同的光谱特征分成不同的亚类型进行取样,对提高人工智能神经网络进行山区植被变化探测精度有着非常重要的促进作用,此处理可以降低山区影像的地形效应.

Vegetation change detection using artificial neural networks with ancillary data in Xishuangbanna,Yunnan Province,China

地球的及时、精确的变化察觉“ s 表面特征为更好计划，管理和环境研究提供基础。在这个学习 ANN 变化，察觉被用来执行植被变化察觉，并且与分类以后的方法相比。以前分类以后被执行 ANN 分类被用来产出多时间的植被地图。ANN 也被用来在 2003 和 2004 为图象执行一个一个通行证分类。DEM 和斜坡被用作二条额外的隧道。在训练阶段期间，训练数据包括 36 个变化子类和 46 个没有变化子类被分开成 82 个子类。而且 NDVI differencing 方法被用来开发变化面具。当鉴别参考区域能为 ANN 那个生产更精确的变化察觉结果时，结果显示出那把 NDVI differencing 方法与视觉解释相结合通行证变化分类。而且，和 PCA 部件把举起和斜坡用作额外的隧道是有效的，在多山的学习区域执行基于 ANN 的变化察觉。把植被转变类分开成子类基于也是重要的光谱反应模式，特别为多山的地面。这个处理能减少地志的效果并且改进变化察觉精确性。

Monthly soil erosion monitoring based on remotely sensed biophysical parameters: a case study in Strymonas river basin towards a functional pan-European service

Currently,many soil erosion studies at local,regional,national or continental scale use models based on the USLE-family approaches.Applications of these models pay little attention to seasonal changes,despite evidence in the literature which suggests that erosion risk may change rapidly according to intra-annual rainfall figures and vegetation phenology.This paper emphasises the aspect of seasonality in soil erosion mapping by using month-step rainfall erosivity data and biophysical time series data derived from remote-sensing.The latter,together with other existing pan-European geo-databases sets the basis for a functional pan-European service for soil erosion monitoring at a scale of 1:500,000.This potential service has led to the establishment of a new modelling approach(called the G2 model)based on the inheritance of USLE-family models.The G2 model proposes innovative techniques for the estimation of vegetation and protection factors.The model has been applied in a 14,500 km 2 study area in SE Europe covering a major part of the basin of the cross-border river,Strymonas.Model results were verified with erosion and sedimentation figures from previous research.The study confirmed that monthly erosion mapping would identify the critical months and would allow erosion figures to be linked to specific land uses.