27Groundwater is the most economic natural source of drinking in urban and rural areas which 28 are degraded due to high population growth and increased industrial development. We applied 29 a GIS-based DRASTIC model in a populated urban area of Pakistan (Peshawar) to assess 30 groundwater vulnerability to pollution. Six input parametersdepth to phreatic/groundwater 31 level, groundwater recharge, aquifer material, soil type, slope and hydraulic conductivity - 32were used in the model to generate the groundwater vulnerable zones. Each parameter was 33 divided into different ranges or media types, and ratings = 1-10 were assigned to each factor 34 where 1 represented the very low impact on pollution potential and 10 represented very high 35 impact. Weight multipliers = 1-5 were also used to balance and enhance the importance of 36 each factor. The DRASTIC model scores obtained varied from 47 to 147, which were divided 37 into three different zones: low, moderate and high vulnerability to pollution. The final results 38 indicate that about 31.22%, 39.50%, and 29.27% of the total area are under low, moderate, and 39 high vulnerable zones, respectively. Our method presents a very simple and robust way to 40 assess groundwater vulnerability to pollution and helps the decision makers to select 41 appropriate landfill sites for waste disposals, and manage groundwater pollution problems 42 efficiently. 43 44

Groundwater is an important source of water for drinking, agriculture, and other household purposes, but high population growth, industrialization, and lack of oversight on environmental policies and implementation have not only degraded the quality but also stressed the quantity of this precious source of water. Many options existed, but this study evaluated, classified, and mapped the quality of groundwater used for potable consumption with a simple approach in an urban area (Peshawar valley) of Pakistan. More than 100 groundwater samples were collected and analyzed for physio-chemical parameters in a laboratory. Hierarchal clustering analysis (HCA) and classification and regression tree (CART) analysis were sequentially applied to produce potential clusters/groups (groundwater quality classes), extract the threshold values of the clusters, classify and map the groundwater quality data into meaningful classes, and identify the most critical parameters in the classification. The HCA produced six distinct potential clusters. We found a high correlation of electrical conductivity with t o t a l   h a r d n e s s ( R 2 =   0.72 ), a l k a l i n i t y ( R 2 =   0.59 ) and c h l o r i d e   ( R 2 =   0.64 ) , and, t o t a l   h a r d n e s s with c h l o r i d e ( R 2 = 0.62), and a l k a l i n i t y ( R 2 = 0.51). The CART analysis conclusively identified the threshold values of the six classes and showed that t o t a l   h a r d n e s s was the most critical parameter in the classification. The majority of the groundwater was either with worse quality or good quality, and only a few areas had the worst groundwater quality. This study presents a simple tool for the classification of groundwater quality based on several aesthetic constituents and can assist decision makers develop and support policies and/or regulations to manage groundwater resources.

The aim of this paper was to examine the potential of Ikonos satellite images for estimating boreal forest stand characteristics using frequency distributions of radiometric values. The spectral features selected for use in the estimation were medians, standard deviations, and the parameters of the two-parametric Weibull distribution derived from the standwise spectral histograms of the Ikonos image. Ancillary map information, such as land-use and peatland classes, was also included. The method of estimation was nonparametric k-most similar neighbors (K-MSN) method. The most accurate results were achieved using spectral features that were derived from the multispectral images. The lowest RMSEs for the mean total stem volume, basal area, and mean height were 52.2 m 3 /ha (31.3 percent), 5.6 m 2 /ha (25.3 percent), and 3.1 m (20.6 percent), respectively. When only the panchromatic image was used in the analysis, the RMSEs for the mean total stem volume and basal area were about 3 percentage points higher. No differences in the mean height estimates were observed between the multispectral and panchromatic images. The most efficient predictor variables were the medians and the scale parameters of the Weibull distribution. The use of classified map information did not improve the results. The findings suggest that Ikonos satellite images can be used in to estimate forest stand characteristics giving an accuracy that corresponds to that achieved with aerial photographs.

Photogrammetric point clouds obtained with unmanned aircraft systems (UAS) have emerged as an alternative source of remotely sensed data for small area forest management inventories (FMI). Nonetheless, it is often overlooked that small area FMI require considerable field data in addition to UAS data, to support the modelling of forest attributes. In this study, we propose a method whereby tree volumes by species are predicted with photogrammetric UAS data and Sentinel-2 images, using models fitted with airborne laser scanning data. The study area is in a managed boreal forest area in Eastern Finland. First, we predicted total volume with UAS point cloud metrics using a prior regression model fitted in another area with ALS data. Tree species proportions were then predicted by nearest neighbor (-NN) imputation based on bi-seasonal Sentinel-2 images without measuring new field plot data. Species-specific volumes were then obtained by multiplying the total volume by species proportions. The relative root mean square error (RMSE) values for total and species-specific volume predictions at the validation plot level (30 m × 30 m) were 9.0%, and 33.4–62.6%, respectively. Our approach appears promising for species-specific small area FMI in Finland and in comparable forest conditions in which suitable field plots are available.kk

90 %, 95 % ja 100 % (maksimiläpimitta). 5 %:n pisteen läpimittaa ei käytetty, koska useissa metsiköissä 0 %:n ja 10 %:n läpimitat olivat hyvin lähellä toisiaan. Näistä keskiläpimitta oletettiin maastossa mitatuksi, ja muut läpimitat ennustettiin keskiläpimitan ja muiden metsikkötunnusten avulla.Mallien estimoimiseen käytettiin ns. SUR-menetelmää (Seemingly Unrelated Regression), jossa yhtälöryhmän kertoimet estimoidaan ottaen huomioon yhtälöiden virheiden korrelaatiot. Ei-negatiivisten läpimittojen varmistamiseksi mallit tehtiin logaritmimuodossa. Jotta voitiin varmistaa myös monotoninen jakauma (positiiviset erotukset peräkkäisille läpimitoille), malliryhmiin piti sijoittaa ylimääräi-nen malli kuvaamaan 10 %:n läpimitan ja minimilä-pimitan erotusta (koivuille 40 %:n ja 30 %:n). Käy-tetyn SUR menetelmän vuoksi ylimääräinen erotusmalli rajoitti mallien kertoimet siten, että erotukset pysyivät loogisina.Kullekin puulajille estimoitiin kaksi malliryhmää, joista toisessa runkoluku on mukana selittäjänä ja toisessa ei. Runkolukua käytettiin selittäjänä suhteessa puuston pohjapinta-alaan -puhdas runkoluku tuotti usein epäloogisia tuloksia. Muut selittävät muuttujat olivat puuston ikä, pohjapinta-ala sekä kivennäismaavalemuuttuja. Mikäli runkoluku oli malliryhmässä mukana, se selitti läpimittoja jakauman alkupäässä. Ikä selitti kaikissa malliryhmissä läpi-mittoja jakauman loppupäässä. Tärkein selittäjä kaikissa malleissa oli kuitenkin keskiläpimitta.Lopuksi prosenttipisteiden perusteella tuotettujen jakaumien avulla laskettuja puustotunnuksia verrattiin todellisiin. Runkoluvullinen malli osoittautui luotettavammaksi kuin runkoluvuton malli. Esimerkiksi tilavuuden keskivirhe vaihteli runkoluvullisella mallilla 1,54 %:sta 3,24 %:iin, runkoluvuttoman taas 2,29 %:sta 4,31 %:iin. Puulajeista männyn tuloksen saatiin luotettavimmin, koivun epätarkimmin.I MMT Annika Kangas, Metla, Kannuksen tutkimusasema; MMT Matti Maltamo, Metla, Joensuun tutkimusasema. Säh-köposti annika.kangas@metla.fi t u t k i m u s s e l o s t e i t a K uvioittaisen arvioinnin tulosten laskenta perustuu nykyisin puuston läpimittajakauman ennustamiseen malleilla maastossa arvioitujen keski-ja summatunnusten avulla. Tutkimuksessa estimoitiin prosenttipisteisiin perustuvat mallit puuston pohjapinta-alan läpimittajakauman ennustamiseen puulajeittain. Prosenttipisteisiin perustuvassa menetelmässä ennustetaan ensin läpimittoja jakauman kertymäfunktion eri pisteissä. Interpoloimalla estimoitujen pisteiden väliset arvot saadaan muodostettua varsinainen kertymäjakauma. Monotonisen jakauman varmistamiseksi interpoloinnissa käytettiin tavanomaisemman kuutiosplinin asemesta ns. Spät-hin rationaalista spliniä. Suhteellinen pohjapinta-ala halutuissa läpimittaluokissa saadaan luokan loppuja alkupisteen kertymäfunktion arvojen erotuksena. Näistä saadaan metsikön pohjapinta-alalla kertomalla luokkien absoluuttiset pohjapinta-alat.Tutkimusaineistona on erään metsäyhtiön kuvioittaisen arvioinnin tarkistuskoeala-aineisto. Aineisto on mitattu Keski-ja Etelä-Suomesta. Kustaki...